diff --git a/_includes/head.html b/_includes/head.html
index 7e554ab6d..05b427ca4 100644
--- a/_includes/head.html
+++ b/_includes/head.html
@@ -14,7 +14,7 @@
{% endif %}
{% endunless %}
-
+
diff --git a/applications/live-chat.md b/applications/live-chat.md
deleted file mode 100644
index 74e2baf70..000000000
--- a/applications/live-chat.md
+++ /dev/null
@@ -1,32 +0,0 @@
----
-nav_order: 8
-parent: Application areas
-title: Live chat
-description: Machine translation for live chat
----
-
-# Live chat
-
-Machine translation for **live chat** is used to translate messages between two users who do not speak a common language.
-
-Live chat messages are a type of [user-generated content](user-generated-content.md).
-Live chat is challenging for machine translation because it is noisy and context-dependent.
-
-Common scenarios are in-game chat in [gaming](gaming.md) and customer support for [commerce and marketplaces](commerce-and-marketplaces.md).
-
-Many commercial chat applications that have incorporated machine translation for live chat:
-
-- Skype
-- Telegram
-- WeChat
-- Line
-
-## Companies
-
-* [Unbabel](/industry/companies.md/#unbabel)
-* [Language I/O](/industry/companies.md/#language-io)
-* [KantanMT](/industry/companies.md/#kantanmt)
-
-## See also
-
-* [Speech translation](/other-input-types/speech.md)
diff --git a/CNAME b/files/CNAME
similarity index 100%
rename from CNAME
rename to files/CNAME
diff --git a/generate.py b/generate.py
index 44508bdab..9dfe9cb49 100644
--- a/generate.py
+++ b/generate.py
@@ -144,7 +144,7 @@ def normalize(code):
name = LANGUAGE_FAMILIES[code]
slug = slugify(name)
- filepath = f'languages/{ slug }.md'
+ filepath = f'nav_bar/languages/{ slug }.md'
# TODO: check that it won't be overwritten by a language
@@ -257,7 +257,7 @@ def normalize(code):
}
slug = slugify(name)
- filepath = f'languages/{ slug }.md'
+ filepath = f'nav_bar/languages/{ slug }.md'
content = read_content(filepath)
@@ -392,7 +392,7 @@ def normalize(code):
content = read_content(filepath)
- filepath = f'apis/{ api_id }.md'
+ filepath = f'nav_bar/apis/{ api_id }.md'
with open(filepath, 'w', encoding='utf8') as f:
f.write(f'''\
---
@@ -497,7 +497,7 @@ def normalize(code):
content = read_content(filepath)
- filepath = f'integrations/{tms_id}.md'
+ filepath = f'nav_bar/integrations/{tms_id}.md'
with open(filepath, 'w', encoding='utf8') as f:
f.write(f'''\
---
@@ -554,7 +554,7 @@ def normalize(code):
content = read_content(filepath)
- filepath = f'aggregators/{a_id}.md'
+ filepath = f'nav_bar/aggregators/{a_id}.md'
with open(filepath, 'w', encoding='utf8') as f:
f.write(f'''\
---
@@ -679,9 +679,8 @@ def normalize(code):
}
}
- slug = slugify(name)
-
- filepath = f'quality-estimation/{ slug }.md'
+ slug = slugify(name.replace('-', '').replace(' ', ' '))
+ filepath = f'nav_bar/quality-estimation/{ slug }.md'
content = read_content(filepath)
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diff --git a/applications/advanced-concepts/bridging.md b/nav_bar/building-and-research/applications/advanced-concepts/bridging.md
similarity index 86%
rename from applications/advanced-concepts/bridging.md
rename to nav_bar/building-and-research/applications/advanced-concepts/bridging.md
index a892b01d9..10140d6bb 100644
--- a/applications/advanced-concepts/bridging.md
+++ b/nav_bar/building-and-research/applications/advanced-concepts/bridging.md
@@ -12,5 +12,5 @@ English is a common choice for the bridge because many languages have training d
## Applications
-- Translation without [parallel data](/customisation/parallel-data.md)
+- Translation without [parallel data](/nav_bar/features/customisation/parallel-data.md)
- Paraphrasing in a single language
diff --git a/applications/advanced-concepts/locale.md b/nav_bar/building-and-research/applications/advanced-concepts/locale.md
similarity index 81%
rename from applications/advanced-concepts/locale.md
rename to nav_bar/building-and-research/applications/advanced-concepts/locale.md
index 9dfa158dc..1a1f4e76a 100644
--- a/applications/advanced-concepts/locale.md
+++ b/nav_bar/building-and-research/applications/advanced-concepts/locale.md
@@ -5,9 +5,9 @@ title: Locale
description: Specification of language variants
---
-A **locale** is an identifier of a [language](/languages/languages.md) and region, plus an optional writing script.
-The locale is used in [machine translation APIs](/apis/apis.md) to specify the language of the source and target text.
-Locales are used to indicate the language of documents in [web crawling](/customisation/crawling.md) to build [training data](/customisation/crawling.md).
+A **locale** is an identifier of a [language](/nav_bar/languages/languages.md) and region, plus an optional writing script.
+The locale is used in [machine translation APIs](/nav_bar/apis/apis.md) to specify the language of the source and target text.
+Locales are used to indicate the language of documents in [web crawling](/nav_bar/features/customisation/crawling.md) to build [training data](/nav_bar/features/customisation/crawling.md).
Example: `frCA` means French (fr) as spoken in Canada (CA)
@@ -53,4 +53,4 @@ These language variations are supported by many API vendors:
## See also
-- [Languages](/languages/languages.md)
+- [Languages](/nav_bar/languages/languages.md)
diff --git a/applications/advanced-concepts/multi-engine-machine-translation.md b/nav_bar/building-and-research/applications/advanced-concepts/multi-engine-machine-translation.md
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diff --git a/applications/advanced-concepts/zero-shot-translation.md b/nav_bar/building-and-research/applications/advanced-concepts/zero-shot-translation.md
similarity index 63%
rename from applications/advanced-concepts/zero-shot-translation.md
rename to nav_bar/building-and-research/applications/advanced-concepts/zero-shot-translation.md
index 5f4bdebc5..b26c179a3 100644
--- a/applications/advanced-concepts/zero-shot-translation.md
+++ b/nav_bar/building-and-research/applications/advanced-concepts/zero-shot-translation.md
@@ -13,11 +13,11 @@ Zero-shot machine translation is an active area of research.
## Approaches
-- Multilingual [neural machine translation](/approaches/neural-machine-translation.md) (MNMT): This approach learns a single model for all language pairs. The target language is an input to the model. This approach can produce translations between languages that both have parallel data, but not necessarily parallel data with each other. As of 2022, [Google Translate uses MNMT](https://ai.googleblog.com/2022/05/24-new-languages-google-translate.html).
+- Multilingual [neural machine translation](/nav_bar/building-and-research/approaches/neural-machine-translation.md) (MNMT): This approach learns a single model for all language pairs. The target language is an input to the model. This approach can produce translations between languages that both have parallel data, but not necessarily parallel data with each other. As of 2022, [Google Translate uses MNMT](https://ai.googleblog.com/2022/05/24-new-languages-google-translate.html).
- Stitching together encoders and decoders
- When training models for many language pairs, it's possible to ensure that the latent representations are similar across languages, then connect the appropriate encoder and decoder for the desired language pair.
- Unsupervised methods learn encoders and decoders as denoising autoencoders, making an effort to ensure that the latent representations are similar across languages, then connect the appropriate encoder and decoder for the desired language pair.
## See also
-- [Bridging](bridging.md): Bridging is also commonly used to translate between languages without parallel data.
+- [Bridging](/nav_bar/building-and-research/applications/advanced-concepts/bridging.md): Bridging is also commonly used to translate between languages without parallel data.
diff --git a/applications/applications.md b/nav_bar/building-and-research/applications/applications.md
similarity index 100%
rename from applications/applications.md
rename to nav_bar/building-and-research/applications/applications.md
diff --git a/applications/automatic-post-editing.md b/nav_bar/building-and-research/applications/automatic-post-editing.md
similarity index 100%
rename from applications/automatic-post-editing.md
rename to nav_bar/building-and-research/applications/automatic-post-editing.md
diff --git a/applications/commerce-and-marketplaces.md b/nav_bar/building-and-research/applications/commerce-and-marketplaces.md
similarity index 79%
rename from applications/commerce-and-marketplaces.md
rename to nav_bar/building-and-research/applications/commerce-and-marketplaces.md
index cd0b0a384..4a9b2bdd7 100644
--- a/applications/commerce-and-marketplaces.md
+++ b/nav_bar/building-and-research/applications/commerce-and-marketplaces.md
@@ -10,7 +10,7 @@ Machine translation for **commerce and marketplaces** is the translation of prod
## Goals
* sales conversion
-* [SEO](seo.md) and site search
+* [SEO](/nav_bar/building-and-research/applications/seo.md) and site search
* customer support
The top merchants and platforms cannot human-translate all products into all languages, because of the scale.
@@ -22,4 +22,4 @@ The translation of product titles and descriptions is handled by the platforms,
* Product titles
* Product descriptions
* Product reviews
-* Customer support [chat](live-chat.md) and email
+* Customer support [chat](/nav_bar/building-and-research/applications/live-chat.md) and email
diff --git a/applications/content-drafting-images/drafting-text-deepl.png b/nav_bar/building-and-research/applications/content-drafting-images/drafting-text-deepl.png
similarity index 100%
rename from applications/content-drafting-images/drafting-text-deepl.png
rename to nav_bar/building-and-research/applications/content-drafting-images/drafting-text-deepl.png
diff --git a/applications/content-drafting-images/image_with_text_eng.svg b/nav_bar/building-and-research/applications/content-drafting-images/image_with_text_eng.svg
similarity index 100%
rename from applications/content-drafting-images/image_with_text_eng.svg
rename to nav_bar/building-and-research/applications/content-drafting-images/image_with_text_eng.svg
diff --git a/applications/content-drafting-images/image_with_text_ger.svg b/nav_bar/building-and-research/applications/content-drafting-images/image_with_text_ger.svg
similarity index 100%
rename from applications/content-drafting-images/image_with_text_ger.svg
rename to nav_bar/building-and-research/applications/content-drafting-images/image_with_text_ger.svg
diff --git a/applications/content-drafting.md b/nav_bar/building-and-research/applications/content-drafting.md
similarity index 82%
rename from applications/content-drafting.md
rename to nav_bar/building-and-research/applications/content-drafting.md
index 2cf10a6cb..4be744aff 100644
--- a/applications/content-drafting.md
+++ b/nav_bar/building-and-research/applications/content-drafting.md
@@ -25,7 +25,7 @@ When applied to localised content, machine translation is used to detect potenti
- truncated words or phrases due to lack of screen space
- unsuitable sort order in target language
-Detecting these issues at an early phase allows taking corrective actions, in parallel with the actual professional translation or [post-editing](/post-editing.md), or planning for necessary post-processing of localised content, for example:
+Detecting these issues at an early phase allows taking corrective actions, in parallel with the actual professional translation or [post-editing](/nav_bar/building-and-research/applications/workflows/post-editing.md), or planning for necessary post-processing of localised content, for example:
- Enable proper locale, character sets and fonts.
- Resize assets for various text lengths, or plan for resizing these assets at the time of translation.
@@ -39,11 +39,11 @@ Detecting these issues at an early phase allows taking corrective actions, in pa
The source image in English relies on the exact length of English text:
-![English image](content-drafting-images/image_with_text_eng.svg)
+![English image](/nav_bar/building-and-research/applications/content-drafting-images/image_with_text_eng.svg)
Localised images will have different text lengths depending on the target language. For example, German text is usually longer and does not fit, so some work is needed after the translation to adjust the localised images:
-![German image](content-drafting-images/image_with_text_ger.svg)
+![German image](/nav_bar/building-and-research/applications/content-drafting-images/image_with_text_ger.svg)
@@ -65,4 +65,4 @@ This technique omits the basic difficulty of post-editing: while the post-editor
An author may use DeepL in the browser to draft an English paragraph from a Polish source.
-![Drafting text in DeepL](content-drafting-images/drafting-text-deepl.png)
+![Drafting text in DeepL](/nav_bar/building-and-research/applications/content-drafting-images/drafting-text-deepl.png)
diff --git a/applications/gaming.md b/nav_bar/building-and-research/applications/gaming.md
similarity index 89%
rename from applications/gaming.md
rename to nav_bar/building-and-research/applications/gaming.md
index f9ebf2747..dd7c6b970 100644
--- a/applications/gaming.md
+++ b/nav_bar/building-and-research/applications/gaming.md
@@ -19,7 +19,7 @@ In-game:
* UI strings
* On-screen text and images
* Subtitles
-* [Live chat](live-chat.md)
+* [Live chat](/nav_bar/building-and-research/applications/live-chat.md)
Out-of-game:
diff --git a/nav_bar/building-and-research/applications/live-chat.md b/nav_bar/building-and-research/applications/live-chat.md
new file mode 100644
index 000000000..8d84bd0ad
--- /dev/null
+++ b/nav_bar/building-and-research/applications/live-chat.md
@@ -0,0 +1,32 @@
+---
+nav_order: 8
+parent: Application areas
+title: Live chat
+description: Machine translation for live chat
+---
+
+# Live chat
+
+Machine translation for **live chat** is used to translate messages between two users who do not speak a common language.
+
+Live chat messages are a type of [user-generated content](/nav_bar/building-and-research/applications/user-generated-content.md).
+Live chat is challenging for machine translation because it is noisy and context-dependent.
+
+Common scenarios are in-game chat in [gaming](/nav_bar/building-and-research/applications/gaming.md) and customer support for [commerce and marketplaces](/nav_bar/building-and-research/applications/commerce-and-marketplaces.md).
+
+Many commercial chat applications that have incorporated machine translation for live chat:
+
+- Skype
+- Telegram
+- WeChat
+- Line
+
+## Companies
+
+* [Unbabel](/nav_bar/more/industry/companies.md/#unbabel)
+* [Language I/O](/nav_bar/more/industry/companies.md/#language-io)
+* [KantanMT](/nav_bar/more/industry/companies.md/#kantanmt)
+
+## See also
+
+* [Speech translation](/nav_bar/building-and-research/other-input-types/speech.md)
diff --git a/applications/multilingual-search.md b/nav_bar/building-and-research/applications/multilingual-search.md
similarity index 75%
rename from applications/multilingual-search.md
rename to nav_bar/building-and-research/applications/multilingual-search.md
index ce59bc238..2c0c7e2d7 100644
--- a/applications/multilingual-search.md
+++ b/nav_bar/building-and-research/applications/multilingual-search.md
@@ -14,6 +14,6 @@ This is a challenge for machine translation since many names and terms can have
# Content types
-- [Social networks](social-networks.md)
+- [Social networks](/nav_bar/building-and-research/applications/social-networks.md)
- Messenger apps
-- [Commerce and marketplaces](commerce-and-marketplaces.md)
+- [Commerce and marketplaces](/nav_bar/building-and-research/applications/commerce-and-marketplaces.md)
diff --git a/applications/seo.md b/nav_bar/building-and-research/applications/seo.md
similarity index 62%
rename from applications/seo.md
rename to nav_bar/building-and-research/applications/seo.md
index d0be1796f..9eb4c0254 100644
--- a/applications/seo.md
+++ b/nav_bar/building-and-research/applications/seo.md
@@ -5,13 +5,13 @@ title: SEO
description: Machine translation for SEO
---
-Machine translation for **search engine optimization** \(**SEO**\) is the translation of [commerce and marketplace](commerce-and-marketplaces.md) website content into the languages in which users are searching.
+Machine translation for **search engine optimization** \(**SEO**\) is the translation of [commerce and marketplace](/nav_bar/building-and-research/applications/commerce-and-marketplaces.md) website content into the languages in which users are searching.
Translations for search engine optimization is challenging for machine translation because the end goal is not just to convey the meaning, but to use the words that the users actually search for in the target language.
Short input, like keywords and tags, and non-sentence input, like lists of keywords, are also a challenge for machine translation.
-Content can be purely machine-translated, [hybrid-translated](/workflows/hybrid-translation.md) or human [post-edited](/workflows/post-editing.md).
+Content can be purely machine-translated, [hybrid-translated](/nav_bar/building-and-research/applications/workflows/hybrid-translation.md) or human [post-edited](/nav_bar/building-and-research/applications/workflows/post-editing.md).
Search engines can penalize machine-generated content, including purely machine-translated content.
### Content types
diff --git a/applications/social-networks.md b/nav_bar/building-and-research/applications/social-networks.md
similarity index 81%
rename from applications/social-networks.md
rename to nav_bar/building-and-research/applications/social-networks.md
index e3a707138..1d8aa40b9 100644
--- a/applications/social-networks.md
+++ b/nav_bar/building-and-research/applications/social-networks.md
@@ -17,7 +17,7 @@ Social networks with a machine translation feature include:
* YouTube
* TikTok
-While the static content of social networks is human-translated, machine translation is used for [user-generated content](user-generated-content.md).
+While the static content of social networks is human-translated, machine translation is used for [user-generated content](/nav_bar/building-and-research/applications/user-generated-content.md).
## Content types
diff --git a/applications/translation-and-localisation.md b/nav_bar/building-and-research/applications/translation-and-localisation.md
similarity index 80%
rename from applications/translation-and-localisation.md
rename to nav_bar/building-and-research/applications/translation-and-localisation.md
index 3a9d40c2f..e6571a847 100644
--- a/applications/translation-and-localisation.md
+++ b/nav_bar/building-and-research/applications/translation-and-localisation.md
@@ -10,12 +10,12 @@ Machine translation is a key technology in professional **translation and locali
### Integrations
-Translation software, like [translation management systems](/integrations) (TMS) and computer-aided translation tools (CAT), integrate [machine translation APIs](/apis), directly or via plugins.
+Translation software, like [translation management systems](/nav_bar/integrations) (TMS) and computer-aided translation tools (CAT), integrate [machine translation APIs](/nav_bar/apis), directly or via plugins.
### Workflow
-The translation software fills in the machine translation for the human translator to [post-edit](/workflows/post-editing.md).
-The machine translation can be inserted in whole files at once or one [segment](/concepts/segment.md) at a time.
+The translation software fills in the machine translation for the human translator to [post-edit](/nav_bar/building-and-research/applications/workflows/post-editing.md).
+The machine translation can be inserted in whole files at once or one [segment](/nav_bar/concepts/segment.md) at a time.
Some systems that translate segment-by-segment can learn from post-edits and adapt the machine translation output accordingly.
### Productivity
@@ -41,7 +41,7 @@ A software localisation process consists of the following steps:
2. Identifying the features that need to be replaced or adapted.
3. Translating user interface and user assistance content.
4. Replacing or adapting the features that can’t be used in the target culture.
-5. Creating versions of the software in the target [locale](/applications/advanced-concepts/locale.md) that are target culture specific.
+5. Creating versions of the software in the target [locale](/nav_bar/building-and-research/applications/advanced-concepts/locale.md) that are target culture specific.
6. Testing the localised versions:
- Verifying the validity of the translation in the context of the software.
- Checking if the new versions work well for the target audience in each language:
diff --git a/applications/user-generated-content.md b/nav_bar/building-and-research/applications/user-generated-content.md
similarity index 61%
rename from applications/user-generated-content.md
rename to nav_bar/building-and-research/applications/user-generated-content.md
index a03fd5963..826e2d574 100644
--- a/applications/user-generated-content.md
+++ b/nav_bar/building-and-research/applications/user-generated-content.md
@@ -16,7 +16,7 @@ User-generated content is challenging for machine translation because it is open
## Content types
-* [Posts and comments](social-networks.md)
-* [Live chat](live-chat.md)
-* [Product titles, descriptions and reviews](commerce-and-marketplaces.md)
-* [In-game content](gaming.md)
+* [Posts and comments](/nav_bar/building-and-research/applications/social-networks.md)
+* [Live chat](/nav_bar/building-and-research/applications/live-chat.md)
+* [Product titles, descriptions and reviews](/nav_bar/building-and-research/applications/commerce-and-marketplaces.md)
+* [In-game content](/nav_bar/building-and-research/applications/gaming.md)
diff --git a/workflows/casmacat_interactive_machine_translation.gif b/nav_bar/building-and-research/applications/workflows/casmacat_interactive_machine_translation.gif
similarity index 100%
rename from workflows/casmacat_interactive_machine_translation.gif
rename to nav_bar/building-and-research/applications/workflows/casmacat_interactive_machine_translation.gif
diff --git a/workflows/hybrid-translation-workflow.png b/nav_bar/building-and-research/applications/workflows/hybrid-translation-workflow.png
similarity index 100%
rename from workflows/hybrid-translation-workflow.png
rename to nav_bar/building-and-research/applications/workflows/hybrid-translation-workflow.png
diff --git a/workflows/hybrid-translation.md b/nav_bar/building-and-research/applications/workflows/hybrid-translation.md
similarity index 50%
rename from workflows/hybrid-translation.md
rename to nav_bar/building-and-research/applications/workflows/hybrid-translation.md
index 87cd3138a..d6c1ff6f3 100644
--- a/workflows/hybrid-translation.md
+++ b/nav_bar/building-and-research/applications/workflows/hybrid-translation.md
@@ -8,16 +8,16 @@ description: Workflow with both human translations and pure machine translations
In a **hybrid translation** workflow, some raw machine translations are never seen or edited by a human translator.
-Hybrid translation can be faster and cheaper than full human [post-editing](post-editing.md).
+Hybrid translation can be faster and cheaper than full human [post-editing](/nav_bar/building-and-research/applications/workflows/post-editing.md).
Hybrid translation requires good machine translation.
A signification portion of the machine translated segments should be usable as-is.
> ### Workflow diagram
-> The hybrid translation workflow was first presented by Microsoft, VMWare and [Unbabel](/industry/companies.md#unbabel).
+> The hybrid translation workflow was first presented by Microsoft, VMWare and [Unbabel](/nav_bar/more/industry/companies.md#unbabel).
>
-> ##### Slide from a [ModelFront](/industry/companies.md#modelfront) presentation
->
+> ##### Slide from a [ModelFront](/nav_bar/more/industry/companies.md#modelfront) presentation
+>
A risk **threshold** is set.
Each new machine translation is automatically classified as high-quality or low-quality.
@@ -31,17 +31,17 @@ They are marked as translated or approved, and potentially even locked.
### Technology
-The key technology for a hybrid translation workflow is translation [**quality prediction**](/quality/quality-estimation.md), which is known as *machine translation quality estimation* in the [research](/building-and-research/building-and-research.md) world.
+The key technology for a hybrid translation workflow is translation [**quality prediction**](/nav_bar/building-and-research/quality/quality-estimation.md), which is known as *machine translation quality estimation* in the [research](/nav_bar/building-and-research/building-and-research.md) world.
### Adoption
-At first, companies like Microsoft, [Unbabel](/industry/companies.md/#unbabel), VMWare and Wayfair implemented hybrid translation by researching and developing their own machine translation quality estimation.
+At first, companies like Microsoft, [Unbabel](/nav_bar/more/industry/companies.md/#unbabel), VMWare and Wayfair implemented hybrid translation by researching and developing their own machine translation quality estimation.
-With the launch of the [ModelFront](/industry/companies.md/#modelfront) translation quality prediction API, more companies started to adopt the hybrid translation workflow within commercially available translation management systems.
+With the launch of the [ModelFront](/nav_bar/more/industry/companies.md/#modelfront) translation quality prediction API, more companies started to adopt the hybrid translation workflow within commercially available translation management systems.
---
### See also
-- [**Quality estimation**](/quality/quality-estimation.md)
+- [**Quality estimation**](/nav_bar/building-and-research/quality/quality-estimation.md)
diff --git a/workflows/interactive-translation-prediction.md b/nav_bar/building-and-research/applications/workflows/interactive-translation-prediction.md
similarity index 75%
rename from workflows/interactive-translation-prediction.md
rename to nav_bar/building-and-research/applications/workflows/interactive-translation-prediction.md
index 9f4ac5691..a228cefaf 100644
--- a/workflows/interactive-translation-prediction.md
+++ b/nav_bar/building-and-research/applications/workflows/interactive-translation-prediction.md
@@ -9,7 +9,7 @@ description: Workflow for human translation with sentence completion
Unlike traditional predictive text, interactive translation prediction uses the machine translation model to provide better completions of the sentence.
> ##### User interface from [Casmacat](https://www.casmacat.eu/)
->
+>
## Challenges
@@ -19,4 +19,4 @@ Unlike traditional predictive text, interactive translation prediction uses the
## See also
-- [A user study of neural interactive translation prediction](https://link.springer.com/article/10.1007/s10590-019-09235-8) (2019) found that interactive translation prediction was more efficient than [post-editing](post-editing.md) with neural machine translation and also highlights user experience challenges.
+- [A user study of neural interactive translation prediction](https://link.springer.com/article/10.1007/s10590-019-09235-8) (2019) found that interactive translation prediction was more efficient than [post-editing](/nav_bar/building-and-research/applications/workflows/post-editing.md) with neural machine translation and also highlights user experience challenges.
diff --git a/workflows/post-editing.md b/nav_bar/building-and-research/applications/workflows/post-editing.md
similarity index 100%
rename from workflows/post-editing.md
rename to nav_bar/building-and-research/applications/workflows/post-editing.md
diff --git a/workflows/workflows.md b/nav_bar/building-and-research/applications/workflows/workflows.md
similarity index 100%
rename from workflows/workflows.md
rename to nav_bar/building-and-research/applications/workflows/workflows.md
diff --git a/approaches/approaches.md b/nav_bar/building-and-research/approaches/approaches.md
similarity index 58%
rename from approaches/approaches.md
rename to nav_bar/building-and-research/approaches/approaches.md
index c8b136490..5b87b7849 100644
--- a/approaches/approaches.md
+++ b/nav_bar/building-and-research/approaches/approaches.md
@@ -8,13 +8,13 @@ seo:
name: Approaches to machine translation
---
-The earliest machine translation systems were built with [*rule-based* approaches](rule-based-machine-translation.md).
+The earliest machine translation systems were built with [*rule-based* approaches](/nav_bar/building-and-research/approaches/rule-based-machine-translation.md).
-By the 2010s, the top systems, like Google Translate, used [*statistical* machine translation](/approaches/statistical-machine-translation.md) (SMT).
+By the 2010s, the top systems, like Google Translate, used [*statistical* machine translation](/nav_bar/building-and-research/approaches/statistical-machine-translation.md) (SMT).
Statistical machine translation is also known as [phrase-based machine translation](https://machinetranslate.org/statistical-machine-translation#approaches) (PBMT).
-By the 2020s, the top systems used [*neural machine* translation](neural-machine-translation.md).
-A few of the neural systems launched an [adaptive customisation](/customisation/adaptive.md) feature.
+By the 2020s, the top systems used [*neural machine* translation](/nav_bar/building-and-research/approaches/neural-machine-translation.md).
+A few of the neural systems launched an [adaptive customisation](/nav_bar/features/customisation/adaptive.md) feature.
Each paradigm shift made machine translation systems more accurate and also easier to build.
-The result was more [language support](/languages/languages.md) and more production systems from more [companies](/industry/companies.md).
+The result was more [language support](/nav_bar/languages/languages.md) and more production systems from more [companies](/nav_bar/more/industry/companies.md).
diff --git a/approaches/byte-pair-encoding.md b/nav_bar/building-and-research/approaches/byte-pair-encoding.md
similarity index 100%
rename from approaches/byte-pair-encoding.md
rename to nav_bar/building-and-research/approaches/byte-pair-encoding.md
diff --git a/approaches/neural-machine-translation.md b/nav_bar/building-and-research/approaches/neural-machine-translation.md
similarity index 76%
rename from approaches/neural-machine-translation.md
rename to nav_bar/building-and-research/approaches/neural-machine-translation.md
index de6d01a2c..ea389ac8e 100644
--- a/approaches/neural-machine-translation.md
+++ b/nav_bar/building-and-research/approaches/neural-machine-translation.md
@@ -7,11 +7,11 @@ description: Deep learning approaches to machine translation
---
**Neural machine translation** (NMT) is a machine translation approach based on machine learning that uses large neural networks to predict the likelihood of correct translations.
-Like [statistical machine translation](statistical-machine-translation.md), neural machine translation is data-driven.
+Like [statistical machine translation](/nav_bar/building-and-research/approaches/statistical-machine-translation.md), neural machine translation is data-driven.
### Neural networks
-Neural networks use [training data](/customisation/training-data.md) to create [vectors](/concepts/vector.md) for every word and its relations, called word embeddings.
+Neural networks use [training data](/nav_bar/features/customisation/training-data.md) to create [vectors](/concepts/vector.md) for every word and its relations, called word embeddings.
Words with similar meaning cluster together, and words with more than one meaning appear simultaneously in different clusters.
**Cluster1**:
@@ -38,7 +38,7 @@ The sequence model arranges a sentence order by calculating the probability of t
### Encoder/decoder framework
-Neural machine translation architecture consists of an encoder and a [decoder](/concepts/language-model.md#decoding).
+Neural machine translation architecture consists of an encoder and a [decoder](/nav_bar/concepts/language-model.md#decoding).
The encoder analyses the input sequence words and their relations.
The result is the representation of the sentence, called context vector.
@@ -57,5 +57,5 @@ The attention mechanism also learns the alignment between the relevant informati
## See also
-* [Byte-pair encoding](byte-pair-encoding.md)
-* [Transformers](transformers.md)
+* [Byte-pair encoding](/nav_bar/building-and-research/approaches/byte-pair-encoding.md)
+* [Transformers](/nav_bar/building-and-research/approaches/transformers.md)
diff --git a/approaches/rule-based-machine-translation.md b/nav_bar/building-and-research/approaches/rule-based-machine-translation.md
similarity index 100%
rename from approaches/rule-based-machine-translation.md
rename to nav_bar/building-and-research/approaches/rule-based-machine-translation.md
diff --git a/approaches/statistical-machine-translation.md b/nav_bar/building-and-research/approaches/statistical-machine-translation.md
similarity index 91%
rename from approaches/statistical-machine-translation.md
rename to nav_bar/building-and-research/approaches/statistical-machine-translation.md
index 31aae6d34..fd5fa320b 100644
--- a/approaches/statistical-machine-translation.md
+++ b/nav_bar/building-and-research/approaches/statistical-machine-translation.md
@@ -9,7 +9,7 @@ description: Statistical approaches to machine translation
**Statistical machine translation** (SMT) is a machine translation approach that uses large volumes of bilingual data to find the most probable translation for a given input.
Statistical machine translation systems learn to translate by analysing the statistical relationships between original texts and their existing human translations.
-The most important components in statistical machine translation are the translation model and the [language model](/concepts/language-model.md).
+The most important components in statistical machine translation are the translation model and the [language model](/nav_bar/concepts/language-model.md).
### Language model
@@ -19,9 +19,9 @@ The language model can be associated with fluency in the translation because it
### Translation model
-The statistical machine translation model is trained with [parallel data](/customisation/parallel-data.md).
+The statistical machine translation model is trained with [parallel data](/nav_bar/features/customisation/parallel-data.md).
A translation model is a table of aligned phrases and their translation.
-These phrases are called [n-grams](/concepts/n-gram.md).
+These phrases are called [n-grams](/nav_bar/concepts/n-gram.md).
The purpose of the translation model is to predict candidate translations for specific input texts.
The translation model can be associated with adequacy because it preserves the meaning of the source.
diff --git a/approaches/transformer-model-architecture.png b/nav_bar/building-and-research/approaches/transformer-model-architecture.png
similarity index 100%
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index 20123d309..9de257f76 100644
--- a/approaches/transformers.md
+++ b/nav_bar/building-and-research/approaches/transformers.md
@@ -6,7 +6,7 @@ description: A deep learning model architecture for neural machine translation
---
**Transformers** are a type of neural network architecture that relies only on the [attention](/concepts/attention.md) mechanism.
-A transformer allows [neural machine translation](/approaches/neural-machine-translation.md) models to better handle the dependencies between the input and output sequences.
+A transformer allows [neural machine translation](/nav_bar/building-and-research/approaches/neural-machine-translation.md) models to better handle the dependencies between the input and output sequences.
The transformer was first introduced in the paper titled [*”Attention is all you need”*](https://arxiv.org/pdf/1706.03762.pdf) by Google Research in 2017.
@@ -16,11 +16,11 @@ The transformer consists of an encoder and a decoder, which have several layers.
These layers include sublayers of *self-attention* and *feed-forward neural networks*.
> ##### Image from the [Transformers](https://en.wikipedia.org/wiki/Transformer_(machine_learning_model)) Wikipedia article
->
+>
### Encoder
-The input sequence is first embedded into a continuous [vector](/concepts/vector.md) space and then passed through multiple self-attention layers.
+The input sequence is first embedded into a continuous [vector](/nav_bar/building-and-research/concepts/vector.md) space and then passed through multiple self-attention layers.
Each self-attention layer takes the input and creates a context vector based on the weighted sum of the input words.
The generated output encoding then passes through a feed-forward neural network.
The process is repeated for several layers.
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--- /dev/null
+++ b/nav_bar/building-and-research/quality/metrics/metrics.md
@@ -0,0 +1,85 @@
+---
+parent: Building and research
+has_children: true
+nav_order: 150
+title: Metrics
+description: List of metrics for evaluating machine translation
+seo:
+ name: List of metrics for evaluating machine translation
+---
+
+**Metrics** measure the quality of the output of a machine translation system.
+
+Metrics are used to compare different machine translation systems.
+
+The best known metric for machine translation is [BLEU](/nav_bar/building-and-research/metrics/bleu.md), a string-based automatic metric.
+
+{% include collapsible_toc.html %}
+
+## Automatic evaluation metrics
+
+Automatic quality metrics can be computed relatively quickly.
+
+Automatic quality metrics are divided into **string-based metrics** and **machine learning-based metrics**.
+
+### String-based metrics
+
+String-based metrics generally measure the word or character distance between the target sentence and the reference translation.
+
+Examples:
+- [BLEU](/nav_bar/building-and-research/metrics/bleu.md)
+- [METEOR](/nav_bar/building-and-research/metrics/meteor.md)
+- [NIST](/nav_bar/building-and-research/metrics/nist.md)
+- [chrF](/nav_bar/building-and-research/metrics/chrF.md)
+- [TER](/nav_bar/building-and-research/metrics/ter.md)
+
+String-based are used in research papers and competitions because they are explainable and fair, and they can support any language pair.
+
+But string-based metrics can punish translations that convey the correct meaning, and the scores cannot be compared across language pairs.
+
+The scores generally do not correlate well with human evaluation scores when translation quality is high.
+
+
+### Machine learning-based metrics
+
+Machine learning-based metrics use [sentence embeddings](/nav_bar/concepts/sentence-embeddings.md) to calculate the difference between the generated target sentence and the reference translation, or even between the target senternce and the source sentence.
+
+Examples:
+- [COMET](/nav_bar/building-and-research/metrics/comet.md)
+- [YiSi](/nav_bar/building-and-research/metrics/yisi.md)
+- [BERTscore](/nav_bar/building-and-research/metrics/bertscore.md)
+
+Machine learning-based metrics require a model that was trained on data with the source and target languages.
+
+The score can correlate well with human evaluation scores.
+
+But the scores are not explainable or fair, so they cannot be used in a research competition.
+
+
+## Human evaluation metrics
+
+Human evaluation is the gold standard.
+
+- [MQM](/nav_bar/building-and-research/metrics/human-evaluation-metrics.md#mqm)
+- [SQM](/nav_bar/building-and-research/metrics/human-evaluation-metrics.md#sqm)
+- [Average score and average z-score](/nav_bar/building-and-research/metrics/human-evaluation-metrics.md#average-score-and-average-z-score)
+- [TrueSkill](/nav_bar/building-and-research/metrics/human-evaluation-metrics.md#trueskill)
+- [Adecuacy and fluency judgement](/nav_bar/building-and-research/metrics/human-evaluation-metrics.md#adequacy-and-fluency-judgement)
+- [Relative ranking](/nav_bar/building-and-research/metrics/human-evaluation-metrics.md#relative-ranking)
+- [Constituent ranking](/nav_bar/building-and-research/metrics/human-evaluation-metrics.md#constituent-ranking)
+- [Yes or no constituent judgement](/nav_bar/building-and-research/metrics/human-evaluation-metrics.md#yes-and-no-constituent-judgement)
+- [Direct assessment](/nav_bar/building-and-research/metrics/human-evaluation-metrics.md#direct-assessment)
+
+But human evaluation is slow, expensive and subjective.
+
+
+### Evolution
+>
+> ##### Slide from [Unbabel](/nav_bar/more/industry/companies.md#unbabel) for [AMTA 2022](/nav_bar/events/amta2022.md)
+>
+
+
+## See also
+
+- [Quality evaluation](/nav_bar/building-and-research/quality-evaluation.md)
+- [Quality estimation](/nav_bar/building-and-research/quality-estimation.md)
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diff --git a/quality/metrics/metrics.md b/quality/metrics/metrics.md
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index b227fec97..000000000
--- a/quality/metrics/metrics.md
+++ /dev/null
@@ -1,85 +0,0 @@
----
-parent: Building and research
-has_children: true
-nav_order: 150
-title: Metrics
-description: List of metrics for evaluating machine translation
-seo:
- name: List of metrics for evaluating machine translation
----
-
-**Metrics** measure the quality of the output of a machine translation system.
-
-Metrics are used to compare different machine translation systems.
-
-The best known metric for machine translation is [BLEU](bleu.md), a string-based automatic metric.
-
-{% include collapsible_toc.html %}
-
-## Automatic evaluation metrics
-
-Automatic quality metrics can be computed relatively quickly.
-
-Automatic quality metrics are divided into **string-based metrics** and **machine learning-based metrics**.
-
-### String-based metrics
-
-String-based metrics generally measure the word or character distance between the target sentence and the reference translation.
-
-Examples:
-- [BLEU](bleu.md)
-- [METEOR](meteor.md)
-- [NIST](nist.md)
-- [chrF](chrF.md)
-- [TER](ter.md)
-
-String-based are used in research papers and competitions because they are explainable and fair, and they can support any language pair.
-
-But string-based metrics can punish translations that convey the correct meaning, and the scores cannot be compared across language pairs.
-
-The scores generally do not correlate well with human evaluation scores when translation quality is high.
-
-
-### Machine learning-based metrics
-
-Machine learning-based metrics use [sentence embeddings](/concepts/sentence-embeddings.md) to calculate the difference between the generated target sentence and the reference translation, or even between the target senternce and the source sentence.
-
-Examples:
-- [COMET](comet.md)
-- [YiSi](yisi.md)
-- [BERTscore](bertscore.md)
-
-Machine learning-based metrics require a model that was trained on data with the source and target languages.
-
-The score can correlate well with human evaluation scores.
-
-But the scores are not explainable or fair, so they cannot be used in a research competition.
-
-
-## Human evaluation metrics
-
-Human evaluation is the gold standard.
-
-- [MQM](human-evaluation-metrics.md#mqm)
-- [SQM](human-evaluation-metrics.md#sqm)
-- [Average score and average z-score](human-evaluation-metrics.md#average-score-and-average-z-score)
-- [TrueSkill](human-evaluation-metrics.md#trueskill)
-- [Adecuacy and fluency judgement](human-evaluation-metrics.md#adequacy-and-fluency-judgement)
-- [Relative ranking](human-evaluation-metrics.md#relative-ranking)
-- [Constituent ranking](human-evaluation-metrics.md#constituent-ranking)
-- [Yes or no constituent judgement](human-evaluation-metrics.md#yes-and-no-constituent-judgement)
-- [Direct assessment](human-evaluation-metrics.md#direct-assessment)
-
-But human evaluation is slow, expensive and subjective.
-
-
-### Evolution
->
-> ##### Slide from [Unbabel](/industry/companies.md#unbabel) for [AMTA 2022](/events/amta2022.md)
->
-
-
-## See also
-
-- [Quality evaluation](building-and-research/quality-evaluation.md)
-- [Quality estimation](building-and-research/quality-estimation.md)
diff --git a/quality/quality-estimation.md b/quality/quality-estimation.md
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index 7ff2d6eea..000000000
--- a/quality/quality-estimation.md
+++ /dev/null
@@ -1,177 +0,0 @@
----
-parent: Building and research
-nav_order: 165
-title: Quality estimation
-description: Machine translation quality estimation
-featured: true
-redirect_from:
- - /quality-prediction
----
-
-Machine translation **quality estimation** (**QE** or **MTQE**) or machine translation **quality prediction** (**QP** or **MTQP**) is the task of automatically predicting the quality of machine translation output.
-
-| Original | Translation | Quality prediction |
-| -----------------------------| -------------------------------| ---------------------|
-| `English` `July 30th, 2021` | `French` `30 juillet 2021` | `Good` |
-| `English` `This is my home.` | `Spanish` `Este es mi inicio.` | `Bad` |
-
-Quality estimation does not require human reference translations, so it is useful for new content. It uses machine learning to provide predictions that are relatively accurate at the segment level.
-
-Quality estimation is fundamentally different than [quality *evaluation*](/quality/quality-evaluation.md).
-Quality evaluation [metrics](/quality/metrics.md) like [BLEU](/quality/metrics/bleu.md) require human reference translations against which to compare the machine translation output.
-But for new content, there are no human reference translations available.
-
-
-### Use cases
-
-Quality estimation has offline and production use cases:
-
-* [Hybrid translation](/workflows/hybrid-translation.md)
-* Estimating [post-editing](/workflows/post-editing.md) effort
-* Validating final human translations
-* Comparing machine translation systems or translation models
-* Filtering training data for machine translation
-
-The main use case is hybrid translation.
-
-
-### Granularity
-
-Quality estimation is most commonly at the segment-level, for example a sentence.
-
-* Word-level
-* Phrase-level
-* Sentence-level
-* Document-level
-
-Sentence-level scores can be aggregated into paragraph-level scores or document-level scores.
-Word-, phrase- and sentence-level scores can indicate if a machine translation output needs to be post-edited.
-Document-level scores indicate if a machine translation output can be used without human post-editing.
-
----
-
-In 2012, [Lucia Specia](/people/lucia-specia.md) and Google researcher Radu Soricut organized the first *Shared Task on Quality Estimation*.
-
-In 2018, Lucia Specia, Carolina Scarton and Gustavo Henrique Paetzold published the book *Quality Estimation for Machine Translation*.
-There was research on word-level quality estimation and paragraph-level quality estimation.
-
-In 2020, [ModelFront](/industry/companies.md#modelfront) launched a multilingual quality estimation API.
-Tharindu Ranasinghe released pretrained models.
-Facebook Research launched unsupervised quality estimation internally.
-
-A growing set of frameworks, models, and systems are generally available.
-
-### Frameworks
-
-Frameworks from academia and industry are available as open-source code and models.
-
-The first framework, QuEst, was released in 2013.
-
-| Name | Owner | Approach
-| ---------- | --------------------------- | --------------------------- |
-| [QuEst](https://github.com/lspecia/quest) | University of Sheffield | Feature engineering |
-| [QuEst++](https://www.quest.dcs.shef.ac.uk/) | University of Sheffield | Feature engineering |
-| [DeepQuest](https://github.com/sheffieldnlp/deepQuest) | University of Sheffield | Deep learning |
-| [OpenKiwi](https://github.com/Unbabel/OpenKiwi) | Unbabel | Deep learning |
-| [TransQuest](https://github.com/TharinduDR/TransQuest) | Tharindu Ranasinghe, University of Wolverhampton | Deep learning |
-
-TransQuest also includes pretrained models. The models were pretrained with [WMT](/associations/wmt.md) data.
-
-
-### Providers
-
-ModelFront launched a standalone production system for quality estimation.
-By 2020, it was generally available and supported more than 10000 language pairs.
-It is provided as an API, so it can be integrated into other systems and products.
-
-There are also providers that offer a quality estimation feature within another product.
-
-| Provider | Product | Availability | Approach |
-| --- | --- | --- | --- |
-| [KantanAI](/industry/companies.md#kantanmt) | [KantanQES](https://www.kantanai.io/kantanqes-home/) | Feature of machine translation API | Glassbox |
-| [ModelFront](/industry/companies.md#modelfront) | [ModelFront quality prediction](https://modelfront.com) | Product with cloud API and console | Deep learning, multilingual, blackbox |
-| [Omniscien](/industry/companies.md#omniscien-technologies) | [Translation Confidence Scoring and Quality Estimates](https://omniscien.com/products/language-studio/) | Feature of machine translation API | Glassbox |
-
-### Features and integrations
-
-A few translation management systems have launched generally available features for quality estimation.
-
-| Product | Feature | Providers |
-| ---| --- | --- |
-| [KantanStream](/industry/companies.md#kantanmt) | [KantanQES](https://www.kantanai.io/kantanqes-home/) | KantanAI |
-| PhraseTMS | [MT quality estimation](https://support.phrase.com/hc/en-us/articles/5709672289180-MT-Quality-Estimation-TMS-) | Phrase |
-| translate5 | [Quality prediction](https://www.modelfront.com/integrations/translate5) | ModelFront |
-| GlobalDoc LangXpert | [Effort estimation](https://globaldoc.com/about-us/our-technology/langxpert-modelfront-technology-partnership/) | ModelFront |
-| Google Cloud Translation Hub | [Quality prediction](https://cloud.google.com/translation-hub/docs/translator-edit#segment-details) | Google |
-| Crowdin | [Quality prediction](https://store.crowdin.com/modelfront) | ModelFront |
-| memoQ | [Quality estimates](https://www.memoq.com/integrations/mt-quality-estimates) | ModelFront, TAUS |
-
-translate5 is open-source.
-
-
-### Internal systems
-
-More companies have researched or launched quality estimation internally.
-They do not provide quality estimation to others.
-- Amazon
-- Microsoft
-- VMware
-- Facebook AI Research
-- eBay
-- SAP
-- MusixMatch
-- Wayfair
-- Unbabel
-- Transperfect
-- CrossLang
-- Fair Trade Translation
-
-*Note: This list is incomplete.*
-
-## Types
-Quality estimation is typically implemented as classification or regression.
-
-### Supervised
-Supervised quality estimation trains on parallel data that includes human labels or human post-edits.
-
-### Unsupervised
-Unsupervised quality estimation trains on monolingual data or parallel data only.
-Supervised quality estimation relies on labeled or post-edited data.
-
-### Glassbox
-Glassbox approaches are tied to the machine translation system itself.
-A glassbox system makes a prediction based on the internal variables of the machine translation model.
-It is like a confidence score.
-
-### Blackbox
-Blackbox approaches are independent of the machine translation system.
-They are not necessarily trained on the same data, and can be used with any machine translation system.
-
-## Approaches
-
-### Feature engineering
-Early quality estimation approaches use machine learning with feature engineering.
-
-Examples of specific features are the number of noun or prepositional phrases in the source and target, the number of named entities, etc.
-Based on these features, a quality estimation model is built using machine learning techniques.
-
-### Deep learning
-With the rise of deep learning, quality estimation technology resorts to deep learning architectures based on artificial neural networks.
-
-### Single-language-pair
-Early quality estimation approaches created one model or system per language pair, similar to most machine translation systems at the time.
-
-### Multilingual
-Multilingual quality estimation uses one model or system for many language pairs, similar to multilingual machine translation systems.
-
----
-
-### See also
-
-- [Hybrid translation](/workflows/hybrid-translation.md)
-- [Automatic post-editing](/applications/automatic-post-editing.md)
-- [Post-editing](/applications/post-editing.md)
-
-### References
-
-- [*Learn about machine translation quality prediction*](https://www.modelfront.com/quality-prediction)