Skip to content

Latest commit

 

History

History
69 lines (60 loc) · 2.89 KB

README.MD

File metadata and controls

69 lines (60 loc) · 2.89 KB
Raw

Introduction

Code for the paper Exploring the zero-shot limit of FewRel. This repository implements a zero-shot relation extractor.

Dataset

The dataset FewRel 1.0 has been created in the paper FewRel: A Large-Scale Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation and is available here.

Run the Extractor from the notebook

An example relation extraction is in this notebook. The extractor needs a list of candidate relations in English

relations = ['noble title', 'founding date', 'occupation of a person']
extractor = RelationExtractor(model, tokenizer, relations)

Then the model ranks the surface forms by the belief that the relation connects the entities in the text

extractor.rank(text='John Smith received an OBE', head='John Smith', tail='OBE')

[('noble title', 0.9690611883997917),
 ('occupation of a person', 0.0012609362602233887),
 ('founding date', 0.00024014711380004883)]

Training

This repository contains 4 training scripts related to the 4 models in the paper.

train_bert_large_with_squad.py
train_bert_large_without_squad.py
train_distillbert_with_squad.py
train_distillbert_without_squad.py

Validation

There are also 4 scripts for validation

test_bert_large_with_squad.py
test_bert_large_without_squad.py
test_distillbert_with_squad.py
test_distillbert_without_squad.py

The results as in the paper are

Model 0-shot 5-ways 0-shot 10-ways
(1) Distillbert 70.1±0.5 55.9±0.6
(2) Bert Large 80.8±0.4 69.6±0.5
(3) Distillbert + SQUAD 81.3±0.4 70.0±0.2
(4) Bert Large + SQUAD 86.0±0.6 76.2±0.4

Cite as

@inproceedings{cetoli-2020-exploring,
    title = "Exploring the zero-shot limit of {F}ew{R}el",
    author = "Cetoli, Alberto",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.124",
    doi = "10.18653/v1/2020.coling-main.124",
    pages = "1447--1451",
    abstract = "This paper proposes a general purpose relation extractor that uses Wikidata descriptions to represent the relation{'}s surface form. The results are tested on the FewRel 1.0 dataset, which provides an excellent framework for training and evaluating the proposed zero-shot learning system in English. This relation extractor architecture exploits the implicit knowledge of a language model through a question-answering approach.",
}