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Natural Language Inference

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평가표: 43
보존된 평가 기록: 505

Natural language inference (NLI) is the task of determining whether a "hypothesis" is true (entailment), false (contradiction), or undetermined (neutral) given a "premise".

Example:

Premise	Label	Hypothesis
A man inspects the uniform of a figure in some East Asian country.	contradiction	The man is sleeping.
An older and younger man smiling.	neutral	Two men are smiling and laughing at the cats playing on the floor.
A soccer game with multiple males playing.	entailment	Some men are playing a sport.

Approaches used for NLI include earlier symbolic and statistical approaches to more recent deep learning approaches. Benchmark datasets used for NLI include SNLI, MultiNLI, SciTail, among others. You can get hands-on practice on the SNLI task by following this d2l.ai chapter.

Further readings:

Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/eunseo1092/Graduation_project

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/evtaktasheva/dependency_extraction

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/expertailab/spaceqa

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/exploration-lab/iitk-semeval-2024-task-1

SimCSE: Simple Contrastive Learning of Sentence Embeddings

https://github.com/f-data/ADD

Supervised Learning of Universal Sentence Representations from Natural Language Inference Data

https://github.com/fabiocorreacordeiro/Elsevier_abstracts-Classification

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/facebookresearch/DomainBed

Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization

https://github.com/facebookresearch/EgoTV

DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

https://github.com/facebookresearch/GENRE

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

https://github.com/facebookresearch/MLQA

Cross-lingual Language Model Pretraining

https://github.com/facebookresearch/UnsupervisedMT

Cross-lingual Language Model Pretraining

https://github.com/facebookresearch/XLM

XNLI: Evaluating Cross-lingual Sentence Representations

https://github.com/facebookresearch/XNLI

XNLI: Evaluating Cross-lingual Sentence Representations

https://github.com/facebookresearch/anli

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/facebookresearch/atlas

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

https://github.com/facebookresearch/bart_ls

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

https://github.com/facebookresearch/multimodal

CoCa: Contrastive Captioners are Image-Text Foundation Models

https://github.com/facebookresearch/pytext

A Structured Self-attentive Sentence Embedding

https://github.com/facebookresearch/reliable_vqa

How Much Can CLIP Benefit Vision-and-Language Tasks?

https://github.com/facebookresearch/text_characterization_toolkit

HellaSwag: Can a Machine Really Finish Your Sentence?

https://github.com/facebookresearch/vizseq

Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond

https://github.com/facebookresearch/xformers

Big Bird: Transformers for Longer Sequences

https://github.com/faizansuhail89/bert

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/fajri91/indommlu

Few-shot Learning with Multilingual Language Models

https://github.com/fanchenyou/transformer-study

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/fancyerii/bert

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/fastnlp/fastNLP

Star-Transformer

https://github.com/fatma-mohamed-98/Recognising-Textual-Entailment-using-Decomposable-Attention

A Decomposable Attention Model for Natural Language Inference

https://github.com/fciannel/bert_fciannel

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/few-shot-NER-benchmark/BaselineCode

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/feyzaakyurek/XLM-LwLL

XNLI: Evaluating Cross-lingual Sentence Representations

https://github.com/flairNLP/flair

Deep contextualized word representations

https://github.com/flexible-fl/flex-nlp

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/flxst/nerblackbox

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/flyliu2017/bert

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/flyliu2017/bert_modularized

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

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