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Natural Language Inference

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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/BM-K/KoSimCSE_SKT

SimCSE: Simple Contrastive Learning of Sentence Embeddings

https://github.com/BPYap/BERTSel

BERTSel: Answer Selection with Pre-trained Models

https://github.com/BR-IDL/PaddleViT/blob/main/image_classification/gMLP

Pay Attention to MLPs

https://github.com/Beacontownfc/rembert_paddle

Rethinking embedding coupling in pre-trained language models

https://github.com/BinWang28/BERT_Sentence_Embedding

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

https://github.com/BinWang28/SBERT-WK-Sentence-Embedding

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

https://github.com/BirgerMoell/bertcastle

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

https://github.com/BroCoLySTyLe/SQLovaReview

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

https://github.com/CLUEbenchmark/CLUE

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

https://github.com/CVxTz/music_genre_classification

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

https://github.com/Caesarzhang/bert-zh

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

https://github.com/CalumPerrio/WNUT-2020

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/CenIII/WSNet

A Structured Self-attentive Sentence Embedding

https://github.com/Chaolei98/FreeZAD

CoCa: Contrastive Captioners are Image-Text Foundation Models

https://github.com/Charliesgithub20221030/BERT

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

https://github.com/ChernovAndrey/chronos-forecasting-wasserstein

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

https://github.com/Chonwai/Learning_BERT

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

https://github.com/ClaudiaRaffaelli/Protein-subcellular-localization

Deep contextualized word representations

https://github.com/Coda-s/BJTU_NLP_Practice

Enhanced LSTM for Natural Language Inference

https://github.com/CogComp/BenchmarkingZeroShot

Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach

https://github.com/Colanim/BERT_STS-B

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

https://github.com/CyberZHG/keras-bert

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

https://github.com/DAI-Lab/fibber

Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment

https://github.com/DataScienceNigeria/ALBERT-for-Natural-Language-Processing

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

https://github.com/DataScienceNigeria/ERNIE-2.0-from-Baidu-Inc.

ERNIE 2.0: A Continual Pre-training Framework for Language Understanding

https://github.com/DataScienceNigeria/Supervised-NLP-from-Facebook-AI-Team

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

https://github.com/DeNeutoy/Decomposable_Attn

A Decomposable Attention Model for Natural Language Inference

https://github.com/DeligientSloth/QQsim

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

https://github.com/DeligientSloth/bert-tensorflow

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

https://github.com/Delta-ML/delta

DELTA: A DEep learning based Language Technology plAtform

https://github.com/DeokO/bert-excercise-ongoing

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

https://github.com/DevSinghSachan/art

End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering

https://github.com/DimasDMM/nlp-completer

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

https://github.com/DimasDMM/transformers

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

https://github.com/Div290/SplitEE/blob/main/README.md

SplitEE: Early Exit in Deep Neural Networks with Split Computing

https://github.com/Doffery/BERT-Sentiment-Analysis-Amazon-Review

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

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.