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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/yanqi1811/attention-is-all-you-need

A Structured Self-attentive Sentence Embedding

https://github.com/yanzhitech/bert

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

https://github.com/yaushian/msimcse

SimCSE: Simple Contrastive Learning of Sentence Embeddings

https://github.com/yg211/bert_nli

Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference

https://github.com/yifding/hetseq

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

https://github.com/yikangshen/megablocks

ST-MoE: Designing Stable and Transferable Sparse Expert Models

https://github.com/yinghao1019/imdb_prac

Combining Similarity Features and Deep Representation Learning for Stance Detection in the Context of Checking Fake News

https://github.com/yiyc-kor/bert-study

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

https://github.com/yiyixuxu/n-grammer-flax

N-Grammer: Augmenting Transformers with latent n-grams

https://github.com/yizhongw/tk-instruct

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

https://github.com/yli-z/ml4h_are_clinical_t5_models_better_for_clinical_text

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

https://github.com/ymcui/LAMB_Optimizer_TF

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

https://github.com/yongbowin/san_mrc_annotation

Stochastic Answer Networks for Natural Language Inference

https://github.com/yoreG123/Paddle-ByT5

ByT5: Towards a token-free future with pre-trained byte-to-byte models

https://github.com/yoshitomo-matsubara/torchdistill

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

https://github.com/young-zonglin/bilm-tf-extended

Deep contextualized word representations

https://github.com/ypeleg/MinimalIsAllYouNeed

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

https://github.com/ys-zong/medfair

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

https://github.com/yuanjing-zhu/elmo

Deep contextualized word representations

https://github.com/yuanxiaosc/ELMo

Deep contextualized word representations

https://github.com/yuehwai0508/Bert_TFL

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

https://github.com/yuelinan/c3vg

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

https://github.com/yueyongjiao/Transformer-Contextual

A Structured Self-attentive Sentence Embedding

https://github.com/yueyongjiao/attention_is_all_you_need_pytorch

A Structured Self-attentive Sentence Embedding

https://github.com/yuhangT/tf_bert

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

https://github.com/yulong-XJTU/SimVLM

SimVLM: Simple Visual Language Model Pretraining with Weak Supervision

https://github.com/yuriak/PPDAI

A Structured Self-attentive Sentence Embedding

https://github.com/yuyangdu01/llm-cl-vqa

Surgical-VQLA: Transformer with Gated Vision-Language Embedding for Visual Question Localized-Answering in Robotic Surgery

https://github.com/ywu94/Code-Notes

Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence

https://github.com/yxzwang/normalized-information-payload

Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention

https://github.com/yydai/bert_test

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

https://github.com/yyfsXZ/structured_self_attention_sent_embedding

A Structured Self-attentive Sentence Embedding

https://github.com/yyhappier/shortcutsuite

A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference

https://github.com/zaaztt/bert_chinese_example

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

https://github.com/zapplea/bert

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

https://github.com/zaradana/Fast_BERT

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.