Papers with Code 아카이브

Natural Language Inference

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 3
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/Domminique/Deploy-BERT-for-Sentiment-Analysis-with-FastAPI-

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

https://github.com/Dongcf/Pytorch_Bert_Text_Classification

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

https://github.com/DorinK/Implementing-an-SNLI-Paper

Shortcut-Stacked Sentence Encoders for Multi-Domain Inference

https://github.com/DylanCope/Image-Captioning-with-Bimodal-Transformers

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

https://github.com/Eeun-ju/NLP-study

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

https://github.com/EightRice/atn_GPT-3

Language Models are Few-Shot Learners

https://github.com/EleutherAI/gpt-neo

Language Models are Few-Shot Learners

https://github.com/EleutherAI/lm-evaluation-harness

Language Models are Few-Shot Learners

https://github.com/EleutherAI/lm_evaluation_harness

Language Models are Few-Shot Learners

https://github.com/ElizaLo/Question-Answering-based-on-SQuAD

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

https://github.com/Elvirasun28/quora-question-duplicate

Bilateral Multi-Perspective Matching for Natural Language Sentences

https://github.com/EssayKillerBrain/NLP-BERT-Chinese

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

https://github.com/EthanWYB/bert-classification

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

https://github.com/EuphoriaYan/bert_component

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

https://github.com/ExplorerFreda/Structured-Self-Attentive-Sentence-Embedding

A Structured Self-attentive Sentence Embedding

https://github.com/Farahn/AES

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

https://github.com/FaskyCC/TextClassification

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

https://github.com/FengJiaChunFromSYSU/Bert

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

https://github.com/FerryHuang/SimVLM

SimVLM: Simple Visual Language Model Pretraining with Weak Supervision

https://github.com/G-4-R-Y/Tweet-Sentiment-Extraction

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/G-4-R-Y/Tweet-Sentiment-Extraction-roBERTa-5fold

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/Gaozhen0816/BERT_QA_For_AILaw

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

https://github.com/Gaozhen0816/BERT_QA_for_Chinese

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

https://github.com/GauthierDmn/question_answering

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

https://github.com/GeorgeLuImmortal/Hierarchical-BERT-Model-with-Limited-Labelled-Data

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/Guillem96/data2vec-vision

data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language

https://github.com/Guowei-Yan/Data

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

https://github.com/HHousen/TransformerSum

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

https://github.com/HIT-SCIR/ELMoForManyLangs

Deep contextualized word representations

https://github.com/HJHGJGHHG/Paddle-FNet

FNet: Mixing Tokens with Fourier Transforms

https://github.com/HKUST-KnowComp/MnemonicReader

Machine Comprehension Using Match-LSTM and Answer Pointer

https://github.com/HKUST-KnowComp/R-Net

Machine Comprehension Using Match-LSTM and Answer Pointer

https://github.com/HUSTLyn/SentEval

SentEval: An Evaluation Toolkit for Universal Sentence Representations

https://github.com/Hanlard/Bert-for-WebQA

Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering

https://github.com/HareeshBahuleyan/size-fit-net

Supervised Learning of Universal Sentence Representations from Natural Language Inference Data

https://github.com/HazyResearch/hgcn

Hyperbolic Neural Networks

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