Papers with Code 아카이브
Natural Language Inference
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 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:
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/UKPLab/emnlp2020-debiasing-unknown
Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference
https://github.com/Unbabel/COMET
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/UnknownGenie/altered-BERT-KPE
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/VachanVY/gpt.jax
https://github.com/VietHoang1512/khmer-nltk
https://github.com/VietHoang1710/khmer-nltk
https://github.com/Vladimetr/ASR-Knowledge-Transferring
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/W4ngatang/qags
https://github.com/Walter-B/bert-20-classes
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/WangJiuniu/SRQA
Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering
https://github.com/Wattsy2020/closure_learning
Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference
https://github.com/Westlake-AI/openmixup
https://github.com/XINZXZHANG/Bert_repo
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Xaniar87/SAN_SQuAD2
https://github.com/Xinsen-Zhang/transformer
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/XinyuJiang/fudan_nlp_beginner
https://github.com/Y1ran/NLP-BERT--ChineseVersion
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/YC-wind/embedding_study
https://github.com/YIKUAN8/Transformers-VQA
https://github.com/YJHMITWEB/GLoMo-tensorflow
GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations
https://github.com/YYGXjpg/BERT_WL
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/YYGe01/ZEN
ZEN: Pre-training Chinese Text Encoder Enhanced by N-gram Representations
https://github.com/Yipeng91/text_classifier_pub
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/YongWookHa/BERT-on-Pytorch-Template
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/YuanEric88/bert-nlu
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/YuehChuan/bert-Agus
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Zeeshan75/Bert_Telugu_Ner
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Zehui127/SQUAD_BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/ZmeiGorynych/transformer_pytorch
https://github.com/a414351664/Bert_MRC
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/aaronHuang0121/AI-CUP-2020
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/aau-es-ml/ssl_noise-robust_kws
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
https://github.com/abdelghanibelgaid/FNet-TensorFlow-PyTorch
https://github.com/abdumaa/hiqualprop
https://github.com/abelriboulot/onnxt5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.