Papers with Code 아카이브
Natural Language Inference
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
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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:
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/HiJiGOO/self-attentive
https://github.com/Hibb-bb/ALLM
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Hironsan/anago
https://github.com/Holldean/BERT-Pruning
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/HuihuiChyan/BJTUNLP_Practice2020
https://github.com/HuihuiChyan/BJTUNLP_Practice2021
https://github.com/IBM/MAX-Question-Answering
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/IBM/MAX-Text-Sentiment-Classifier
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/IBM/MAX-Toxic-Comment-Classifier
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/IMPLabUniPr/UniParma-at-semeval-2021-task-5
https://github.com/INK-USC/MHGRN
KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning
https://github.com/Impavidity/relogic
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/IndicoDataSolutions/finetune
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/IsaacRodgz/mmbt_experiments
Supervised Multimodal Bitransformers for Classifying Images and Text
https://github.com/IsaacRodgz/multimodal-transformers-movies
Supervised Multimodal Bitransformers for Classifying Images and Text
https://github.com/JA-Bar/nlp-depression
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/JHKim-snu/GVCCI
https://github.com/JHKim-snu/PGA
https://github.com/JHart96/keras_elmo_embedding_layer
https://github.com/JMSaindon/NlpSquad
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/JNUpython/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/JONGWE1/Paddle-DeBERTa
https://github.com/JRC1995/Abstractive-Summarization
https://github.com/Jackma3/NER-Bert-BiLstm
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Jason-J-Choi/DeBERTa_TxtClassifier
https://github.com/JeheonPark596/sentimentsurvival
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/JeremyZhao1998/Nystromformer-Paddle
Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention
https://github.com/Jessicak016/lstm_nli
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
https://github.com/JoegameZhou/mPanGu-Alpha-53
https://github.com/JohannLee1996/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Jun-Zhang-32108/Sentiment-Analysis
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/JunnYu/x-transformers-paddle
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/JunnYu/xlm_paddle
https://github.com/KAGUYAHONGLAI/SRC
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/KLUE-benchmark/KLUE-baseline
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.