Papers with Code 아카이브
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:
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/dhlee347/pytorchic-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/dhruvdcoder/sparse-structured-attention
From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification
https://github.com/diabhaque/Sixth-Sense
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/dig-team/hanna-benchmark-asg
https://github.com/dispink/xpt
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/dll-wu/is-cse
https://github.com/dltmddbs100/SimCSE
https://github.com/dmlc/gluon-nlp
A Decomposable Attention Model for Natural Language Inference
https://github.com/dngback/co-forget-protocol
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/donghyeonk/match-lstm
https://github.com/dreamgonfly/BERT-pytorch
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/drgriffis/knn-embedding
Second-Order Word Embeddings from Nearest Neighbor Topological Features
https://github.com/dsindex/iclassifier
Dynamic Self-Attention : Computing Attention over Words Dynamically for Sentence Embedding
https://github.com/duanchi1230/NLP_Project_AI2_Reasoning_Challenge
https://github.com/dunesand/Text-Matching-based-on-ESIM-model
https://github.com/duynguyen158/wann-nlp
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
https://github.com/duyunshu/bert-sentiment-analysis
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/dzqjorking/transpose
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/eagle705/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/eaishwa/quora-ques-pair-similarity
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/eecrazy/TransBERT_ijcai2019
https://github.com/eilon47/DL_Ass4
Recurrent Neural Network-Based Sentence Encoder with Gated Attention for Natural Language Inference
https://github.com/elenalenaelena/machine-learning-viz
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/elkhand/QuoraDuplicates
https://github.com/ellerypan/QUORA-INSINCERE-QUESTION-CLASSIFICATION
Dynamic Meta-Embeddings for Improved Sentence Representations
https://github.com/enod/arxiv-nlp-notes
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/enzomuschik/distilfnd
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/epcpu/deep-nlp-links
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/epfml/collaborative-attention
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/eric-erki/decaNLP
The Natural Language Decathlon: Multitask Learning as Question Answering
https://github.com/ericperfect/Bert_Position_BiLSTM_Attention_CRF_LSTMDecoder
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/erksch/fnet-pytorch
https://github.com/esadsetts/Mod_4_project
https://github.com/eternityyw/tram-benchmark
https://github.com/ethanjperez/true_few_shot
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.