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Natural Language Inference

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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/orhonovich/q-squared

$Q^{2}$: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering

https://github.com/orparask/VS-Loss

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

https://github.com/osu-nlp-group/mind2web

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

https://github.com/overfitter/biobert_embedding

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

https://github.com/owainwest/uniprot_bert

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

https://github.com/paddorch/SelfAttnSent.paddle

A Structured Self-attentive Sentence Embedding

https://github.com/paolanu/BERT_epitope

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

https://github.com/pauldevos/python-notes

XLNet: Generalized Autoregressive Pretraining for Language Understanding

https://github.com/pengming617/bert_classification

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

https://github.com/pengshuyuan/Bert

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

https://github.com/pfecht/bert-exploration

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

https://github.com/philschmid/knowledge-distillation-transformers-pytorch-sagemaker

DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

https://github.com/phohenecker/pytorch-transformer

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

https://github.com/phueb/BabyBertSRL

Multi-Task Deep Neural Networks for Natural Language Understanding

https://github.com/phueb/CHILDES-SRL

Multi-Task Deep Neural Networks for Natural Language Understanding

https://github.com/piegu/language-models

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

https://github.com/pingheng001/Cnn-Bert

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

https://github.com/pisalore/roberta_results

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

https://github.com/plkmo/BERT-Relation-Extraction

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

https://github.com/polyusmart/poll-question-generation

https://github.com/polzinben/TensorFlow_NLP

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

https://github.com/postech-ami/smile-dataset

Language Models are Few-Shot Learners

https://github.com/prabhakar267/LASER-improved

Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond

https://github.com/prakashpandey9/Text-Classification-Pytorch

A Structured Self-attentive Sentence Embedding

https://github.com/primeqa/primeqa

MuMuQA: Multimedia Multi-Hop News Question Answering via Cross-Media Knowledge Extraction and Grounding

https://github.com/princeton-nlp/helmet

QAMPARI: An Open-domain Question Answering Benchmark for Questions with Many Answers from Multiple Paragraphs

https://github.com/princeton-nlp/rationale-robustness

SPECTRA: Sparse Structured Text Rationalization

https://github.com/priyamtejaswin/multistep-retrieve-summarize

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

https://github.com/ps2811/Bert-Multi-Label-Text-Classification-GitVersion

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

https://github.com/pvgladkov/knowledge-distillation

Distilling Task-Specific Knowledge from BERT into Simple Neural Networks

https://github.com/pwc-1/Paper-10/tree/main/squeezebert

SqueezeBERT: What can computer vision teach NLP about efficient neural networks?

https://github.com/pwc-1/Paper-10/tree/main/tinybert

TinyBERT: Distilling BERT for Natural Language Understanding

https://github.com/pwc-1/Paper-5/tree/main/mobilebert

MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices

https://github.com/pwc-1/Paper-5/tree/main/mt5

mT5: A massively multilingual pre-trained text-to-text transformer

https://github.com/pwc-1/Paper-8/tree/main/albert

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

https://github.com/pwc-1/Paper-8/tree/main/big_bird

Big Bird: Transformers for Longer Sequences

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