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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:

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/allenai/macaw

Language Models are Few-Shot Learners

https://github.com/allenai/scientific-claim-generation

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

https://github.com/allenai/scifact

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

https://github.com/allenai/semantic_fragments

Probing Natural Language Inference Models through Semantic Fragments

https://github.com/alvinchangw/COCON_ICLR2021

Poison Attacks against Text Datasets with Conditional Adversarially Regularized Autoencoder

https://github.com/amanb2000/magic_words

AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts

https://github.com/amauriciorr/AubreyBot

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

https://github.com/amazon-science/alexa-teacher-models

AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model

https://github.com/amazon-science/chronos-forecasting

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

https://github.com/ambityga/memsem

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

https://github.com/amitakamath/hard_positives

CoCa: Contrastive Captioners are Image-Text Foundation Models

https://github.com/amitakamath/whatsup_vlms

CoCa: Contrastive Captioners are Image-Text Foundation Models

https://github.com/amoramine/FNet_classification

FNet: Mixing Tokens with Fourier Transforms

https://github.com/amoramine/FNet_with_BART_classification

FNet: Mixing Tokens with Fourier Transforms

https://github.com/amore-upf/masked-coreference

SpanBERT: Improving Pre-training by Representing and Predicting Spans

https://github.com/anaishoareau/french_preprocessing

CamemBERT: a Tasty French Language Model

https://github.com/andi611/Mockingjay-Speech-Representation

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

https://github.com/anirudharc/transformer

A Structured Self-attentive Sentence Embedding

https://github.com/ankurbanga/Language-Models

Deep contextualized word representations

https://github.com/anonwhymoos/connectivity

BERTs of a feather do not generalize together: Large variability in generalization across models with similar test set performance

https://github.com/anshulwadhawan/ABSA

Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence

https://github.com/anton-bushuiev/ppiformer

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

https://github.com/antonyvigouret/Pay-Attention-to-MLPs

Pay Attention to MLPs

https://github.com/anupamsingh610/bert_ner_stride

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

https://github.com/appcoreopc/berty

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

https://github.com/applicaai/senteval

SentEval: An Evaluation Toolkit for Universal Sentence Representations

https://github.com/aqweteddy/BERTDistilForClassification

Distilling Task-Specific Knowledge from BERT into Simple Neural Networks

https://github.com/archinetai/smart-pytorch

SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization

https://github.com/archinetai/vat-pytorch

SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization

https://github.com/arielsho/decomposition-table-reasoning

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

https://github.com/arkel23/PyTorch-Pretrained-ViT

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

https://github.com/asahi417/lmppl

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

https://github.com/asahi417/relbert

Language Models are Few-Shot Learners

https://github.com/asgaardlab/test-case-similarity-technique

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

https://github.com/ashi-ta/speechglue

GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

https://github.com/ashutosh1919/data2vec-pytorch

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

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