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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/mklimasz/transferable-modularity

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Is Modularity Transferable? A Case Study through the Lens of Knowledge Distillation

https://github.com/ml-postech/selective-generation

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Selective Generation for Controllable Language Models

https://github.com/ml4ai/nli4wills-corpus

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Validity Assessment of Legal Will Statements as Natural Language Inference

https://github.com/mlburnham/stance_detection_tutorials

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Stance Detection: A Practical Guide to Classifying Political Beliefs in Text

https://github.com/mlpen/Nystromformer

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Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention

https://github.com/mnschmit/SherLIiC

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SherLIiC: A Typed Event-Focused Lexical Inference Benchmark for Evaluating Natural Language Inference

https://github.com/mnschmit/lm-lexical-inference

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Language Models for Lexical Inference in Context

https://github.com/moritzlaurer/zeroshot-classifier

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Building Efficient Universal Classifiers with Natural Language Inference

https://github.com/morning-dews/ST

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Transformation of Dense and Sparse Text Representations

https://github.com/mosharafhossain/afin

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A Question-Answer Driven Approach to Reveal Affirmative Interpretations from Verbal Negations

https://github.com/mosharafhossain/large-afin-and-nlu

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Leveraging Affirmative Interpretations from Negation Improves Natural Language Understanding

https://github.com/mosheber/token_elimination

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Optimizing Retrieval-augmented Reader Models via Token Elimination

https://github.com/mprompting/xlmrprompt

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Discrete and Soft Prompting for Multilingual Models

https://github.com/mr-kenlee/cloze

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Just ClozE! A Novel Framework for Evaluating the Factual Consistency Faster in Abstractive Summarization

https://github.com/mramg-bench/mramg

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MRAMG-Bench: A Comprehensive Benchmark for Advancing Multimodal Retrieval-Augmented Multimodal Generation

https://github.com/mrinal18/MAMBA_QA

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Multilingual State Space Models for Structured Question Answering in Indic Languages

https://github.com/msadat3/mscinli

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MSciNLI: A Diverse Benchmark for Scientific Natural Language Inference

https://github.com/msadat3/scinli

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SciNLI: A Corpus for Natural Language Inference on Scientific Text

https://github.com/msadat3/ssl_for_nli

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Learning to Infer from Unlabeled Data: A Semi-supervised Learning Approach for Robust Natural Language Inference

https://github.com/msadat3/weighted_cotraining

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Co-training for Low Resource Scientific Natural Language Inference

https://github.com/mshukor/vicha

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Efficient Vision-Language Pretraining with Visual Concepts and Hierarchical Alignment

https://github.com/mtanti/mbert-language-specificity

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On the Language-specificity of Multilingual BERT and the Impact of Fine-tuning

https://github.com/muberraozmen/bncl

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Substituting Data Annotation with Balanced Updates and Collective Loss in Multi-label Text Classification

https://github.com/muditchaudhary/CUHK-DSTC9

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Unstructured Knowledge Access in Task-oriented Dialog Modeling using Language Inference, Knowledge Retrieval and Knowledge-Integrative Response Generation

https://github.com/mugekural9/cogeval

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Quantifying Divergence for Human-AI Collaboration and Cognitive Trust

https://github.com/mxschmdt/mrqa-gen-al

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Combining Data Generation and Active Learning for Low-Resource Question Answering

https://github.com/mynlp/ccg2lambda

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Acquisition of Phrase Correspondences using Natural Deduction Proofs

https://github.com/myrthereuver/modeldecisionsstance

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Investigating the Robustness of Modelling Decisions for Few-Shot Cross-Topic Stance Detection: A Preregistered Study

https://github.com/nadiasaeed/medifact-m3g-mediqa-2024

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MediFact at MEDIQA-M3G 2024: Medical Question Answering in Dermatology with Multimodal Learning

https://github.com/namisan/mt-dnn

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Multi-Task Deep Neural Networks for Natural Language Understanding

https://github.com/nativeatom/presque

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Pragmatic Reasoning Unlocks Quantifier Semantics for Foundation Models

https://github.com/nbahador/-fine_tuned_gpt2_model_on_epilepsy_related_pubmed_abstracts

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Localized Definitions and Distributed Reasoning: A Proof-of-Concept Mechanistic Interpretability Study via Activation Patching

https://github.com/ncbi-nlp/BLUE_Benchmark

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Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets

https://github.com/ncbi-nlp/NCBI_BERT

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Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets

https://github.com/necla-ml/SNLI-VE

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Visual Entailment Task for Visually-Grounded Language Learning

https://github.com/nesl/nlp_adversarial_examples

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Generating Natural Language Adversarial Examples

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