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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/fshaik8/mismatched

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A MISMATCHED Benchmark for Scientific Natural Language Inference

https://github.com/fudanvi/generative-abstract-reasoning

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Abstracting Concept-Changing Rules for Solving Raven's Progressive Matrix Problems

https://github.com/fulifeng/Counterfactual_Reasoning_Model

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Empowering Language Understanding with Counterfactual Reasoning

https://github.com/furrutiav/nllf-emnlp-2023

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Deep Natural Language Feature Learning for Interpretable Prediction

https://github.com/galsang/SATA-Tree-LSTM

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Dynamic Compositionality in Recursive Neural Networks with Structure-aware Tag Representations

https://github.com/georgevern/smala

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Subword Mapping and Anchoring across Languages

https://github.com/getalp/Flaubert

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FlauBERT: Unsupervised Language Model Pre-training for French

https://github.com/gijswijnholds/sick_nl

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SICKNL: A Dataset for Dutch Natural Language Inference

https://github.com/gingasan/adversarialsa

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Adversarial Self-Attention for Language Understanding

https://github.com/gmichalo/UmlsBERT

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UmlsBERT: Clinical Domain Knowledge Augmentation of Contextual Embeddings Using the Unified Medical Language System Metathesaurus

https://github.com/gomate-community/trustrag

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TrustRAG: An Information Assistant with Retrieval Augmented Generation

https://github.com/google-deepmind/inli

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Entailed Between the Lines: Incorporating Implication into NLI

https://github.com/google-research-datasets/NewsQuizQA

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Quiz-Style Question Generation for News Stories

https://github.com/google-research-datasets/Textual-Entailment-New-Protocols

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New Protocols and Negative Results for Textual Entailment Data Collection

https://github.com/google-research-datasets/wiki-translated-clusters-nli

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Stretching Sentence-pair NLI Models to Reason over Long Documents and Clusters

https://github.com/google-research-datasets/xsum_hallucination_annotations

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On Faithfulness and Factuality in Abstractive Summarization

https://github.com/google-research/ALBERT

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

https://github.com/google-research/bert

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

https://github.com/google-research/bigbird

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Big Bird: Transformers for Longer Sequences

https://github.com/google-research/byt5

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ByT5: Towards a token-free future with pre-trained byte-to-byte models

https://github.com/google-research/flan

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Finetuned Language Models Are Zero-Shot Learners

https://github.com/google-research/google-research

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RealFormer: Transformer Likes Residual Attention

https://github.com/google-research/google-research/tree/master/true_teacher

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TrueTeacher: Learning Factual Consistency Evaluation with Large Language Models

https://github.com/google-research/language

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Pretraining with Contrastive Sentence Objectives Improves Discourse Performance of Language Models

https://github.com/google-research/language/tree/master/language/bert_extraction

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Thieves on Sesame Street! Model Extraction of BERT-based APIs

https://github.com/google-research/multilingual-t5

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

https://github.com/google-research/tapas

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Understanding tables with intermediate pre-training

https://github.com/google/BEGIN-dataset

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Evaluating Attribution in Dialogue Systems: The BEGIN Benchmark

https://github.com/gowitheflow-1998/pixel-linguist

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Pixel Sentence Representation Learning

https://github.com/gsh199449/productqa

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Product-Aware Answer Generation in E-Commerce Question-Answering

https://github.com/gt4sd/zberta

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Z-BERT-A: a zero-shot Pipeline for Unknown Intent detection

https://github.com/gt4sd/zero-shot-bert-adapters

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Z-BERT-A: a zero-shot Pipeline for Unknown Intent detection

https://github.com/gtfintechlab/kg-qagen

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KG-QAGen: A Knowledge-Graph-Based Framework for Systematic Question Generation and Long-Context LLM Evaluation

https://github.com/guilk/kat

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KAT: A Knowledge Augmented Transformer for Vision-and-Language

https://github.com/gyauney/data-label-alignment

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Comparing Text Representations: A Theory-Driven Approach

https://github.com/hannahxchen/balanced-adversarial-training

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Balanced Adversarial Training: Balancing Tradeoffs between Fickleness and Obstinacy in NLP Models

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