Papers with Code 아카이브

Natural Language Inference

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 3
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/ai-systems/hybrid_autoregressive_inference

공식 코드

Hybrid Autoregressive Inference for Scalable Multi-hop Explanation Regeneration

https://github.com/ai-systems/nli4ct

공식 코드

NLI4CT: Multi-Evidence Natural Language Inference for Clinical Trial Reports

https://github.com/ai-systems/transportability

공식 코드

Switching Contexts: Transportability Measures for NLP

https://github.com/aic-factcheck/csfever-and-ctkfacts-paper

공식 코드

CsFEVER and CTKFacts: Acquiring Czech data for fact verification

https://github.com/aizanzafar/MedLogicAQA

공식 코드

MedLogic-AQA: Enhancing Medical Question Answering with Abstractive Models Focusing on Logical Structures

https://github.com/alapanju/NewsStake

공식 코드

From Text to Context: An Entailment Approach for News Stakeholder Classification

https://github.com/alexkoulakos/explain-then-predict

공식 코드

Enhancing adversarial robustness in Natural Language Inference using explanations

https://github.com/alexwarstadt/data_generation

공식 코드

Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition

https://github.com/alinlab/MASKER

공식 코드

MASKER: Masked Keyword Regularization for Reliable Text Classification

https://github.com/alireza-javanmardi/conformal-credal-sets

공식 코드

Conformalized Credal Set Predictors

https://github.com/alirezasalemi7/arman

공식 코드

ARMAN: Pre-training with Semantically Selecting and Reordering of Sentences for Persian Abstractive Summarization

https://github.com/alisawuffles/wanli

공식 코드

WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation

https://github.com/allegro/klejbenchmark-baselines

공식 코드

KLEJ: Comprehensive Benchmark for Polish Language Understanding

https://github.com/allenai/close

공식 코드

I Can't Believe There's No Images! Learning Visual Tasks Using only Language Supervision

https://github.com/allenai/label_rationale_association

공식 코드

Measuring Association Between Labels and Free-Text Rationales

https://github.com/allenai/sledgehammer

공식 코드

The Right Tool for the Job: Matching Model and Instance Complexities

https://github.com/allenai/visual-reasoning-rationalization

공식 코드

Natural Language Rationales with Full-Stack Visual Reasoning: From Pixels to Semantic Frames to Commonsense Graphs

https://github.com/altsoph/EENLP

공식 코드

EENLP: Cross-lingual Eastern European NLP Index

https://github.com/alvinchangw/CARA_EMNLP2020

공식 코드

Poison Attacks against Text Datasets with Conditional Adversarially Regularized Autoencoder

https://github.com/amazon-research/mix-generation

공식 코드

MixGen: A New Multi-Modal Data Augmentation

https://github.com/amazon-research/sentence-representations

공식 코드

Pairwise Supervised Contrastive Learning of Sentence Representations

https://github.com/amazon-science/aws-swing

공식 코드

SWING: Balancing Coverage and Faithfulness for Dialogue Summarization

https://github.com/amazon-science/characterizing-measuring-linguistic-drift

공식 코드

Characterizing and Measuring Linguistic Dataset Drift

https://github.com/amazon-science/contextual-product-qa

공식 코드

xPQA: Cross-Lingual Product Question Answering across 12 Languages

https://github.com/amazonqa/amazonqa

공식 코드

AmazonQA: A Review-Based Question Answering Task

https://github.com/ameyagodbole/long-tail-likelihood-splits

공식 코드

Benchmarking Long-tail Generalization with Likelihood Splits

https://github.com/amirabaskohi/lm-cppf

공식 코드

LM-CPPF: Paraphrasing-Guided Data Augmentation for Contrastive Prompt-Based Few-Shot Fine-Tuning

https://github.com/amitkumarj441/cikm2023_subspaceembedding

공식 코드

Lightweight Adaptation of Neural Language Models via Subspace Embedding

https://github.com/amodaresi/debias_w_saliencies

공식 코드

Guide the Learner: Controlling Product of Experts Debiasing Method Based on Token Attribution Similarities

https://github.com/anastasiakrith/multimodal-retrieval-for-vwsd

공식 코드

Large Language Models and Multimodal Retrieval for Visual Word Sense Disambiguation

https://github.com/anranwu-richpo/dcqa

공식 코드

DCQA: Document-Level Chart Question Answering towards Complex Reasoning and Common-Sense Understanding

https://github.com/ant-research/structuredlm_rtdt

공식 코드

Augmenting Transformers with Recursively Composed Multi-grained Representations

https://github.com/antongolubev5/Auto-Dataset-For-Transfer-Learning

공식 코드

Transfer Learning for Improving Results on Russian Sentiment Datasets

https://github.com/antongolubev5/Targeted-SA-for-Russian-Datasets

공식 코드

Improving Results on Russian Sentiment Datasets

https://github.com/aoshuang92/css_uq_llms

공식 코드

CSS: Contrastive Semantic Similarity for Uncertainty Quantification of LLMs

https://github.com/apple/ml-tweak

공식 코드

Think While You Write: Hypothesis Verification Promotes Faithful Knowledge-to-Text Generation

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