SOTAVerified

Natural Language Inference

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:

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1101–1150 of 1961 papers

TitleStatusHype
Unsupervised Pre-training with Structured Knowledge for Improving Natural Language Inference—0
Unsupervised Question Answering for Fact-Checking—0
Unsupervised Relation Discovery with Sense Disambiguation—0
Unsupervised Source Hierarchies for Low-Resource Neural Machine Translation—0
Unsupervised Transfer Learning via BERT Neuron Selection—0
Unsupervised Verb Inference from Nouns Crossing Root Boundary—0
User Edits Classification Using Document Revision Histories—0
USFD at SemEval-2016 Task 6: Any-Target Stance Detection on Twitter with Autoencoders—0
Using a Weighted Semantic Network for Lexical Semantic Relatedness—0
Using contradictions improves question answering systems—0
Using Discourse Information for Paraphrase Extraction—0
Using English Dictionaries to generate Commonsense Knowledge in Natural Language—0
Using Minimal Recursion Semantics for Entailment Recognition—0
Using Natural Language Inference to Improve Persona Extraction from Dialogue in a New Domain—0
Using Similarity to Evaluate Factual Consistency in Summaries—0
Using the text to evaluate short answers for reading comprehension exercises—0
UTexas: Natural Language Semantics using Distributional Semantics and Probabilistic Logic—0
UTTime: Temporal Relation Classification using Deep Syntactic Features—0
UU\_TAILS at MEDIQA 2019: Learning Textual Entailment in the Medical Domain—0
UWB at SemEval-2016 Task 6: Stance Detection—0
UW-BHI at MEDIQA 2019: An Analysis of Representation Methods for Medical Natural Language Inference—0
Validation of Facts Against Textual Sources—0
Variational Inference and Deep Generative Models—0
VENSESEVAL at Semeval-2016 Task 2 iSTS - with a full-fledged rule-based approach—0
Verbing Weirds Language (Models): Evaluation of English Zero-Derivation in Five LLMs—0
Verbs Taking Clausal and Non-Finite Arguments as Signals of Modality -- Revisiting the Issue of Meaning Grounded in Syntax—0
ViANLI: Adversarial Natural Language Inference for Vietnamese—0
ViNLI: A Vietnamese Corpus for Studies on Open-Domain Natural Language Inference—0
Visual Denotations for Recognizing Textual Entailment—0
Visual Entailment: A Novel Task for Fine-Grained Image Understanding—0
Visual Entailment Task for Visually-Grounded Language Learning—0
Visual Interrogation of Attention-Based Models for Natural Language Inference and Machine Comprehension—0
Visually grounded generation of entailments from premises—0
Visually-Verifiable Textual Entailment: A Challenge Task for Combining Language and Vision—0
VLSP 2021 - ViMRC Challenge: Vietnamese Machine Reading Comprehension—0
VLSP 2023 -- LTER: A Summary of the Challenge on Legal Textual Entailment Recognition—0
VUACLTL at SemEval 2016 Task 12: A CRF Pipeline to Clinical TempEval—0
What can we learn from Semantic Tagging?—0
What does the Failure to Reason with "Respectively" in Zero/Few-Shot Settings Tell Us about Language Models?—0
What do Language Models know about word senses? Zero-Shot WSD with Language Models and Domain Inventories—0
What Knowledge is Needed to Solve the RTE5 Textual Entailment Challenge?—0
What Would Elsa Do? Freezing Layers During Transformer Fine-Tuning—0
When Does Meaning Backfire? Investigating the Role of AMRs in NLI—0
Where's the meeting that was cancelled? existential implications of transitive verbs—0
Where Was Alexander the Great in 325 BC? Toward Understanding History Text with a World Model—0
"Who can help me?": Knowledge Infused Matching of Support Seekers and Support Providers during COVID-19 on Reddit—0
``Who said it, and Why?'' Provenance for Natural Language Claims—0
Why and How to Pay Different Attention to Phrase Alignments of Different Intensities—0
"Why is this misleading?": Detecting News Headline Hallucinations with Explanations—0
Would you Rather? A New Benchmark for Learning Machine Alignment with Cultural Values and Social Preferences—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UnitedSynT5 (3B)% Test Accuracy94.7—Unverified
2UnitedSynT5 (335M)% Test Accuracy93.5—Unverified
3EFL (Entailment as Few-shot Learner) + RoBERTa-large% Test Accuracy93.1—Unverified
4Neural Tree Indexers for Text Understanding% Test Accuracy93.1—Unverified
5RoBERTa-large+Self-Explaining% Test Accuracy92.3—Unverified
6RoBERTa-large + self-explaining layer% Test Accuracy92.3—Unverified
7CA-MTL% Test Accuracy92.1—Unverified
8SemBERT% Test Accuracy91.9—Unverified
9MT-DNN-SMARTLARGEv0% Test Accuracy91.7—Unverified
10MT-DNN-SMART_100%ofTrainingDataDev Accuracy91.6—Unverified
#ModelMetricClaimedVerifiedStatus
1Vega v2 6B (KD-based prompt transfer)Accuracy96—Unverified
2PaLM 540B (fine-tuned)Accuracy95.7—Unverified
3Turing NLR v5 XXL 5.4B (fine-tuned)Accuracy94.1—Unverified
4ST-MoE-32B 269B (fine-tuned)Accuracy93.5—Unverified
5DeBERTa-1.5BAccuracy93.2—Unverified
6MUPPET Roberta LargeAccuracy92.8—Unverified
7DeBERTaV3largeAccuracy92.7—Unverified
8T5-XXL 11BAccuracy92.5—Unverified
9T5-XXL 11B (fine-tuned)Accuracy92.5—Unverified
10ST-MoE-L 4.1B (fine-tuned)Accuracy92.1—Unverified
#ModelMetricClaimedVerifiedStatus
1UnitedSynT5 (3B)Matched92.6—Unverified
2Turing NLR v5 XXL 5.4B (fine-tuned)Matched92.6—Unverified
3T5-XXL 11B (fine-tuned)Matched92—Unverified
4T5Matched92—Unverified
5T5-11BMismatched91.7—Unverified
6T5-3BMatched91.4—Unverified
7ALBERTMatched91.3—Unverified
8DeBERTa (large)Matched91.1—Unverified
9Adv-RoBERTa ensembleMatched91.1—Unverified
10SMARTRoBERTaDev Matched91.1—Unverified