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 1701–1750 of 1961 papers

TitleStatusHype
SemEval-2015 Task 2: Semantic Textual Similarity, English, Spanish and Pilot on Interpretability—0
SemEval-2015 Task 17: Taxonomy Extraction Evaluation (TExEval)—0
TATO: Leveraging on Multiple Strategies for Semantic Textual Similarity—0
Fusion of Compositional Network-based and Lexical Function Distributional Semantic Models—0
ECNU: Using Traditional Similarity Measurements and Word Embedding for Semantic Textual Similarity Estimation—0
Resolving Discourse-Deictic Pronouns: A Two-Stage Approach to Do It—0
Classification and Acquisition of Contradictory Event Pairs using Crowdsourcing—0
CICBUAPnlp: Graph-Based Approach for Answer Selection in Community Question Answering Task—0
A Word-Embedding-based Sense Index for Regular Polysemy Representation—0
Multi-Level Alignments As An Extensible Representation Basis for Textual Entailment Algorithms—0
DLS@CU: Sentence Similarity from Word Alignment and Semantic Vector Composition—0
JAIST: Combining multiple features for Answer Selection in Community Question Answering—0
ASAP-II: From the Alignment of Phrases to Textual Similarity—0
FBK-HLT: An Effective System for Paraphrase Identification and Semantic Similarity in Twitter—0
Representing Meaning with a Combination of Logical and Distributional ModelsCode0
An Optimal Quadratic Approach to Monolingual Paraphrase Alignment—0
Lexical Event Ordering with an Edge-Factored Model—0
Exploring Lexical, Syntactic, and Semantic Features for Chinese Textual Entailment in NTCIR RITE Evaluation Tasks—0
Efficiency in Ambiguity: Two Models of Probabilistic Semantics for Natural Language—0
On the Proper Treatment of Quantifiers in Probabilistic Logic Semantics—0
Book Reviews: Recognizing Textual Entailment: Models and Applications by Ido Dagan, Dan Roth, Mark Sammons and Fabio Massimo Zanzotto—0
Reasoning about Quantities in Natural Language—0
Encoding Generalized Quantifiers in Dependency-based Compositional Semantics—0
Lexical Substitution for the Medical Domain—0
Exploiting Timegraphs in Temporal Relation Classification—0
Learning Compact Lexicons for CCG Semantic Parsing—0
Taxonomy Construction Using Syntactic Contextual Evidence—0
SemEval-2014 Task 1: Evaluation of Compositional Distributional Semantic Models on Full Sentences through Semantic Relatedness and Textual Entailment—0
AI-KU: Using Co-Occurrence Modeling for Semantic Similarity—0
Inclusive yet Selective: Supervised Distributional Hypernymy Detection—0
UoW: NLP techniques developed at the University of Wolverhampton for Semantic Similarity and Textual EntailmentCode0
Unsupervised Verb Inference from Nouns Crossing Root Boundary—0
The Meaning Factory: Formal Semantics for Recognizing Textual Entailment and Determining Semantic Similarity—0
Argument structure of adverbial derivatives in Russian—0
SemEval-2014 Task 10: Multilingual Semantic Textual Similarity—0
Proposition Knowledge Graphs—0
ASAP: Automatic Semantic Alignment for Phrases—0
Illinois-LH: A Denotational and Distributional Approach to Semantics—0
Text Summarization through Entailment-based Minimum Vertex Cover—0
Compositional Distributional Semantics Models in Chunk-based Smoothed Tree Kernels—0
Towards Syntax-aware Compositional Distributional Semantic Models—0
Focus Annotation in Reading Comprehension Data—0
Exploring the use of word embeddings and random walks on Wikipedia for the CogAlex shared task—0
Exploring Fine-grained Entity Type Constraints for Distantly Supervised Relation Extraction—0
An analysis of textual inference in German customer emails—0
FBK-TR: SVM for Semantic Relatedeness and Corpus Patterns for RTE—0
haLF: Comparing a Pure CDSM Approach with a Standard Machine Learning System for RTE—0
UIO-Lien: Entailment Recognition using Minimal Recursion Semantics—0
BUAP: Evaluating Compositional Distributional Semantic Models on Full Sentences through Semantic Relatedness and Textual Entailment—0
Dead parrots make bad pets: Exploring modifier effects in noun phrases—0
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All datasetsSNLIRTEMultiNLIQNLIANLI testWNLILiDiRusRCBTERRaCommitmentBankSciTailFarsTail

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