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 1501–1550 of 1961 papers

TitleStatusHype
Recognizing Textual Entailment in Twitter Using Word Embeddings—0
Identifying Semantic Edit Intentions from Revisions in Wikipedia—0
LCT-MALTA's Submission to RepEval 2017 Shared Task—0
Detecting Metaphorical Phrases in the Polish Language—0
TAG Parser Evaluation using Textual Entailments—0
Neural Paraphrase Generation using Transfer Learning—0
Visual Denotations for Recognizing Textual Entailment—0
Applying Deep Neural Network to Retrieve Relevant Civil Law Articles—0
deepCybErNet at EmoInt-2017: Deep Emotion Intensities in Tweets—0
Towards Improving Abstractive Summarization via Entailment Generation—0
A Hybrid System to apply Natural Language Inference over Dependency Trees—0
Using English Dictionaries to generate Commonsense Knowledge in Natural Language—0
Inter-Weighted Alignment Network for Sentence Pair Modeling—0
Evaluating Hierarchies of Verb Argument Structure with Hierarchical Clustering—0
Graph-Based Approach to Recognizing CST Relations in Polish Texts—0
UDLex: Towards Cross-language Subcategorization Lexicons—0
Shortcut-Stacked Sentence Encoders for Multi-Domain InferenceCode0
Recurrent Neural Network-Based Sentence Encoder with Gated Attention for Natural Language InferenceCode0
Generating Pattern-Based Entailment Graphs for Relation Extraction—0
SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation—0
MITRE at SemEval-2017 Task 1: Simple Semantic Similarity—0
Prediction of Frame-to-Frame Relations in the FrameNet Hierarchy with Frame Embeddings—0
Detecting Cross-Lingual Semantic Divergence for Neural Machine Translation—0
Acquiring Predicate Paraphrases from News Tweets—0
The Event StoryLine Corpus: A New Benchmark for Causal and Temporal Relation Extraction—0
Detecting Asymmetric Semantic Relations in Context: A Case-Study on Hypernymy Detection—0
STS-UHH at SemEval-2017 Task 1: Scoring Semantic Textual Similarity Using Supervised and Unsupervised Ensemble—0
The RepEval 2017 Shared Task: Multi-Genre Natural Language Inference with Sentence Representations—0
Character-level Intra Attention Network for Natural Language InferenceCode0
Refining Raw Sentence Representations for Textual Entailment Recognition via AttentionCode0
Learning to Compose Task-Specific Tree StructuresCode0
Evaluating Compound Splitters Extrinsically with Textual Entailment—0
RelTextRank: An Open Source Framework for Building Relational Syntactic-Semantic Text Pair Representations—0
TextFlow: A Text Similarity Measure based on Continuous Sequences—0
An Empirical Study on End-to-End Sentence Modelling—0
A Monotonicity Calculus and Its Completeness—0
Dynamic Integration of Background Knowledge in Neural NLU Systems—0
Learning to Compute Word Embeddings On the Fly—0
Max-Cosine Matching Based Neural Models for Recognizing Textual Entailment—0
Jointly Learning Sentence Embeddings and Syntax with Unsupervised Tree-LSTMs—0
Second-Order Word Embeddings from Nearest Neighbor Topological FeaturesCode0
A Regularized Framework for Sparse and Structured Neural AttentionCode0
Baselines and test data for cross-lingual inferenceCode0
Learning to Predict Denotational Probabilities For Modeling Entailment—0
Arabic Textual Entailment with Word Embeddings—0
How Well Can We Predict Hypernyms from Word Embeddings? A Dataset-Centric Analysis—0
Annotating omission in statement pairsCode0
Integer Linear Programming formulations in Natural Language Processing—0
Modelling metaphor with attribute-based semantics—0
Social Bias in Elicited Natural Language InferencesCode0
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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