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
Knowledge Graphs for Multilingual Language Translation and Generation—0
Towards Improving Selective Prediction Ability of NLP Systems—0
Ranking Clarification Questions via Natural Language Inference—0
Logical Semantics, Dialogical Argumentation, and Textual Entailment—0
On Learning Universal Representations Across Languages—0
A Study of fastText Word Embedding Effects in Document Classification in Bangla LanguageCode0
Improving Results on Russian Sentiment DatasetsCode0
Mono vs Multilingual Transformer-based Models: a Comparison across Several Language TasksCode0
Logic, Language, and Calculus—0
GLUECoS: An Evaluation Benchmark for Code-Switched NLP—0
Would you Rather? A New Benchmark for Learning Machine Alignment with Cultural Values and Social Preferences—0
``Who said it, and Why?'' Provenance for Natural Language Claims—0
Similarity and Farness Based Bidirectional Neural Co-Attention for Amharic Natural Language Inference—0
Joint Training with Semantic Role Labeling for Better Generalization in Natural Language Inference—0
Answer Ranking for Product-Related Questions via Multiple Semantic Relations Modeling—0
SqueezeBERT: What can computer vision teach NLP about efficient neural networks?Code0
Automatic Validation of Textual Attribute Values in E-commerce Catalog by Learning with Limited Labeled Data—0
How does Punctuation Affect Neural Models in Natural Language Inference—0
Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models—0
Training Multilingual Machine Translation by Alternately Freezing Language-Specific Encoders-Decoders—0
Pretraining with Contrastive Sentence Objectives Improves Discourse Performance of Language Models—0
Towards Question Format Independent Numerical Reasoning: A Set of Prerequisite Tasks—0
CS-NLP team at SemEval-2020 Task 4: Evaluation of State-of-the-art NLP Deep Learning Architectures on Commonsense Reasoning Task—0
Towards Robustifying NLI Models Against Lexical Dataset BiasesCode0
Probing Linguistic SystematicityCode0
Probing the Natural Language Inference Task with Automated Reasoning ToolsCode0
Improving Truthfulness of Headline GenerationCode0
Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance?—0
Towards the Necessity for Debiasing Natural Language Inference Datasets—0
AIA-BDE: A Corpus of FAQs in Portuguese and their Variations—0
Japanese Realistic Textual Entailment Corpus—0
Mandarinograd: A Chinese Collection of Winograd Schemas—0
SAPPHIRE: Simple Aligner for Phrasal Paraphrase with Hierarchical Representation—0
Discourse Component to Sentence (DC2S): An Efficient Human-Aided Construction of Paraphrase and Sentence Similarity Dataset—0
Decomposing and Comparing Meaning Relations: Paraphrasing, Textual Entailment, Contradiction, and Specificity—0
Improving the Precision of Natural Textual Entailment Problem Datasets—0
Towards Verified Robustness under Text Deletion Interventions—0
Neural Natural Language Inference Models Partially Embed Theories of Lexical Entailment and NegationCode0
BURT: BERT-inspired Universal Representation from Twin Structure—0
Elastic weight consolidation for better bias inoculationCode0
The Curse of Performance Instability in Analysis Datasets: Consequences, Source, and SuggestionsCode0
Semantics-Aware Inferential Network for Natural Language Understanding—0
UXLA: A Robust Unsupervised Data Augmentation Framework for Zero-Resource Cross-Lingual NLP—0
GLUECoS : An Evaluation Benchmark for Code-Switched NLP—0
Adversarial Training for Large Neural Language Models—0
Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision—0
Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial TrainingCode0
A New Dataset for Natural Language Inference from Code-mixed Conversations—0
Translation Artifacts in Cross-lingual Transfer LearningCode0
Locality Preserving Loss: Neighbors that Live together, Align together—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