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 751–800 of 1961 papers

TitleStatusHype
Paraphrasing, textual entailment, and semantic similarity above word level—0
Improving Distantly Supervised Relation Extraction by Natural Language Inference—0
Smoothing Entailment Graphs with Language ModelsCode0
Code Comment Inconsistency Detection with BERT and LongformerCode0
Language models show human-like content effects on reasoning tasksCode0
Exploiting Word Semantics to Enrich Character Representations of Chinese Pre-trained ModelsCode0
Predicting Out-of-Domain Generalization with Neighborhood Invariance—0
Learning to Generate Examples for Semantic Processing Tasks—0
AnaLog: Testing Analytical and Deductive Logic Learnability in Language Models—0
An Understanding-Oriented Robust Machine Reading Comprehension ModelCode0
Is “My Favorite New Movie” My Favorite Movie? Probing the Understanding of Recursive Noun Phrases—0
Overconfidence in the Face of Ambiguity with Adversarial DataCode0
Improving Distantly Supervised Document-Level Relation Extraction Through Natural Language Inference—0
Bilingual Tabular Inference: A Case Study on Indic Languages—0
Uncertainty-Aware Cross-Lingual Transfer with Pseudo Partial LabelsCode0
Adversarial Self-Attention for Language UnderstandingCode0
Template-based Approach to Zero-shot Intent Recognition—0
RuArg-2022: Argument Mining Evaluation—0
DialogueScript: Using Dialogue Agents to Produce a Script—0
BaIT: Barometer for Information Trustworthiness—0
Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems—0
Adversarial Text Normalization—0
Analyzing the Effects of Annotator Gender across NLP TasksCode0
Mitigating Dataset Artifacts in Natural Language Inference Through Automatic Contextual Data Augmentation and Learning Optimization—0
The Chinese Causative-Passive Homonymy Disambiguation: an adversarial Dataset for NLI and a Probing Task—0
‘Am I the Bad One’? Predicting the Moral Judgement of the Crowd Using Pre–trained Language Models—0
Fine-grained Entailment: Resources for Greek NLI and Precise EntailmentCode0
Filtrage et régularisation pour améliorer la plausibilité des poids d’attention dans la tâche d’inférence en langue naturelle (Filtering and regularization to improve the plausibility of attention weights in NLI)—0
Sentence Pair Embeddings Based Evaluation Metric for Abstractive and Extractive Summarization—0
A Deep Transfer Learning Method for Cross-Lingual Natural Language Inference—0
A Multi-level Supervised Contrastive Learning Framework for Low-Resource Natural Language Inference—0
ORCA: Interpreting Prompted Language Models via Locating Supporting Data Evidence in the Ocean of Pretraining Data—0
Less Learn Shortcut: Analyzing and Mitigating Learning of Spurious Feature-Label CorrelationCode0
On Advances in Text Generation from Images Beyond Captioning: A Case Study in Self-Rationalization—0
Policy Compliance Detection via Expression Tree Inference—0
A Question-Answer Driven Approach to Reveal Affirmative Interpretations from Verbal NegationsCode0
Penguins Don't Fly: Reasoning about Generics through Instantiations and Exceptions—0
Logical Reasoning with Span-Level Predictions for Interpretable and Robust NLI ModelsCode0
Few-Shot Natural Language Inference Generation with PDD: Prompt and Dynamic Demonstration—0
Nebula-I: A General Framework for Collaboratively Training Deep Learning Models on Low-Bandwidth Cloud Clusters—0
Persian Natural Language Inference: A Meta-learning approachCode0
Towards Debiasing Translation ArtifactsCode0
Falsesum: Generating Document-level NLI Examples for Recognizing Factual Inconsistency in SummarizationCode0
Lifting the Curse of Multilinguality by Pre-training Modular Transformers—0
Natural Language Inference with Self-Attention for Veracity Assessment of Pandemic Claims—0
Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learning—0
Neural Language Taskonomy: Which NLP Tasks are the most Predictive of fMRI Brain Activity?—0
Semantic Diversity in Dialogue with Natural Language Inference—0
Deep Neural Representations for Multiword Expressions DetectionCode0
Uncovering Values: Detecting Latent Moral Content from Natural Language with Explainable and Non-Trained MethodsCode0
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All datasetsSNLIRTEMultiNLIQNLIANLI testWNLILiDiRusRCBTERRaCommitmentBankSciTailFarsTail

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UnitedSynT5 (3B)% Test Accuracy94.7—Unverified
2UnitedSynT5 (335M)% Test Accuracy93.5—Unverified
3Neural Tree Indexers for Text Understanding% Test Accuracy93.1—Unverified
4EFL (Entailment as Few-shot Learner) + RoBERTa-large% 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 11B (fine-tuned)Accuracy92.5—Unverified
9T5-XXL 11BAccuracy92.5—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
8Adv-RoBERTa ensembleMatched91.1—Unverified
9DeBERTa (large)Matched91.1—Unverified
10SMARTRoBERTaDev Matched91.1—Unverified