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 551–600 of 1961 papers

TitleStatusHype
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
Fine-grained Entailment: Resources for Greek NLI and Precise EntailmentCode0
Analyzing the Effects of Annotator Gender across NLP TasksCode0
Sentence Pair Embeddings Based Evaluation Metric for Abstractive and Extractive Summarization—0
Mitigating Dataset Artifacts in Natural Language Inference Through Automatic Contextual Data Augmentation and Learning Optimization—0
‘Am I the Bad One’? Predicting the Moral Judgement of the Crowd Using Pre–trained Language Models—0
A Deep Transfer Learning Method for Cross-Lingual Natural Language Inference—0
The Chinese Causative-Passive Homonymy Disambiguation: an adversarial Dataset for NLI and a Probing Task—0
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
A Multi-level Supervised Contrastive Learning Framework for Low-Resource Natural Language Inference—0
Less Learn Shortcut: Analyzing and Mitigating Learning of Spurious Feature-Label CorrelationCode0
ORCA: Interpreting Prompted Language Models via Locating Supporting Data Evidence in the Ocean of Pretraining Data—0
FLUTE: Figurative Language Understanding through Textual ExplanationsCode1
On Advances in Text Generation from Images Beyond Captioning: A Case Study in Self-Rationalization—0
Policy Compliance Detection via Expression Tree Inference—0
Penguins Don't Fly: Reasoning about Generics through Instantiations and Exceptions—0
A Question-Answer Driven Approach to Reveal Affirmative Interpretations from Verbal NegationsCode0
On Measuring Social Biases in Prompt-Based Multi-Task LearningCode1
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
Lifting the Curse of Multilinguality by Pre-training Modular Transformers—0
Falsesum: Generating Document-level NLI Examples for Recognizing Factual Inconsistency in SummarizationCode0
UL2: Unifying Language Learning ParadigmsCode1
The Unreliability of Explanations in Few-shot Prompting for Textual ReasoningCode1
Natural Language Inference with Self-Attention for Veracity Assessment of Pandemic Claims—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
Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learning—0
Deep Neural Representations for Multiword Expressions DetectionCode0
ClusterFormer: Neural Clustering Attention for Efficient and Effective Transformer—0
IMPLI: Investigating NLI Models’ Performance on Figurative LanguageCode1
Empathy and Distress Prediction using Transformer Multi-output Regression and Emotion Analysis with an Ensemble of Supervised and Zero-Shot Learning Models—0
ASCM: An Answer Space Clustered Prompting Method without Answer EngineeringCode0
Clustering Examples in Multi-Dataset Benchmarks with Item Response Theory—0
PARADISE”:" Exploiting Parallel Data for Multilingual Sequence-to-Sequence Pretraining—0
Uncovering Values: Detecting Latent Moral Content from Natural Language with Explainable and Non-Trained MethodsCode0
Capture Human Disagreement Distributions by Calibrated Networks for Natural Language Inference—0
Enhancing Cross-lingual Natural Language Inference by Prompt-learning from Cross-lingual TemplatesCode0
Document Retrieval and Claim Verification to Mitigate COVID-19 Misinformation—0
Trans-KBLSTM: An External Knowledge Enhanced Transformer BiLSTM Model for Tabular Reasoning—0
XInfoTabS: Evaluating Multilingual Tabular Natural Language Inference—0
To be or not to be an Integer? Encoding Variables for Mathematical Text—0
Solution of DeBERTaV3 on CommonsenseQACode0
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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