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 1001–1050 of 1961 papers

TitleStatusHype
Universal Natural Language Processing with Limited Annotations: Try Few-shot Textual Entailment as a StartCode1
InfoBERT: Improving Robustness of Language Models from An Information Theoretic PerspectiveCode1
Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous SpaceCode0
Reading Comprehension as Natural Language Inference: A Semantic Analysis—0
Mining Knowledge for Natural Language Inference from Wikipedia CategoriesCode1
Understanding tables with intermediate pre-training—0
Case-Based Abductive Natural Language Inference—0
TaxiNLI: Taking a Ride up the NLU HillCode1
Context-theoretic Semantics for Natural Language: an Algebraic Framework—0
Learning to Attack: Towards Textual Adversarial Attacking in Real-world Situations—0
Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters & Less DataCode1
Looking Beyond Sentence-Level Natural Language Inference for Downstream Tasks—0
FarsTail: A Persian Natural Language Inference DatasetCode1
Generating Label Cohesive and Well-Formed Adversarial ClaimsCode1
Knowledge Graphs for Multilingual Language Translation and Generation—0
GREEK-BERT: The Greeks visiting Sesame StreetCode1
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
Big Bird: Transformers for Longer SequencesCode1
Mono vs Multilingual Transformer-based Models: a Comparison across Several Language TasksCode0
An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language ModelsCode1
Logic, Language, and Calculus—0
Transferability of Natural Language Inference to Biomedical Question AnsweringCode1
Joint Training with Semantic Role Labeling for Better Generalization in Natural Language Inference—0
Similarity and Farness Based Bidirectional Neural Co-Attention for Amharic Natural Language Inference—0
``Who said it, and Why?'' Provenance for Natural Language Claims—0
Would you Rather? A New Benchmark for Learning Machine Alignment with Cultural Values and Social Preferences—0
GLUECoS: An Evaluation Benchmark for Code-Switched NLP—0
Towards Holistic and Automatic Evaluation of Open-Domain Dialogue GenerationCode1
Answer Ranking for Product-Related Questions via Multiple Semantic Relations Modeling—0
Compositional Explanations of NeuronsCode1
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
DeBERTa: Decoding-enhanced BERT with Disentangled AttentionCode2
M3P: Learning Universal Representations via Multitask Multilingual Multimodal Pre-trainingCode1
How does Punctuation Affect Neural Models in Natural Language Inference—0
Training Multilingual Machine Translation by Alternately Freezing Language-Specific Encoders-Decoders—0
Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models—0
Language Models are Few-Shot LearnersCode3
NILE : Natural Language Inference with Faithful Natural Language ExplanationsCode1
L2R2: Leveraging Ranking for Abductive ReasoningCode1
Stance Prediction and Claim Verification: An Arabic PerspectiveCode1
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
Logical Inferences with Comparatives and Generalized QuantifiersCode1
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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