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 1401–1450 of 1961 papers

TitleStatusHype
CoMeT: Integrating different levels of linguistic modeling for meaning assessment—0
Comparative Analysis of Contextual Relation Extraction based on Deep Learning Models—0
Compare, Compress and Propagate: Enhancing Neural Architectures with Alignment Factorization for Natural Language Inference—0
Comparing Hallucination Detection Metrics for Multilingual Generation—0
Comparing two acquisition systems for automatically building an English---Croatian parallel corpus from multilingual websites—0
Comparison and Combination of Sentence Embeddings Derived from Different Supervision Signals—0
Compliance Checking with NLI: Privacy Policies vs. Regulations—0
Compositional De-Attention Networks—0
Compositional Distributional Models of Meaning—0
Compositional Distributional Semantics Models in Chunk-based Smoothed Tree Kernels—0
Compositional Phrase Alignment and Beyond—0
Composition of Sentence Embeddings: Lessons from Statistical Relational Learning—0
Composition of Sentence Embeddings:Lessons from Statistical Relational Learning—0
Compressing Sentence Representation with maximum Coding Rate Reduction—0
Concept Extensions as the Basis for Vector-Space Semantics: Combining Distributional and Ontological Information about Entities—0
Conformal Predictor for Improving Zero-shot Text Classification Efficiency—0
Connecting Supervised and Unsupervised Sentence Embeddings—0
Consistent CCG Parsing over Multiple Sentences for Improved Logical Reasoning—0
Consolidating and Developing Benchmarking Datasets for the Nepali Natural Language Understanding Tasks—0
Constructing a Lexicon of Relational Nouns—0
Constructing a Question Corpus for Textual Semantic Relations—0
Context-Sensitive Inference Rule Discovery: A Graph-Based Method—0
Context-theoretic Semantics for Natural Language: an Algebraic Framework—0
Contradiction Detection for Rumorous Claims—0
Contrastive Policy Gradient: Aligning LLMs on sequence-level scores in a supervised-friendly fashion—0
Convolutional Interaction Network for Natural Language Inference—0
Corpora for Learning the Mutual Relationship between Semantic Relatedness and Textual Entailment—0
Corpus Annotation through Crowdsourcing: Towards Best Practice Guidelines—0
Corpus annotation with a linguistic analysis of the associations between event mentions and spatial expressions—0
Cross-Document Cross-Lingual NLI via RST-Enhanced Graph Fusion and Interpretability Prediction—0
Cross-Lingual Ability of Multilingual BERT: An Empirical Study—0
Cross-Lingual Ability of Multilingual Masked Language Models: A Study of Language Structure—0
Cross-lingual Adaption Model-Agnostic Meta-Learning for Natural Language Understanding—0
Crosslingual Induction of Semantic Roles—0
Cross-Lingual Transfer for Natural Language Inference via Multilingual Prompt Translator—0
Cross-lingual Transfer of Semantic Role Labeling Models—0
Cross-lingual Transfer or Machine Translation? On Data Augmentation for Monolingual Semantic Textual Similarity—0
Cross-Lingual Transfer with MAML on Trees—0
Cross-Policy Compliance Detection via Question Answering—0
Crowd-Calibrator: Can Annotator Disagreement Inform Calibration in Subjective Tasks?—0
Crowdsourcing Complex Language Resources: Playing to Annotate Dependency Syntax—0
Crowdsourcing Inference-Rule Evaluation—0
CS-NLP team at SemEval-2020 Task 4: Evaluation of State-of-the-art NLP Deep Learning Architectures on Commonsense Reasoning Task—0
CSReader at SemEval-2018 Task 11: Multiple Choice Question Answering as Textual Entailment—0
Curriculum Discovery through an Encompassing Curriculum Learning Framework—0
Data Augmentation with Adversarial Training for Cross-Lingual NLI—0
Data-aware Low-Rank Compression for Large NLP Models—0
Dead parrots make bad pets: Exploring modifier effects in noun phrases—0
Decomposing and Comparing Meaning Relations: Paraphrasing, Textual Entailment, Contradiction, and Specificity—0
deepCybErNet at EmoInt-2017: Deep Emotion Intensities in Tweets—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