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 1451–1500 of 1961 papers

TitleStatusHype
Deep Learning for Natural Language Inference—0
Deep Neural Model Inspection and Comparison via Functional Neuron Pathways—0
DeepPurple: Estimating Sentence Semantic Similarity using N-gram Regression Models and Web Snippets—0
DeFTX: Denoised Sparse Fine-Tuning for Zero-Shot Cross-Lingual Transfer—0
DEIM: An effective deep encoding and interaction model for sentence matching—0
DeModify: A Dataset for Analyzing Contextual Constraints on Modifier Deletion—0
Dependency-Based Open Information Extraction—0
DerivBase.hr: A High-Coverage Derivational Morphology Resource for Croatian—0
DErivBase: Inducing and Evaluating a Derivational Morphology Resource for German—0
Dermacen Analytica: A Novel Methodology Integrating Multi-Modal Large Language Models with Machine Learning in tele-dermatology—0
Design and Realization of the EXCITEMENT Open Platform for Textual Entailment—0
Designing NLP Systems That Adapt to Diverse Worldviews—0
Detecting Asymmetric Semantic Relations in Context: A Case-Study on Hypernymy Detection—0
Detecting Bipolar Semantic Relations among Natural Language Arguments with Textual Entailment: a Study.—0
Detecting Contradictory COVID-19 Drug Efficacy Claims from Biomedical Literature—0
Detecting Cross-Lingual Semantic Divergence for Neural Machine Translation—0
Detecting Logical Relation In Contract Clauses—0
Detecting Metaphorical Phrases in the Polish Language—0
Detecting Negated and Uncertain Information in Biomedical and Review Texts—0
Detecting Semantic Equivalence and Information Disparity in Cross-lingual Documents—0
Detecting Stance in Tweets And Analyzing its Interaction with Sentiment—0
Detecting Subevent Structure for Event Coreference Resolution—0
Determining is-a relationships for Textual Entailment—0
Developing a Reliable, Fast, General-Purpose Hallucination Detection and Mitigation Service—0
DFKI-NLP at SemEval-2024 Task 2: Towards Robust LLMs Using Data Perturbations and MinMax Training—0
Dialogue Natural Language Inference—0
DialogueScore: Evaluating Responses in Task-Oriented Dialogue—0
DialogueScript: Using Dialogue Agents to Produce a Script—0
DialogueScript: Using Dialogue Agents to Produce a Script—0
DIFM:An effective deep interaction and fusion model for sentence matching—0
DirRelCond3: Detecting Textual Entailment Across Languages With Conditions On Directional Text Relatedness Scores—0
Discourse Component to Sentence (DC2S): An Efficient Human-Aided Construction of Paraphrase and Sentence Similarity Dataset—0
Discourse-Driven Evaluation: Unveiling Factual Inconsistency in Long Document Summarization—0
Discourse-sensitive Automatic Identification of Generic Expressions—0
Discovering Better Model Architectures for Medical Query Understanding—0
DISSECT - DIStributional SEmantics Composition Toolkit—0
Distance-based Self-Attention Network for Natural Language Inference—0
DISTRIBUTIONALLY ROBUST NEURAL NETWORKS FOR GROUP SHIFTS ON THE IMPORTANCE OF REGULARIZATION FOR WORST-CASE GENERALIZATION—0
Diversifying Knowledge Enhancement of Biomedical Language Models using Adapter Modules and Knowledge Graphs—0
Divide & Conquer for Entailment-aware Multi-hop Evidence Retrieval—0
DKE-Research at SemEval-2024 Task 2: Incorporating Data Augmentation with Generative Models and Biomedical Knowledge to Enhance Inference Robustness—0
DLS@CU at SemEval-2016 Task 1: Supervised Models of Sentence Similarity—0
DLS@CU: Sentence Similarity from Word Alignment and Semantic Vector Composition—0
Document Attribution: Examining Citation Relationships using Large Language Models—0
Document Level Novelty Detection: Textual Entailment Lends a Helping Hand—0
Document Retrieval and Claim Verification to Mitigate COVID-19 Misinformation—0
Do Current Natural Language Inference Models Truly Understand Sentences? Insights from Simple Sentences—0
Does Putting a Linguist in the Loop Improve NLU Data Collection?—0
Does Putting a Linguist in the Loop Improve NLU Data Collection?—0
Does Training on Synthetic Data Make Models Less Robust?—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