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 1301–1350 of 1961 papers

TitleStatusHype
How Fast can BERT Learn Simple Natural Language Inference?—0
How often are errors in natural language reasoning due to paraphrastic variability?—0
How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Tasks—0
How Well Can We Predict Hypernyms from Word Embeddings? A Dataset-Centric Analysis—0
How well do NLI models capture verb veridicality?—0
Hunting for Entailing Pairs in the Penn Discourse Treebank—0
Hybrid Emoji-Based Masked Language Models for Zero-Shot Abusive Language Detection—0
Hybrid Neural Attention for Agreement/Disagreement Inference in Online Debates—0
Hyperbolic sentence representations for solving Textual Entailment—0
HypoNLI: Exploring the Artificial Patterns of Hypothesis-only Bias in Natural Language Inference—0
Hypothesis-only Biases in Large Language Model-Elicited Natural Language Inference—0
IBM MNLP IE at CASE 2021 Task 2: NLI Reranking for Zero-Shot Text Classification—0
ICT: A Translation based Method for Cross-lingual Textual Entailment—0
Identification of Entailment and Contradiction Relations between Natural Language Sentences: A Neurosymbolic Approach—0
Identifying Constant and Unique Relations by using Time-Series Text—0
Identifying Factual Inconsistencies in Summaries: Grounding LLM Inference via Task Taxonomy—0
Identifying hypernyms in distributional semantic spaces—0
Identifying science concepts and student misconceptions in an interactive essay writing tutor—0
Identifying Semantic Edit Intentions from Revisions in Wikipedia—0
Identifying Untyped Relation Mentions in a Corpus given an Ontology—0
I do not disagree: leveraging monolingual alignment to detect disagreement in dialogue—0
IFlyLegal: A Chinese Legal System for Consultation, Law Searching, and Document Analysis—0
IITP-AI-NLP-ML@ CL-SciSumm 2020, CL-LaySumm 2020, LongSumm 2020—0
IITP at MEDIQA 2019: Systems Report for Natural Language Inference, Question Entailment and Question Answering—0
iKernels-Core: Tree Kernel Learning for Textual Similarity—0
Illinois-LH: A Denotational and Distributional Approach to Semantics—0
Illustrative Language Understanding: Large-Scale Visual Grounding with Image Search—0
"I'm Not Mad": Commonsense Implications of Negation and Contradiction—0
``I'm Not Mad'': Commonsense Implications of Negation and Contradiction—0
ImPaKT: A Dataset for Open-Schema Knowledge Base Construction—0
IMPLI: Investigating NLI Models' Performance on Figurative Language—0
IMPLI: Investigatng NLI Models' Performance on Figurative Language—0
Improved Beam Search for Hallucination Mitigation in Abstractive Summarization—0
Improved CCG Parsing with Semi-supervised Supertagging—0
Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting—0
Improved Representation Learning for Question Answer Matching—0
Improved Sentence Modeling using Suffix Bidirectional LSTM—0
Improving Composition of Sentence Embeddings through the Lens of Statistical Relational Learning—0
Improving Distantly Supervised Document-Level Relation Extraction Through Natural Language Inference—0
Improving Distantly Supervised Relation Extraction by Natural Language Inference—0
Improving Domain-Specific Retrieval by NLI Fine-Tuning—0
Improving Generalization by Incorporating Coverage in Natural Language Inference—0
Improving Implicit Discourse Relation Recognition Through Feature Set Optimization—0
Improving Medical NLI Using Context-Aware Domain Knowledge—0
Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding—0
Improving Natural Language Inference Using External Knowledge in the Science Questions Domain—0
Distilling Robustness into Natural Language Inference Models with Domain-Targeted Augmentation—0
Improving the Natural Language Inference robustness to hard dataset by data augmentation and preprocessing—0
Improving the Out-Of-Distribution Generalization Capability of Language Models: Counterfactually-Augmented Data is not Enough—0
Improving the Precision of Natural Textual Entailment Problem Datasets—0
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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