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 851–900 of 1961 papers

TitleStatusHype
Academic Case Reports Lack Diversity: Assessing the Presence and Diversity of Sociodemographic and Behavioral Factors related to Post COVID-19 Condition—0
HILDIF: Interactive Debugging of NLI Models Using Influence Functions—0
HindiLLM: Large Language Model for Hindi—0
How does Punctuation Affect Neural Models in Natural Language Inference—0
Effective Context Selection in LLM-based Leaderboard Generation: An Empirical Study—0
BreakingBERT@IITK at SemEval-2021 Task 9: Statement Verification and Evidence Finding with Tables—0
How do Negation and Modality Impact on Opinions?—0
How Entangled is Factuality and Deception in German?—0
Instruction Finetuning for Leaderboard Generation from Empirical AI Research—0
Semi-supervised Visual Feature Integration for Pre-trained Language Models—0
Interactive Model with Structural Loss for Language-based Abductive Reasoning—0
How often are errors in natural language reasoning due to paraphrastic variability?—0
InferLite: Simple Universal Sentence Representations from Natural Language Inference Data—0
Inducing Example-based Semantic Frames from a Massive Amount of Verb Uses—0
Annotating Factive Verbs—0
How Well Can We Predict Hypernyms from Word Embeddings? A Dataset-Centric Analysis—0
How well do NLI models capture verb veridicality?—0
Cross-Lingual Ability of Multilingual BERT: An Empirical Study—0
Inference is Everything: Recasting Semantic Resources into a Unified Evaluation Framework—0
Hunting for Entailing Pairs in the Penn Discourse Treebank—0
Cross-lingual Adaption Model-Agnostic Meta-Learning for Natural Language Understanding—0
Hybrid Emoji-Based Masked Language Models for Zero-Shot Abusive Language Detection—0
Infusing Knowledge into the Textual Entailment Task Using Graph Convolutional Networks—0
Crosslingual Induction of Semantic Roles—0
ECNU: Using Traditional Similarity Measurements and Word Embedding for Semantic Textual Similarity Estimation—0
Attention Mechanism, Transformers, BERT, and GPT: Tutorial and Survey—0
Hyperbolic sentence representations for solving Textual Entailment—0
HypoNLI: Exploring the Artificial Patterns of Hypothesis-only Bias in Natural Language Inference—0
ECNU: One Stone Two Birds: Ensemble of Heterogenous Measures for Semantic Relatedness and Textual Entailment—0
Boosting Neural Language Inference via Cascaded Interactive Reasoning—0
Hypothesis-only Biases in Large Language Model-Elicited Natural Language Inference—0
ECNU: Leveraging Word Embeddings to Boost Performance for Paraphrase in Twitter—0
ECNUCS: Recognizing Cross-lingual Textual Entailment Using Multiple Text Similarity and Text Difference Measures—0
ICT: A Translation based Method for Cross-lingual Textual Entailment—0
Annotating Entailment Relations for Shortanswer Questions—0
Identification of Entailment and Contradiction Relations between Natural Language Sentences: A Neurosymbolic Approach—0
A Hybrid Neural Network Model for Commonsense Reasoning—0
Identifying Factual Inconsistencies in Summaries: Grounding LLM Inference via Task Taxonomy—0
Inherent Disagreements in Human Textual Inferences—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
Cross-Policy Compliance Detection via Question Answering—0
ECNU at SemEval-2016 Task 1: Leveraging Word Embedding From Macro and Micro Views to Boost Performance for Semantic Textual Similarity—0
Book Reviews: Recognizing Textual Entailment: Models and Applications by Ido Dagan, Dan Roth, Mark Sammons and Fabio Massimo Zanzotto—0
Dynamic Neural Turing Machine with Soft and Hard Addressing Schemes—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
Dynamic Integration of Background Knowledge in Neural NLU Systems—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