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 1501–1550 of 1961 papers

TitleStatusHype
Domain Adaptation for Disease Phrase Matching with Adversarial Networks—0
Do Prompt-Based Models Really Understand the Meaning of Their Prompts?—0
Drafting Event Schemas using Language Models—0
DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference—0
DReCa: A General Task Augmentation Strategy for Few-Shot Natural Language Inference—0
DRONE: Data-aware Low-rank Compression for Large NLP Models—0
Dropping Networks for Transfer Learning—0
Dr.Quad at MEDIQA 2019: Towards Textual Inference and Question Entailment using contextualized representations—0
Dynamic Integration of Background Knowledge in Neural NLU Systems—0
Dynamic Neural Turing Machine with Soft and Hard Addressing Schemes—0
ECNU at SemEval-2016 Task 1: Leveraging Word Embedding From Macro and Micro Views to Boost Performance for Semantic Textual Similarity—0
ECNUCS: Recognizing Cross-lingual Textual Entailment Using Multiple Text Similarity and Text Difference Measures—0
ECNU: Leveraging Word Embeddings to Boost Performance for Paraphrase in Twitter—0
ECNU: One Stone Two Birds: Ensemble of Heterogenous Measures for Semantic Relatedness and Textual Entailment—0
ECNU: Using Traditional Similarity Measurements and Word Embedding for Semantic Textual Similarity Estimation—0
Effective Context Selection in LLM-based Leaderboard Generation: An Empirical Study—0
Efficiency in Ambiguity: Two Models of Probabilistic Semantics for Natural Language—0
Efficient Logical Inference for Semantic Processing—0
Efficient Nearest Neighbor based Uncertainty Estimation for Natural Language Processing Tasks—0
Efficient Search for Transformation-based Inference—0
Efficient Tree-based Approximation for Entailment Graph Learning—0
EHU-ALM: Similarity-Feature Based Approach for Student Response Analysis—0
Element-wise Bilinear Interaction for Sentence Matching—0
Eliminating Reasoning via Inferring with Planning: A New Framework to Guide LLMs' Non-linear Thinking—0
Embedding WordNet Knowledge for Textual Entailment—0
Emergent: a novel data-set for stance classification—0
Empathy and Distress Prediction using Transformer Multi-output Regression and Emotion Analysis with an Ensemble of Supervised and Zero-Shot Learning Models—0
Empirical Methods for the Study of Denotation in Nominalizations in Spanish—0
Emulating the Human Mind: A Neural-symbolic Link Prediction Model with Fast and Slow Reasoning and Filtered Rules—0
Enabling Transitivity for Lexical Inference on Chinese Verbs Using Probabilistic Soft Logic—0
Enacting textual entailment and ontologies for automated essay grading in chemical domain—0
Encoding Generalized Quantifiers in Dependency-based Compositional Semantics—0
English-Basque Statistical and Neural Machine Translation—0
English Prompts are Better for NLI-based Zero-Shot Emotion Classification than Target-Language Prompts—0
English Resource Semantics—0
Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference—0
Enhancing SLM via ChatGPT and Dataset Augmentation—0
Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic—0
ENIAM: Categorial Syntactic-Semantic Parser for Polish—0
EntailE: Introducing Textual Entailment in Commonsense Knowledge Graph Completion—0
蘊涵句型分析於改進中文文字蘊涵識別系統 (Entailment Analysis for Improving Chinese Recognizing Textual Entailment System) [In Chinese]—0
Entailment-based Text Exploration with Application to the Health-care Domain—0
Entailment Graph Learning with Textual Entailment and Soft Transitivity—0
Entailment Relation Aware Paraphrase Generation—0
Entangled Relations: Leveraging NLI and Meta-analysis to Enhance Biomedical Relation Extraction—0
EoANN: Lexical Semantic Relation Classification Using an Ensemble of Artificial Neural Networks—0
EPA: Easy Prompt Augmentation on Large Language Models via Multiple Sources and Multiple Targets—0
ERNIE-NLI: Analyzing the Impact of Domain-Specific External Knowledge on Enhanced Representations for NLI—0
ESCRITO - An NLP-Enhanced Educational Scoring Toolkit—0
ESNLIR: A Spanish Multi-Genre Dataset with Causal Relationships—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