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 1051–1100 of 1961 papers

TitleStatusHype
Towards Linguistically Informed Multi-Objective Pre-Training for Natural Language Inference—0
Towards Question Format Independent Numerical Reasoning: A Set of Prerequisite Tasks—0
Towards Syntax-aware Compositional Distributional Semantic Models—0
Towards the Necessity for Debiasing Natural Language Inference Datasets—0
Towards the Unsupervised Acquisition of Implicit Semantic Roles—0
Towards Verified Robustness under Text Deletion Interventions—0
Toward the automatic extraction of knowledge of usable goods—0
To what extent do human explanations of model behavior align with actual model behavior?—0
To What Extent Do Natural Language Understanding Datasets Correlate to Logical Reasoning? A Method for Diagnosing Logical Reasoning.—0
Training a Korean SRL System with Rich Morphological Features—0
Training Multilingual Machine Translation by Alternately Freezing Language-Specific Encoders-Decoders—0
TRANS-BLSTM: Transformer with Bidirectional LSTM for Language Understanding—0
Transducing Sentences to Syntactic Feature Vectors: an Alternative Way to ``Parse''?—0
Transformer-Based Contextualized Language Models Joint with Neural Networks for Natural Language Inference in Vietnamese—0
Transition-based Dependency DAG Parsing Using Dynamic Oracles—0
Trans-KBLSTM: An External Knowledge Enhanced Transformer BiLSTM Model for Tabular Reasoning—0
TrustDataFilter:Leveraging Trusted Knowledge Base Data for More Effective Filtering of Unknown Information—0
Tuning HeidelTime for identifying time expressions in clinical texts in English and French—0
UAlacant: Using Online Machine Translation for Cross-Lingual Textual Entailment—0
UCCA: A Semantics-based Grammatical Annotation Scheme—0
UCL Machine Reading Group: Four Factor Framework For Fact Finding (HexaF)—0
UDLex: Towards Cross-language Subcategorization Lexicons—0
UIO-Lien: Entailment Recognition using Minimal Recursion Semantics—0
UKP-BIU: Similarity and Entailment Metrics for Student Response Analysis—0
UKP: Computing Semantic Textual Similarity by Combining Multiple Content Similarity Measures—0
Ukrainian Texts Classification: Exploration of Cross-lingual Knowledge Transfer Approaches—0
UMCC\_DLSI: Multidimensional Lexical-Semantic Textual Similarity—0
UMCC\_DLSI: Textual Similarity based on Lexical-Semantic features—0
Umelb: Cross-lingual Textual Entailment with Word Alignment and String Similarity Features—0
Uncertain Natural Language Inference—0
Uncovering More Shallow Heuristics: Probing the Natural Language Inference Capacities of Transformer-Based Pre-Trained Language Models Using Syllogistic Patterns—0
Understanding and Predicting Human Label Variation in Natural Language Inference through Explanation—0
Understanding Deep Learning Performance through an Examination of Test Set Difficulty: A Psychometric Case Study—0
Understanding Roles and Entities: Datasets and Models for Natural Language Inference—0
Understanding tables with intermediate pre-training—0
Undivided Attention: Are Intermediate Layers Necessary for BERT?—0
Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks—0
UniMelb at SemEval-2016 Task 3: Identifying Similar Questions by combining a CNN with String Similarity Measures—0
Universal Multimodal Representation for Language Understanding—0
Universal Sentence Representation Learning with Conditional Masked Language Model—0
Universal Sentence Representations Learning with Conditional Masked Language Model—0
Universal Text Representation from BERT: An Empirical Study—0
University Entrance Examinations as a Benchmark Resource for NLP-based Problem Solving—0
University\_Of\_Sheffield: Two Approaches to Semantic Text Similarity—0
Unlock the Potential of Counterfactually-Augmented Data in Out-Of-Distribution Generalization—0
Unreasonable Effectiveness of Rule-Based Heuristics in Solving Russian SuperGLUE Tasks—0
Unsupervised Contextual Paraphrase Generation using Lexical Control and Reinforcement Learning—0
Unsupervised Detection of Downward-Entailing Operators By Maximizing Classification Certainty—0
Unsupervised Knowledge Graph Construction and Event-centric Knowledge Infusion for Scientific NLI—0
Unsupervised Learning of Prototypical Fillers for Implicit Semantic Role Labeling—0
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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