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 1551–1600 of 1961 papers

TitleStatusHype
On-demand Injection of Lexical Knowledge for Recognising Textual Entailment—0
Soft Label Memorization-Generalization for Natural Language Inference—0
Understanding Deep Learning Performance through an Examination of Test Set Difficulty: A Psychometric Case Study—0
Bilateral Multi-Perspective Matching for Natural Language SentencesCode0
Structured Attention Networks—0
Task-Specific Attentive Pooling of Phrase Alignments Contributes to Sentence Matching—0
Textual Entailment with Structured Attentions and CompositionCode0
Proceedings of the Computing Natural Language Inference Workshop—0
Quantifier Scoping and Semantic Preferences—0
Textual Inference: getting logic from humansCode0
An overview of Natural Language Inference Data Collection: The way forward?—0
A Type-Theoretical system for the FraCaS test suite: Grammatical Framework meets Coq—0
Correcting ContradictionsCode0
Modeling Extractive Sentence Intersection via Subtree Entailment—0
Compositional Distributional Models of Meaning—0
Reading and Thinking: Re-read LSTM Unit for Textual Entailment Recognition—0
Context-Sensitive Inference Rule Discovery: A Graph-Based Method—0
ENIAM: Categorial Syntactic-Semantic Parser for Polish—0
Annotation and Analysis of Discourse Relations, Temporal Relations and Multi-Layered Situational Relations in Japanese Texts—0
Get Semantic With Me! The Usefulness of Different Feature Types for Short-Answer Grading—0
Building a Dictionary of Affixal NegationsCode0
Natural Language Processing for Intelligent Access to Scientific Information—0
Crowdsourcing Complex Language Resources: Playing to Annotate Dependency Syntax—0
A Hybrid Approach to Generation of Missing Abstracts in Biomedical Literature—0
Neural Attention for Learning to Rank Questions in Community Question Answering—0
A Neural Architecture Mimicking Humans End-to-End for Natural Language Inference—0
Contradiction Detection for Rumorous Claims—0
Ordinal Common-sense Inference—0
Exploiting Sentence Similarities for Better Alignments—0
Building compositional semantics and higher-order inference system for a wide-coverage Japanese CCG parser—0
Rule Extraction for Tree-to-Tree Transducers by Cost Minimization—0
Automatic Identification of Narrative Diegesis and Point of View—0
Automatic Extraction of Implicit Interpretations from Modal Constructions—0
PaCCSS-IT: A Parallel Corpus of Complex-Simple Sentences for Automatic Text Simplification—0
Research on attention memory networks as a model for learning natural language inference—0
AMR Parsing with an Incremental Joint Model—0
Supervised Distributional Hypernym Discovery via Domain Adaptation—0
POLY: Mining Relational Paraphrases from Multilingual Sentences—0
Tense Manages to Predict Implicative Behavior in Verbs—0
Learning to Reason With Adaptive Computation—0
A Hybrid Approach for Deep Machine Translation—0
Toward the automatic extraction of knowledge of usable goods—0
Lexical-Morphological Modeling for Legal Text Analysis—0
Selective Annotation of Modal Readings: Delving into the Difficult Data—0
Machine Comprehension Using Match-LSTM and Answer PointerCode0
Verbs Taking Clausal and Non-Finite Arguments as Signals of Modality -- Revisiting the Issue of Meaning Grounded in Syntax—0
Natural Solution to FraCaS Entailment Problems—0
Adding Context to Semantic Data-Driven Paraphrasing—0
Improved Representation Learning for Question Answer Matching—0
Learning To Use Formulas To Solve Simple Arithmetic Problems—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