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 901–950 of 1961 papers

TitleStatusHype
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
ECNU: One Stone Two Birds: Ensemble of Heterogenous Measures for Semantic Relatedness and Textual Entailment—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
Boosting Neural Language Inference via Cascaded Interactive Reasoning—0
Improved Representation Learning for Question Answer Matching—0
Improved Sentence Modeling using Suffix Bidirectional LSTM—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
Improving Composition of Sentence Embeddings through the Lens of Statistical Relational Learning—0
Annotating Entailment Relations for Shortanswer Questions—0
Improving Distantly Supervised Relation Extraction by Natural Language Inference—0
Improving Domain-Specific Retrieval by NLI Fine-Tuning—0
A Hybrid Neural Network Model for Commonsense Reasoning—0
Improving Generalization by Incorporating Coverage in Natural Language Inference—0
Improving Implicit Discourse Relation Recognition Through Feature Set Optimization—0
ECNU at SemEval-2016 Task 1: Leveraging Word Embedding From Macro and Micro Views to Boost Performance for Semantic Textual Similarity—0
Improving Medical NLI Using Context-Aware Domain Knowledge—0
Book Reviews: Recognizing Textual Entailment: Models and Applications by Ido Dagan, Dan Roth, Mark Sammons and Fabio Massimo Zanzotto—0
Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding—0
Dynamic Neural Turing Machine with Soft and Hard Addressing Schemes—0
Dynamic Integration of Background Knowledge in Neural NLU Systems—0
Annotating by Proving using SemAnTE—0
A Fact Checking and Verification System for FEVEROUS Using a Zero-Shot Learning Approach—0
Adapting Sentence Transformers for the Aviation Domain—0
A Hybrid Approach to Generation of Missing Abstracts in Biomedical Literature—0
Distilling Robustness into Natural Language Inference Models with Domain-Targeted Augmentation—0
Automatic Building and Using Parallel Resources for SMT from Comparable Corpora—0
BloombergGPT: A Large Language Model for Finance—0
Improving the Out-Of-Distribution Generalization Capability of Language Models: Counterfactually-Augmented Data is not Enough—0
Integrating Controllable Motion Skills from Demonstrations—0
Improving the Precision of Natural Textual Entailment Problem Datasets—0
Dr.Quad at MEDIQA 2019: Towards Textual Inference and Question Entailment using contextualized representations—0
Improving Zero-shot Relation Classification via Automatically-acquired Entailment Templates—0
Inclusive yet Selective: Supervised Distributional Hypernymy Detection—0
In-Context Learning Distillation for Efficient Few-Shot Fine-Tuning—0
Dropping Networks for Transfer Learning—0
Black-Box Analysis: GPTs Across Time in Legal Textual Entailment Task—0
Joint Training with Semantic Role Labeling for Better Generalization in Natural Language Inference—0
JU\_CSE\_NLP: Language Independent Cross-lingual Textual Entailment System—0
KU\_ai at MEDIQA 2019: Domain-specific Pre-training and Transfer Learning for Medical NLI—0
Inducing Example-based Semantic Frames from a Massive Amount of Verb Uses—0
Deep Learning for Natural Language Inference—0
Large-Scale Acquisition of Entailment Pattern Pairs by Exploiting Transitivity—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