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 1201–1250 of 1961 papers

TitleStatusHype
Align, Mask and Select: A Simple Method for Incorporating Commonsense Knowledge into Language Representation Models—0
SenseBERT: Driving Some Sense into BERT—0
Abductive Commonsense ReasoningCode0
Reasoning-Driven Question-Answering for Natural Language Understanding—0
StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding—0
Do Neural Language Representations Learn Physical Commonsense?Code0
DELTA: A DEep learning based Language Technology plAtformCode0
Simple and Effective Text Matching with Richer Alignment FeaturesCode0
Saama Research at MEDIQA 2019: Pre-trained BioBERT with Attention Visualisation for Medical Natural Language Inference—0
LasigeBioTM at MEDIQA 2019: Biomedical Question Answering using Bidirectional Transformers and Named Entity Recognition—0
MSIT\_SRIB at MEDIQA 2019: Knowledge Directed Multi-task Framework for Natural Language Inference in Clinical Domain.—0
KU\_ai at MEDIQA 2019: Domain-specific Pre-training and Transfer Learning for Medical NLI—0
Overview of the MEDIQA 2019 Shared Task on Textual Inference, Question Entailment and Question AnsweringCode1
UU\_TAILS at MEDIQA 2019: Learning Textual Entailment in the Medical Domain—0
WTMED at MEDIQA 2019: A Hybrid Approach to Biomedical Natural Language InferenceCode0
ANU-CSIRO at MEDIQA 2019: Question Answering Using Deep Contextual Knowledge—0
Sieg at MEDIQA 2019: Multi-task Neural Ensemble for Biomedical Inference and Entailment—0
ARS\_NITK at MEDIQA 2019:Analysing Various Methods for Natural Language Inference, Recognising Question Entailment and Medical Question Answering System—0
NCUEE at MEDIQA 2019: Medical Text Inference Using Ensemble BERT-BiLSTM-Attention Model—0
Explaining Simple Natural Language InferenceCode0
Fill the GAP: Exploiting BERT for Pronoun ResolutionCode0
Annotating and analyzing the interactions between meaning relationsCode0
ERNIE 2.0: A Continual Pre-training Framework for Language UnderstandingCode3
Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and EntailmentCode1
A Hybrid Neural Network Model for Commonsense Reasoning—0
LINSPECTOR WEB: A Multilingual Probing Suite for Word RepresentationsCode0
RoBERTa: A Robustly Optimized BERT Pretraining ApproachCode1
SpanBERT: Improving Pre-training by Representing and Predicting SpansCode0
Dr.Quad at MEDIQA 2019: Towards Textual Inference and Question Entailment using contextualized representations—0
Discourse Marker Augmented Network with Reinforcement Learning for Natural Language InferenceCode0
A Pragmatics-Centered Evaluation Framework for Natural Language UnderstandingCode0
Fake News Detection as Natural Language InferenceCode0
On Adversarial Removal of Hypothesis-only Bias in Natural Language InferenceCode0
Don't Take the Premise for Granted: Mitigating Artifacts in Natural Language InferenceCode0
UW-BHI at MEDIQA 2019: An Analysis of Representation Methods for Medical Natural Language Inference—0
A Study of the Effect of Resolving Negation and Sentiment Analysis in Recognizing Text Entailment for Arabic—0
Answer Extraction for Why Arabic Questions Answering Systems: EWAQ—0
Reversing Gradients in Adversarial Domain Adaptation for Question Deduplication and Textual Entailment Tasks—0
Ranking Generated Summaries by Correctness: An Interesting but Challenging Application for Natural Language Inference—0
Deep Neural Model Inspection and Comparison via Functional Neuron Pathways—0
Latent Structure Models for Natural Language Processing—0
Learning Latent Trees with Stochastic Perturbations and Differentiable Dynamic ProgrammingCode0
Investigating Biases in Textual Entailment Datasets—0
XLNet: Generalized Autoregressive Pretraining for Language UnderstandingCode1
Surf at MEDIQA 2019: Improving Performance of Natural Language Inference in the Clinical Domain by Adopting Pre-trained Language Model—0
Pre-Training with Whole Word Masking for Chinese BERTCode3
Can neural networks understand monotonicity reasoning?Code0
IITP at MEDIQA 2019: Systems Report for Natural Language Inference, Question Entailment and Question Answering—0
Augmenting Neural Networks with First-order LogicCode0
Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking DatasetsCode1
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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