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 301–350 of 1961 papers

TitleStatusHype
Balanced Adversarial Training: Balancing Tradeoffs Between Oversensitivity and Undersensitivity in NLP Models—0
A critique of word similarity as a method for evaluating distributional semantic models—0
Data-aware Low-Rank Compression for Large NLP Models—0
BaIT: Barometer for Information Trustworthiness—0
Bag-of-Vector Embeddings of Dependency Graphs for Semantic Induction—0
An Analysis of Natural Language Inference Benchmarks through the Lens of Negation—0
Bag of Tricks for Effective Language Model Pretraining and Downstream Adaptation: A Case Study on GLUE—0
Back to Basics for Monolingual Alignment: Exploiting Word Similarity and Contextual Evidence—0
A Formalism and Approach for Improving Robustness of Large Language Models Using Risk-Adjusted Confidence Scores—0
Data Augmentation with Adversarial Training for Cross-Lingual NLI—0
AXCEL: Automated eXplainable Consistency Evaluation using LLMs—0
A Word-Embedding-based Sense Index for Regular Polysemy Representation—0
A Wide-Coverage Symbolic Natural Language Inference System—0
AWE: Asymmetric Word Embedding for Textual Entailment—0
Curriculum Discovery through an Encompassing Curriculum Learning Framework—0
Defining and Evaluating Decision and Composite Risk in Language Models Applied to Natural Language Inference—0
CS-NLP team at SemEval-2020 Task 4: Evaluation of State-of-the-art NLP Deep Learning Architectures on Commonsense Reasoning Task—0
Automatic Validation of Textual Attribute Values in E-commerce Catalog by Learning with Limited Labeled Data—0
Automatic Nominalization of Clauses—0
Analysis of the Fed's communication by using textual entailment model of Zero-Shot classification—0
CSReader at SemEval-2018 Task 11: Multiple Choice Question Answering as Textual Entailment—0
Dead parrots make bad pets: Exploring modifier effects in noun phrases—0
Automatic Knowledge Acquisition for Case Alternation between the Passive and Active Voices in Japanese—0
Automatic Identification of Narrative Diegesis and Point of View—0
Analysis and Mitigation of Dataset Artifacts in OpenAI GPT-3—0
Automatic Fact-Checking with Document-level Annotations using BERT and Multiple Instance Learning—0
Automatic Extraction of Implicit Interpretations from Modal Constructions—0
AnaLog: Testing Analytical and Deductive Logic Learnability in Language Models—0
Affective Common Sense Knowledge Acquisition for Sentiment Analysis—0
Automatic Evaluation of Summary Using Textual Entailment—0
An\'alise de Medidas de Similaridade Sem\^antica na Tarefa de Reconhecimento de Implica \~ao Textual (Analysis of Semantic Similarity Measures in the Recognition of Textual Entailment Task)[In Portuguese]—0
Automatic Building and Using Parallel Resources for SMT from Comparable Corpora—0
PECO: Examining Single Sentence Label Leakage in Natural Language Inference Datasets through Progressive Evaluation of Cluster Outliers—0
A Fact Checking and Verification System for FEVEROUS Using a Zero-Shot Learning Approach—0
Acquiring Predicate Paraphrases from News Tweets—0
Cross-Policy Compliance Detection via Question Answering—0
Auto-GDA: Automatic Domain Adaptation for Efficient Grounding Verification in Retrieval Augmented Generation—0
A Unified Kernel Approach for Learning Typed Sentence Rewritings—0
A corpus of precise natural textual entailment problems—0
Crowd-Calibrator: Can Annotator Disagreement Inform Calibration in Subjective Tasks?—0
Augmenting NLP data to counter Annotation Artifacts for NLI Tasks—0
A Multilingual Perspective Towards the Evaluation of Attribution Methods—0
Adversarial Text Normalization—0
A Type-Theoretical system for the FraCaS test suite: Grammatical Framework meets Coq—0
A Bayesian Approach to Unsupervised Semantic Role Induction—0
Crowdsourcing Complex Language Resources: Playing to Annotate Dependency Syntax—0
Crowdsourcing Inference-Rule Evaluation—0
A Two Level Model for Context Sensitive Inference Rules—0
A Multi-level Supervised Contrastive Learning Framework for Low-Resource Natural Language Inference—0
Cross-Lingual Transfer for Natural Language Inference via Multilingual Prompt Translator—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