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 451–500 of 1961 papers

TitleStatusHype
Compliance Checking with NLI: Privacy Policies vs. Regulations—0
CS-NLP team at SemEval-2020 Task 4: Evaluation of State-of-the-art NLP Deep Learning Architectures on Commonsense Reasoning Task—0
CSReader at SemEval-2018 Task 11: Multiple Choice Question Answering as Textual Entailment—0
Augmenting NLP data to counter Annotation Artifacts for NLI Tasks—0
A Simple Three-Step Approach for the Automatic Detection of Exaggerated Statements in Health Science News—0
Curriculum Discovery through an Encompassing Curriculum Learning Framework—0
A Unified Kernel Approach for Learning Typed Sentence Rewritings—0
Comparison and Combination of Sentence Embeddings Derived from Different Supervision Signals—0
Data Augmentation with Adversarial Training for Cross-Lingual NLI—0
Data-aware Low-Rank Compression for Large NLP Models—0
A Bayesian Approach to Unsupervised Semantic Role Induction—0
Comparing two acquisition systems for automatically building an English---Croatian parallel corpus from multilingual websites—0
ECNU at SemEval-2016 Task 1: Leveraging Word Embedding From Macro and Micro Views to Boost Performance for Semantic Textual Similarity—0
ECNUCS: Recognizing Cross-lingual Textual Entailment Using Multiple Text Similarity and Text Difference Measures—0
ECNU: Using Traditional Similarity Measurements and Word Embedding for Semantic Textual Similarity Estimation—0
Automatic Building and Using Parallel Resources for SMT from Comparable Corpora—0
Emergent: a novel data-set for stance classification—0
ENIAM: Categorial Syntactic-Semantic Parser for Polish—0
Evaluating Paraphrastic Robustness in Textual Entailment Models—0
deepCybErNet at EmoInt-2017: Deep Emotion Intensities in Tweets—0
A SICK cure for the evaluation of compositional distributional semantic models—0
Comparing Hallucination Detection Metrics for Multilingual Generation—0
Automatic Fact-Checking with Document-level Annotations using BERT and Multiple Instance Learning—0
Deep Learning for Natural Language Inference—0
Automatic Identification of Narrative Diegesis and Point of View—0
Deep Neural Model Inspection and Comparison via Functional Neuron Pathways—0
Automatic Knowledge Acquisition for Case Alternation between the Passive and Active Voices in Japanese—0
DeepPurple: Estimating Sentence Semantic Similarity using N-gram Regression Models and Web Snippets—0
Automatic Nominalization of Clauses—0
A Logic-based Approach for Recognizing Textual Entailment Supported by Ontological Background Knowledge—0
Compare, Compress and Propagate: Enhancing Neural Architectures with Alignment Factorization for Natural Language Inference—0
DeFTX: Denoised Sparse Fine-Tuning for Zero-Shot Cross-Lingual Transfer—0
Comparative Analysis of Contextual Relation Extraction based on Deep Learning Models—0
DEIM: An effective deep encoding and interaction model for sentence matching—0
DeModify: A Dataset for Analyzing Contextual Constraints on Modifier Deletion—0
Dependency-Based Open Information Extraction—0
A Sequential Neural Encoder with Latent Structured Description for Modeling Sentences—0
DErivBase: Inducing and Evaluating a Derivational Morphology Resource for German—0
Dermacen Analytica: A Novel Methodology Integrating Multi-Modal Large Language Models with Machine Learning in tele-dermatology—0
Design and Realization of the EXCITEMENT Open Platform for Textual Entailment—0
DRONE: Data-aware Low-rank Compression for Large NLP Models—0
A Semantically Enhanced Approach to Determine Textual Similarity—0
Detecting Asymmetric Semantic Relations in Context: A Case-Study on Hypernymy Detection—0
Detecting Bipolar Semantic Relations among Natural Language Arguments with Textual Entailment: a Study.—0
CoMeT: Integrating different levels of linguistic modeling for meaning assessment—0
A Linguistic Investigation of Machine Learning based Contradiction Detection Models: An Empirical Analysis and Future Perspectives—0
Bag of Tricks for Effective Language Model Pretraining and Downstream Adaptation: A Case Study on GLUE—0
Detecting Logical Relation In Contract Clauses—0
Detecting Metaphorical Phrases in the Polish Language—0
Combining Word Patterns and Discourse Markers for Paradigmatic Relation Classification—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