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

Showing 10011050 of 1961 papers

TitleStatusHype
Sexism Detection on a Data Diet0
SG-FSM: A Self-Guiding Zero-Shot Prompting Paradigm for Multi-Hop Question Answering Based on Finite State Machine0
SG-Net: Syntax Guided Transformer for Language Representation0
Siamese Networks for Inference in Malayalam Language Texts0
Sieg at MEDIQA 2019: Multi-task Neural Ensemble for Biomedical Inference and Entailment0
SIFiD: Reassess Summary Factual Inconsistency Detection with LLM0
SimiHawk at SemEval-2016 Task 1: A Deep Ensemble System for Semantic Textual Similarity0
Similarity and Farness Based Bidirectional Neural Co-Attention for Amharic Natural Language Inference0
Simple Yet Effective Synthetic Dataset Construction for Unsupervised Opinion Summarization0
SIRIUS-LTG: An Entity Linking Approach to Fact Extraction and Verification0
SMoA: Sparse Mixture of Adapters to Mitigate Multiple Dataset Biases0
So-Called Non-Subsective Adjectives0
SOFTCARDINALITY-CORE: Improving Text Overlap with Distributional Measures for Semantic Textual Similarity0
SOFTCARDINALITY: Hierarchical Text Overlap for Student Response Analysis0
SOFTCARDINALITY: Learning to Identify Directional Cross-Lingual Entailment from Cardinalities and SMT0
Soft Cardinality + ML: Learning Adaptive Similarity Functions for Cross-lingual Textual Entailment0
Soft Label Memorization-Generalization for Natural Language Inference0
Soft Layer Selection with Meta-Learning for Zero-Shot Cross-Lingual Transfer0
Spot the Odd Man Out: Exploring the Associative Power of Lexical Resources0
SPred: Large-scale Harvesting of Semantic Predicates0
Squibs: What Is a Paraphrase?0
sranjans : Semantic Textual Similarity using Maximal Weighted Bipartite Graph Matching0
SRIUBC: Simple Similarity Features for Semantic Textual Similarity0
SSAS: Semantic Similarity for Abstractive Summarization0
Stanford: Probabilistic Edit Distance Metrics for STS0
Stress Test Evaluation of Transformer-based Models in Natural Language Understanding Tasks0
Stress-Testing Neural Models of Natural Language Inference with Multiply-Quantified Sentences0
String Re-writing Kernel0
Strong hallucinations from negation and how to fix them0
StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding0
Structural Representations for Learning Relations between Pairs of Texts0
STRUCTURED ALIGNMENT NETWORKS0
Structured Alignment Networks for Matching Sentences0
Structured Attention Networks0
Structured Learning for Taxonomy Induction with Belief Propagation0
STS-UHH at SemEval-2017 Task 1: Scoring Semantic Textual Similarity Using Supervised and Unsupervised Ensemble0
Stubborn Lexical Bias in Data and Models0
Subword ELMo0
SufiSent - Universal Sentence Representations Using Suffix Encodings0
Supervised Distributional Hypernym Discovery via Domain Adaptation0
Supervised Open Information Extraction0
Surf at MEDIQA 2019: Improving Performance of Natural Language Inference in the Clinical Domain by Adopting Pre-trained Language Model0
基於特徵為本及使用SVM 的文本對蘊涵關係的自動推論方法 (Textual Entailment Recognition Using Textual Features and SVM) [In Chinese]0
SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference0
SWOW-8500: Word Association task for Intrinsic Evaluation of Word Embeddings0
SXUCFN-Core: STS Models Integrating FrameNet Parsing Information0
SylloBio-NLI: Evaluating Large Language Models on Biomedical Syllogistic Reasoning0
Synonym Acquisition Using Bilingual Comparable Corpora0
Syntax-based Attention Model for Natural Language Inference0
Systems' Agreements and Disagreements in Temporal Processing: An Extensive Error Analysis of the TempEval-3 Task0
Show:102550
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UnitedSynT5 (3B)% Test Accuracy94.7Unverified
2UnitedSynT5 (335M)% Test Accuracy93.5Unverified
3Neural Tree Indexers for Text Understanding% Test Accuracy93.1Unverified
4EFL (Entailment as Few-shot Learner) + RoBERTa-large% Test Accuracy93.1Unverified
5RoBERTa-large+Self-Explaining% Test Accuracy92.3Unverified
6RoBERTa-large + self-explaining layer% Test Accuracy92.3Unverified
7CA-MTL% Test Accuracy92.1Unverified
8SemBERT% Test Accuracy91.9Unverified
9MT-DNN-SMARTLARGEv0% Test Accuracy91.7Unverified
10MT-DNN-SMART_100%ofTrainingDataDev Accuracy91.6Unverified
#ModelMetricClaimedVerifiedStatus
1Vega v2 6B (KD-based prompt transfer)Accuracy96Unverified
2PaLM 540B (fine-tuned)Accuracy95.7Unverified
3Turing NLR v5 XXL 5.4B (fine-tuned)Accuracy94.1Unverified
4ST-MoE-32B 269B (fine-tuned)Accuracy93.5Unverified
5DeBERTa-1.5BAccuracy93.2Unverified
6MUPPET Roberta LargeAccuracy92.8Unverified
7DeBERTaV3largeAccuracy92.7Unverified
8T5-XXL 11B (fine-tuned)Accuracy92.5Unverified
9T5-XXL 11BAccuracy92.5Unverified
10ST-MoE-L 4.1B (fine-tuned)Accuracy92.1Unverified
#ModelMetricClaimedVerifiedStatus
1UnitedSynT5 (3B)Matched92.6Unverified
2Turing NLR v5 XXL 5.4B (fine-tuned)Matched92.6Unverified
3T5-XXL 11B (fine-tuned)Matched92Unverified
4T5Matched92Unverified
5T5-11BMismatched91.7Unverified
6T5-3BMatched91.4Unverified
7ALBERTMatched91.3Unverified
8Adv-RoBERTa ensembleMatched91.1Unverified
9DeBERTa (large)Matched91.1Unverified
10SMARTRoBERTaDev Matched91.1Unverified