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 1001–1050 of 1961 papers

TitleStatusHype
Supervising Model Attention with Human Explanations for Robust Natural Language InferenceCode0
Does Putting a Linguist in the Loop Improve NLU Data Collection?—0
"I'm Not Mad": Commonsense Implications of Negation and Contradiction—0
Unsupervised Learning of Explainable Parse Trees for Improved GeneralisationCode0
NLI Data Sanity Check: Assessing the Effect of Data Corruption on Model PerformanceCode0
Explaining Neural Network Predictions on Sentence Pairs via Learning Word-Group MasksCode0
BreakingBERT@IITK at SemEval-2021 Task 9 : Statement Verification and Evidence Finding with TablesCode0
Improving Pretrained Models for Zero-shot Multi-label Text Classification through Reinforced Label Hierarchy ReasoningCode0
How Fast can BERT Learn Simple Natural Language Inference?—0
A Simple Three-Step Approach for the Automatic Detection of Exaggerated Statements in Health Science News—0
Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference—0
Cross-Lingual Transfer with MAML on Trees—0
SICK-NL: A Dataset for Dutch Natural Language InferenceCode0
You Can Do Better! If You Elaborate the Reason When Making Prediction—0
Thinking Aloud: Dynamic Context Generation Improves Zero-Shot Reasoning Performance of GPT-2—0
Unsupervised Contextual Paraphrase Generation using Lexical Control and Reinforcement Learning—0
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing—0
SILT: Efficient transformer training for inter-lingual inferenceCode0
Robustly Optimized and Distilled Training for Natural Language Understanding—0
Meta-Learning with MAML on Trees—0
Overcoming Poor Word Embeddings with Word Definitions—0
Have Attention Heads in BERT Learned Constituency Grammar?—0
Capturing Label Distribution: A Case Study in NLI—0
Language Models for Lexical Inference in ContextCode0
A Note on Argumentative Topology: Circularity and Syllogisms as Unsolved Problems—0
Does Putting a Linguist in the Loop Improve NLU Data Collection?—0
Exploring Transitivity in Neural NLI Models through VeridicalityCode0
Muppet: Massive Multi-task Representations with Pre-FinetuningCode0
Evaluation of BERT and ALBERT Sentence Embedding Performance on Downstream NLP Tasks—0
Exploring Lexical Irregularities in Hypothesis-Only Models of Natural Language Inference—0
Unstructured Knowledge Access in Task-oriented Dialog Modeling using Language Inference, Knowledge Retrieval and Knowledge-Integrative Response GenerationCode0
SICKNL: A Dataset for Dutch Natural Language InferenceCode0
Attentive Tree-structured Network for Monotonicity Reasoning—0
Data-aware Low-Rank Compression for Large NLP Models—0
Learning Contextualized Knowledge Graph Structures for Commonsense Reasoning—0
XLA: A Robust Unsupervised Data Augmentation Framework for Cross-Lingual NLP—0
Parameterization of Hypercomplex Multiplications—0
Feedforward Legendre Memory Unit—0
Universal Sentence Representations Learning with Conditional Masked Language Model—0
Using Natural Language Relations between Answer Choices for Machine ComprehensionCode0
CLEAR: Contrastive Learning for Sentence Representation—0
Out of Order: How Important Is The Sequential Order of Words in a Sentence in Natural Language Understanding Tasks?—0
Universal Sentence Representation Learning with Conditional Masked Language Model—0
SG-Net: Syntax Guided Transformer for Language Representation—0
To what extent do human explanations of model behavior align with actual model behavior?—0
Undivided Attention: Are Intermediate Layers Necessary for BERT?—0
LIREx: Augmenting Language Inference with Relevant ExplanationCode0
Looking Beyond Short-Premise Natural Language Inference for Downstream Tasks—0
Exploiting BERT to improve aspect-based sentiment analysis performance on Persian languageCode0
Meta-Embeddings for Natural Language Inference and Semantic Similarity tasks—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