SOTAVerified

Natural Language Understanding

Natural Language Understanding is an important field of Natural Language Processing which contains various tasks such as text classification, natural language inference and story comprehension. Applications enabled by natural language understanding range from question answering to automated reasoning.

Source: Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test?

Papers

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Showing 1101–1150 of 1978 papers

TitleStatusHype
Natural language understanding for logical gamesCode0
A Review of Text Style Transfer using Deep Learning—0
Structural Persistence in Language Models: Priming as a Window into Abstract Language RepresentationsCode0
Logic Pre-Training of Language Models—0
Sparse Attention with Learning to Hash—0
Variance Pruning: Pruning Language Models via Temporal Neuron Variance—0
Pseudo Knowledge Distillation: Towards Learning Optimal Instance-specific Label Smoothing Regularization—0
Call Larisa Ivanovna: Code-Switching Fools Multilingual NLU ModelsCode0
Using Pause Information for More Accurate Entity Recognition—0
FewNLU: Benchmarking State-of-the-Art Methods for Few-Shot Natural Language UnderstandingCode1
Text-based NP EnrichmentCode1
Knowledge Distillation with Noisy Labels for Natural Language Understanding—0
Training Dynamic based data filtering may not work for NLP datasets—0
What Makes Reading Comprehension Questions Difficult? Investigating Variation in Passage Sources and Question Types—0
FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning—0
CoDA21: Evaluating Language Understanding Capabilities of NLP Models With Context-Definition Alignment—0
Remixers: A Mixer-Transformer Architecture with Compositional Operators for Natural Language Understanding—0
Semi-Supervised Few-Shot Intent Classification and Slot Filling—0
Fine-Tuned Transformers Show Clusters of Similar Representations Across Layers—0
Self-training with Few-shot Rationalization: Teacher Explanations Aid Student in Few-shot NLU—0
Can Machines Read Coding Manuals Yet? -- A Benchmark for Building Better Language Models for Code UnderstandingCode0
The Unreasonable Effectiveness of the Baseline: Discussing SVMs in Legal Text Classification—0
ARCH: Efficient Adversarial Regularized Training with CachingCode0
Task-adaptive Pre-training and Self-training are Complementary for Natural Language Understanding—0
Types of Out-of-Distribution Texts and How to Detect ThemCode1
CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and GenerationCode1
How to Select One Among All? An Extensive Empirical Study Towards the Robustness of Knowledge Distillation in Natural Language UnderstandingCode1
GradTS: A Gradient-Based Automatic Auxiliary Task Selection Method Based on Transformer Networks—0
Extracting Event Temporal Relations via Hyperbolic GeometryCode1
Prior Omission of Dissimilar Source Domain(s) for Cost-Effective Few-Shot Learning—0
Exophoric Pronoun Resolution in Dialogues with Topic RegularizationCode0
Temporal Pyramid Transformer with Multimodal Interaction for Video Question AnsweringCode1
Graph Based Network with Contextualized Representations of Turns in DialogueCode1
Debiasing Methods in Natural Language Understanding Make Bias More AccessibleCode1
Continuous Entailment Patterns for Lexical Inference in ContextCode0
Active Learning by Acquiring Contrastive ExamplesCode1
Proto: A Neural Cocktail for Generating Appealing Conversations—0
End-to-End Self-Debiasing Framework for Robust NLU Training—0
Error Detection in Large-Scale Natural Language Understanding Systems Using Transformer Models—0
CREAK: A Dataset for Commonsense Reasoning over Entity KnowledgeCode1
InFoBERT: Zero-Shot Approach to Natural Language Understanding Using Contextualized Word Embedding—0
Approximating a Zulu GF concrete syntax with a neural network for natural language understanding—0
Does Knowledge Help General NLU? An Empirical Study—0
ASR-GLUE: A New Multi-task Benchmark for ASR-Robust Natural Language Understanding—0
WALNUT: A Benchmark on Semi-weakly Supervised Learning for Natural Language Understanding—0
Integrating Heuristics and Learning in a Computational Architecture for Cognitive Trading—0
SLIM: Explicit Slot-Intent Mapping with BERT for Joint Multi-Intent Detection and Slot FillingCode1
SAUCE: Truncated Sparse Document Signature Bit-Vectors for Fast Web-Scale Corpus Expansion—0
A New Sentence Ordering Method Using BERT Pretrained Model—0
Using BERT Encoding and Sentence-Level Language Model for Sentence Ordering—0
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All datasetsPDP60STREUSLELexGLUEDialoGLUE fewshotDialoGLUE fullGLUE

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HNNAccuracy90—Unverified
2UDSSM-II (ensemble)Accuracy78.3—Unverified
3BERT-large 340MAccuracy78.3—Unverified
4UDSSM-I (ensemble)Accuracy76.7—Unverified
5DSSMAccuracy75—Unverified
6UDSSM-IIAccuracy75—Unverified
7BERT-base 110M + MASAccuracy68.3—Unverified
8USSM + Supervised Deepnet + 3 Knowledge BasesAccuracy66.7—Unverified
9Word-level CNN+LSTM (full scoring)Accuracy60—Unverified
10Subword-level Transformer LMAccuracy58.3—Unverified
#ModelMetricClaimedVerifiedStatus
1BERT (pred POS/lemmas)Tags (Full) Acc82.5—Unverified
2BERT (none)Tags (Full) Acc82—Unverified
3BERT (gold POS/lemmas)Tags (Full) Acc81—Unverified
4GloVe (gold POS/lemmas)Tags (Full) Acc79.3—Unverified
5RoBERTa + LinearFull F1 (Preps)78.2—Unverified
6GloVe (none)Tags (Full) Acc77.5—Unverified
7GloVe (pred POS/lemmas)Tags (Full) Acc77.1—Unverified
8SVM (feature-rich, gold syntax)Role F1 (Preps)62.2—Unverified
9BiLSTM + MLP (gold syntax)Role F1 (Preps)62.2—Unverified
10SVM (feature-rich, auto syntax)Role F1 (Preps)58.2—Unverified
#ModelMetricClaimedVerifiedStatus
1CaseLaw-BERTCaseHOLD75.6—Unverified
2Legal-BERTCaseHOLD75.1—Unverified
3DeBERTaCaseHOLD72.1—Unverified
4LongformerCaseHOLD72—Unverified
5RoBERTaCaseHOLD71.7—Unverified
6BERTCaseHOLD70.7—Unverified
7BigBirdCaseHOLD70.4—Unverified
#ModelMetricClaimedVerifiedStatus
1ConvBERT-DGAverage74.6—Unverified
2ConvBERT-DG + Pre + MultiAverage73.8—Unverified
3mslmAverage73.49—Unverified
4ConvBERT + Pre + MultiAverage68.22—Unverified
5BanLanGenAverage39.16—Unverified
#ModelMetricClaimedVerifiedStatus
1ConvBERT + Pre + MultiAverage86.89—Unverified
2mslmAverage85.83—Unverified
3ConvBERT-DG + Pre + MultiAverage85.34—Unverified
#ModelMetricClaimedVerifiedStatus
1MT-DNN-SMARTAverage89.9—Unverified
2BERT-LARGEAverage82.1—Unverified