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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1151–1200 of 1978 papers

TitleStatusHype
What Makes Reading Comprehension Questions Difficult?Code0
Learning Discriminative Representations and Decision Boundaries for Open Intent Detection—0
CoDA21: Evaluating Language Understanding Capabilities of NLP Models With Context-Definition AlignmentCode0
Input-Tuning: Adapting Unfamiliar Inputs to Frozen Pretrained Models—0
SkillNet-NLU: A Sparsely Activated Model for General-Purpose Natural Language Understanding—0
TableFormer: Robust Transformer Modeling for Table-Text EncodingCode0
HyperPrompt: Prompt-based Task-Conditioning of Transformers—0
Bi-directional Joint Neural Networks for Intent Classification and Slot Filling—0
Learning to Merge Tokens in Vision TransformersCode0
Pretraining without Wordpieces: Learning Over a Vocabulary of Millions of Words—0
Aspect Based Sentiment Analysis Using Spectral Temporal Graph Neural Network—0
FedQAS: Privacy-aware machine reading comprehension with federated learningCode0
CALM: Contrastive Aligned Audio-Language Multirate and Multimodal Representations—0
Learnings from Federated Learning in the Real world—0
RescoreBERT: Discriminative Speech Recognition Rescoring with BERT—0
Examining Scaling and Transfer of Language Model Architectures for Machine Translation—0
Cross-Lingual Dialogue Dataset Creation via Outline-Based Generation—0
ScaLA: Accelerating Adaptation of Pre-Trained Transformer-Based Language Models via Efficient Large-Batch Adversarial Noise—0
Pair-Level Supervised Contrastive Learning for Natural Language Inference—0
Language Generation for Broad-Coverage, Explainable Cognitive Systems—0
Convex Polytope Modelling for Unsupervised Derivation of Semantic Structure for Data-efficient Natural Language Understanding—0
Learning To Retrieve Prompts for In-Context Learning—0
RGL: A Simple yet Effective Relation Graph Augmented Prompt-based Tuning Approach for Few-Shot LearningCode0
Curriculum: A Broad-Coverage Benchmark for Linguistic Phenomena in Natural Language Understanding—0
Revisiting the Roles of “Text” in Text Games—0
Exploiting Topic Information for Joint Intent Detection and Slot Filling—0
AraBART: a Pretrained Arabic Sequence-to-Sequence Model for Abstractive Summarization—0
Label-guided Data Augmentation for Prompt-based Few Shot Learners—0
Causal Distillation for Language Models—0
A Transformer-based Threshold-Free Framework for Multi-Intent NLU—0
Temporal Relation Extraction with a Graph-Based Deep Biaffine Attention Model—0
Event Linking: Grounding Event Mentions to Wikipedia—0
MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation—0
TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning—0
Imagination-Augmented Natural Language Understanding—0
Improved and Efficient Conversational Slot Labeling through Question Answering—0
CommonsenseQA 2.0: Exposing the Limits of AI through Gamification—0
The Dark Side of the Language: Pre-trained Transformers in the DarkNet—0
How Does Data Corruption Affect Natural Language Understanding Models? A Study on GLUE datasetsCode0
Latency Adjustable Transformer Encoder for Language Understanding—0
Intent Matching based Customer Services Chatbot with Natural Language Understanding—0
Hyperparameter-free Continuous Learning for Domain Classification in Natural Language Understanding—0
Chatbot for fitness management using IBM Watson—0
Improving Tokenisation by Alternative Treatment of Spaces—0
Training Dynamics for Curriculum Learning: A Study on Monolingual and Cross-lingual NLU—0
Sharpness-Aware Minimization with Dynamic Reweighting—0
Design Challenges for a Multi-Perspective Search EngineCode0
Event Linking: Grounding Event Mentions to WikipediaCode0
Representing Inferences and their Lexicalization—0
Exploring the Limits of Natural Language Inference Based Setup for Few-Shot Intent DetectionCode0
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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