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 1001–1050 of 1978 papers

TitleStatusHype
TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning—0
Temporal Relation Extraction with a Graph-Based Deep Biaffine Attention Model—0
The Dark Side of the Language: Pre-trained Transformers in the DarkNet—0
CommonsenseQA 2.0: Exposing the Limits of AI through Gamification—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
Training Dynamics for Curriculum Learning: A Study on Monolingual and Cross-lingual NLU—0
Improving Tokenisation by Alternative Treatment of Spaces—0
Sharpness-Aware Minimization with Dynamic Reweighting—0
Learning To Retrieve Prompts for In-Context LearningCode1
Event Linking: Grounding Event Mentions to WikipediaCode0
Design Challenges for a Multi-Perspective Search EngineCode0
Representing Inferences and their Lexicalization—0
Towards a Unified Foundation Model: Jointly Pre-Training Transformers on Unpaired Images and Text—0
Exploring the Limits of Natural Language Inference Based Setup for Few-Shot Intent DetectionCode0
Attentive Contextual Carryover for Multi-Turn End-to-End Spoken Language Understanding—0
Detecting Emotion Carriers by Combining Acoustic and Lexical Representations—0
Revisiting the Boundary between ASR and NLU in the Age of Conversational Dialog Systems—0
A Puzzle-Based Dataset for Natural Language InferenceCode0
Few-Shot NLU with Vector Projection Distance and Abstract Triangular CRF—0
Question Answering Survey: Directions, Challenges, Datasets, Evaluation Matrices—0
Causal Distillation for Language ModelsCode1
LoNLI: An Extensible Framework for Testing Diverse Logical Reasoning Capabilities for NLI—0
Probing Linguistic Information For Logical Inference In Pre-trained Language ModelsCode0
DKPLM: Decomposable Knowledge-enhanced Pre-trained Language Model for Natural Language Understanding—0
Tiny-NewsRec: Effective and Efficient PLM-based News RecommendationCode1
Towards Explainable Dialogue System: Explaining Intent Classification using Saliency Techniques—0
Towards More Robust Natural Language Understanding—0
Systematic Generalization with Edge TransformersCode1
Multilingual Pre-training with Universal Dependency Learning—0
Dyna-bAbI: unlocking bAbI's potential with dynamic synthetic benchmarking—0
Simple Contrastive Representation Adversarial Learning for NLP Tasks—0
A Hybrid Neuro-Symbolic approach for Text-Based Games using Inductive Logic Programming—0
RoBERTuito: a pre-trained language model for social media text in SpanishCode1
DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding SharingCode2
When More Data Hurts: A Troubling Quirk in Developing Broad-Coverage Natural Language Understanding Systems—0
EventBERT—0
UNICON: Unsupervised Intent Discovery via Semantic-level Contrastive Learning—0
BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla—0
Knowledge Graph is in Rescue: Task Oriented Dialogue System for Response Generation without NLU and DM—0
What Makes Machine Reading Comprehension Questions Difficult? Investigating Variation in Passage Sources and Question Types—0
Get Your Model Puzzled: Introducing Crossword-Solving as a New NLP Benchmark—0
IDPG: An Instance-Dependent Prompt Generation Method—0
GLM: General Language Model Pretraining with Autoregressive Blank Infilling—0
Data Augmentation and Learned Layer Aggregation for Improved Multilingual Language Understanding in Dialogue—0
CBLUE: A Chinese Biomedical Language Understanding EvaluationBenchmark—0
DAWSON: Data Augmentation using Weak Supervision On Natural Language—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