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 801–850 of 1978 papers

TitleStatusHype
Simple and Effective Gradient-Based Tuning of Sequence-to-Sequence Models—0
Multi-grained Label Refinement Network with Dependency Structures for Joint Intent Detection and Slot FillingCode0
Entity Aware Syntax Tree Based Data Augmentation for Natural Language Understanding—0
From Black Boxes to Conversations: Incorporating XAI in a Conversational AgentCode1
Semantically Meaningful Metrics for Norwegian ASR SystemsCode0
FOLIO: Natural Language Reasoning with First-Order LogicCode1
Evaluating N-best Calibration of Natural Language Understanding for Dialogue SystemsCode0
Dialog Acts for Task Driven Embodied Agents—0
Distilling Multi-Scale Knowledge for Event Temporal Relation Extraction—0
Enhancing Semantic Understanding with Self-supervised Methods for Abstractive Dialogue Summarization—0
Unified Knowledge Prompt Pre-training for Customer Service Dialogues—0
Exploring and Evaluating Personalized Models for Code Generation—0
Building the Intent Landscape of Real-World Conversational Corpora with Extractive Question-Answering Transformers—0
Shortcut Learning of Large Language Models in Natural Language Understanding—0
IndicSUPERB: A Speech Processing Universal Performance Benchmark for Indian languagesCode1
CLOWER: A Pre-trained Language Model with Contrastive Learning over Word and Character Representations—0
Coarse-to-Fine: Hierarchical Multi-task Learning for Natural Language Understanding—0
Adapting Task-Oriented Dialogue Models for Email Conversations—0
Effective Transfer Learning for Low-Resource Natural Language Understanding—0
DeeperDive: The Unreasonable Effectiveness of Weak Supervision in Document Understanding A Case Study in Collaboration with UiPath Inc—0
ShortcutLens: A Visual Analytics Approach for Exploring Shortcuts in Natural Language Understanding Dataset—0
A Hybrid Model of Classification and Generation for Spatial Relation Extraction—0
Efficient Long-Text Understanding with Short-Text ModelsCode1
DoRO: Disambiguation of referred object for embodied agents—0
Dynamic Planning in Open-Ended Dialogue using Reinforcement Learning—0
MoEC: Mixture of Expert Clusters—0
Analyzing Bagging Methods for Language Models—0
On the cross-lingual transferability of multilingual prototypical models across NLU tasks—0
PiC: A Phrase-in-Context Dataset for Phrase Understanding and Semantic SearchCode0
End-to-End Spoken Language Understanding: Performance analyses of a voice command task in a low resource setting—0
Forming Trees with Treeformers—0
Learning to translate by learning to communicateCode0
PLM-ICD: Automatic ICD Coding with Pretrained Language ModelsCode1
Chat-to-Design: AI Assisted Personalized Fashion Design—0
Lightweight Transformers for Conversational AI—0
Efficient Semi-supervised Consistency Training for Natural Language Understanding—0
m-Networks: Adapting the Triplet Networks for Acronym DisambiguationCode0
A New Concept of Knowledge based Question Answering (KBQA) System for Multi-hop Reasoning—0
A Dog Is Passing Over The Jet? A Text-Generation Dataset for Korean Commonsense Reasoning and Evaluation—0
Strategies to Improve Few-shot Learning for Intent Classification and Slot-Filling—0
On Curriculum Learning for Commonsense ReasoningCode0
RGL: A Simple yet Effective Relation Graph Augmented Prompt-based Tuning Approach for Few-Shot Learning—0
AlexU-AL at SemEval-2022 Task 6: Detecting Sarcasm in Arabic Text Using Deep Learning TechniquesCode0
NER4ID at SemEval-2022 Task 2: Named Entity Recognition for Idiomaticity DetectionCode0
Enhancing Self-Attention with Knowledge-Assisted Attention Maps—0
Yes, No or IDK: The Challenge of Unanswerable Yes/No Questions—0
ID10M: Idiom Identification in 10 LanguagesCode0
Efficient Learning of Multiple NLP Tasks via Collective Weight Factorization on BERT—0
Dyna-bAbI: unlocking bAbI’s potential with dynamic synthetic benchmarking—0
Is neural language acquisition similar to natural? A chronological probing studyCode0
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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