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 1901–1950 of 1978 papers

TitleStatusHype
CoNFET: An English Sentence to Emojis Translation AlgorithmCode0
Auto-SLURP: A Benchmark Dataset for Evaluating Multi-Agent Frameworks in Smart Personal AssistantCode0
Concept Tagging for Natural Language Understanding: Two Decadelong Algorithm DevelopmentCode0
Natural Language Processing for Information ExtractionCode0
Automatic Pruning of Fine-tuning Datasets for Transformer-based Language ModelsCode0
Seeing Things from a Different Angle:Discovering Diverse Perspectives about ClaimsCode0
Natural language understanding for logical gamesCode0
Seeing Things from a Different Angle: Discovering Diverse Perspectives about ClaimsCode0
Natural Language Understanding with Distributed RepresentationCode0
A Unified Framework for Slot based Response Generation in a Multimodal Dialogue SystemCode0
XeroAlign: Zero-Shot Cross-lingual Transformer AlignmentCode0
Comprehensive Supersense Disambiguation of English Prepositions and PossessivesCode0
NEO-BENCH: Evaluating Robustness of Large Language Models with NeologismsCode0
NER4ID at SemEval-2022 Task 2: Named Entity Recognition for Idiomaticity DetectionCode0
AudioFormer: Audio Transformer learns audio feature representations from discrete acoustic codesCode0
A Language for Function Signature RepresentationsCode0
Neural Architecture Search: Insights from 1000 PapersCode0
Neural Architecture Search with Reinforcement LearningCode0
Translation Quality Assessment: A Brief Survey on Manual and Automatic MethodsCode0
Attention-Informed Mixed-Language Training for Zero-shot Cross-lingual Task-oriented Dialogue SystemsCode0
Neural Models for Sequence ChunkingCode0
Complement Objective TrainingCode0
Empirical Analysis of Foundational Distinctions in Linked Open DataCode0
Neural Semantic EncodersCode0
Text Summarization using Abstract Meaning RepresentationCode0
A template-independent approach for information extraction in real estate documentsCode0
Semantically-Aligned Equation Generation for Solving and Reasoning Math Word ProblemsCode0
Semantically Meaningful Metrics for Norwegian ASR SystemsCode0
Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical ApplicationsCode0
An Investigation into the Contribution of Locally Aggregated Descriptors to Figurative Language IdentificationCode0
Effect of Visual Extensions on Natural Language Understanding in Vision-and-Language ModelsCode0
Thai Winograd Schemas: A Benchmark for Thai Commonsense ReasoningCode0
NEZHA: Neural Contextualized Representation for Chinese Language UnderstandingCode0
NLEBench+NorGLM: A Comprehensive Empirical Analysis and Benchmark Dataset for Generative Language Models in NorwegianCode0
NLoRA: Nyström-Initiated Low-Rank Adaptation for Large Language ModelsCode0
Effective Use of Transformer Networks for Entity TrackingCode0
A Tale of Two DRAGGNs: A Hybrid Approach for Interpreting Action-Oriented and Goal-Oriented InstructionsCode0
Valid Text-to-SQL Generation with Unification-based DeepStochLogCode0
An Information-theoretic Multi-task Representation Learning Framework for Natural Language UnderstandingCode0
EdgeProfiler: A Fast Profiling Framework for Lightweight LLMs on Edge Using Analytical ModelCode0
Semantics-aware BERT for Language UnderstandingCode0
VALUE: Understanding Dialect Disparity in NLUCode0
Not Eliminate but Aggregate: Post-Hoc Control over Mixture-of-Experts to Address Shortcut Shifts in Natural Language UnderstandingCode0
E2S2: Encoding-Enhanced Sequence-to-Sequence Pretraining for Language Understanding and GenerationCode0
Semantic Sensitivities and Inconsistent Predictions: Measuring the Fragility of NLI ModelsCode0
NusaBERT: Teaching IndoBERT to be Multilingual and MulticulturalCode0
DTW at Qur’an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource DomainCode0
What Makes Reading Comprehension Questions Difficult?Code0
DTW at Qur'an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource DomainCode0
Tri-level Joint Natural Language Understanding for Multi-turn Conversational DatasetsCode0
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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