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
Taming the Beast: Learning to Control Neural Conversational Models—0
FeelsGoodMan: Inferring Semantics of Twitch Neologisms—0
Joint Multiple Intent Detection and Slot Filling via Self-distillation—0
An Investigation of the (In)effectiveness of Counterfactually Augmented Data—0
FlipDA: Effective and Robust Data Augmentation for Few-Shot LearningCode1
Point Discriminative Learning for Data-efficient 3D Point Cloud Analysis—0
LICHEE: Improving Language Model Pre-training with Multi-grained TokenizationCode0
Structure-aware Sentence Encoder in Bert-Based Siamese Network—0
TexSmart: A System for Enhanced Natural Language Understanding—0
Verb Metaphor Detection via Contextual Relation Learning—0
HiddenCut: Simple Data Augmentation for Natural Language Understanding with Better GeneralizabilityCode0
ADEPT: An Adjective-Dependent Plausibility Task—0
Local Structure Matters Most: Perturbation Study in NLU—0
Break, Perturb, Build: Automatic Perturbation of Reasoning Paths Through Question DecompositionCode1
Goal-Oriented Script ConstructionCode0
Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural NetworksCode1
The Effectiveness of Intermediate-Task Training for Code-Switched Natural Language Understanding—0
Argument Linking: A Survey and Forecast—0
FewCLUE: A Chinese Few-shot Learning Evaluation BenchmarkCode1
Trusting RoBERTa over BERT: Insights from CheckListing the Natural Language Inference TaskCode1
An Efficient DP-SGD Mechanism for Large Scale NLP Models—0
End-to-End Natural Language Understanding Pipeline for Bangla Conversational Agents—0
FaVIQ: FAct Verification from Information-seeking QuestionsCode1
ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and GenerationCode0
An Investigation of the (In)effectiveness of Counterfactually Augmented DataCode0
CLINE: Contrastive Learning with Semantic Negative Examples for Natural Language UnderstandingCode1
Scientia Potentia Est -- On the Role of Knowledge in Computational Argumentation—0
On joint training with interfaces for spoken language understanding—0
Rethinking End-to-End Evaluation of Decomposable Tasks: A Case Study on Spoken Language Understanding—0
DeltaLM: Encoder-Decoder Pre-training for Language Generation and Translation by Augmenting Pretrained Multilingual EncodersCode0
Building Intelligent Autonomous Navigation Agents—0
Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimization—0
Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and BetterCode1
Mitigating Biases in Toxic Language Detection through Invariant RationalizationCode0
Target Model Agnostic Adversarial Attacks with Query Budgets on Language Understanding Models—0
CONDA: a CONtextual Dual-Annotated dataset for in-game toxicity understanding and detection—0
Programming PuzzlesCode1
Text-to-SQL in the Wild: A Naturally-Occurring Dataset Based on Stack Exchange DataCode1
Learning Multilingual Representation for Natural Language Understanding with Enhanced Cross-Lingual Supervision—0
Relative Importance in Sentence ProcessingCode1
Learning Slice-Aware Representations with Mixture of Attentions—0
SOCCER: An Information-Sparse Discourse State Tracking Collection in the Sports Commentary Domain—0
Builder, we have done it: Evaluating & Extending Dialogue-AMR NLU Pipeline for Two Collaborative Domains—0
Combining Weakly Supervised ML Techniques for Low-Resource NLU—0
Optimizing NLU Reranking Using Entity Resolution Signals in Multi-domain Dialog Systems—0
Identifying and Resolving Annotation Changes for Natural Language Understanding—0
Continuous Model Improvement for Language Understanding with Machine Translation—0
Event Time Extraction and Propagation via Graph Attention NetworksCode1
Nora: The Well-Being Coach—0
What Ingredients Make for an Effective Crowdsourcing Protocol for Difficult NLU Data Collection Tasks?Code0
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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