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

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Showing 1301–1350 of 1978 papers

TitleStatusHype
Does Knowledge Help General NLU? An Empirical Study—0
Approximating a Zulu GF concrete syntax with a neural network for natural language understanding—0
InFoBERT: Zero-Shot Approach to Natural Language Understanding Using Contextualized Word Embedding—0
ASR-GLUE: A New Multi-task Benchmark for ASR-Robust Natural Language Understanding—0
WALNUT: A Benchmark on Semi-weakly Supervised Learning for Natural Language Understanding—0
Integrating Heuristics and Learning in a Computational Architecture for Cognitive Trading—0
SAUCE: Truncated Sparse Document Signature Bit-Vectors for Fast Web-Scale Corpus Expansion—0
A New Sentence Ordering Method Using BERT Pretrained Model—0
Taming the Beast: Learning to Control Neural Conversational Models—0
Using BERT Encoding and Sentence-Level Language Model for Sentence Ordering—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
Point Discriminative Learning for Data-efficient 3D Point Cloud Analysis—0
LICHEE: Improving Language Model Pre-training with Multi-grained TokenizationCode0
HiddenCut: Simple Data Augmentation for Natural Language Understanding with Better GeneralizabilityCode0
Structure-aware Sentence Encoder in Bert-Based Siamese Network—0
ADEPT: An Adjective-Dependent Plausibility Task—0
Verb Metaphor Detection via Contextual Relation Learning—0
TexSmart: A System for Enhanced Natural Language Understanding—0
Local Structure Matters Most: Perturbation Study in NLU—0
Goal-Oriented Script ConstructionCode0
The Effectiveness of Intermediate-Task Training for Code-Switched Natural Language Understanding—0
Argument Linking: A Survey and Forecast—0
An Efficient DP-SGD Mechanism for Large Scale NLP Models—0
End-to-End Natural Language Understanding Pipeline for Bangla Conversational Agents—0
ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and GenerationCode0
An Investigation of the (In)effectiveness of Counterfactually Augmented DataCode0
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
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
Learning Multilingual Representation for Natural Language Understanding with Enhanced Cross-Lingual Supervision—0
Learning Slice-Aware Representations with Mixture of Attentions—0
SOCCER: An Information-Sparse Discourse State Tracking Collection in the Sports Commentary Domain—0
NewsEmbed: Modeling News through Pre-trained Document Representations—0
Continuous Model Improvement for Language Understanding with Machine Translation—0
Nora: The Well-Being Coach—0
Optimizing NLU Reranking Using Entity Resolution Signals in Multi-domain Dialog Systems—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
What Ingredients Make for an Effective Crowdsourcing Protocol for Difficult NLU Data Collection Tasks?Code0
Identifying and Resolving Annotation Changes for Natural Language Understanding—0
Comparing Test Sets with Item Response Theory—0
HiddenCut: Simple Data Augmentation for Natural Language Understanding with Better GeneralizationCode0
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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