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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 1001–1050 of 1978 papers

TitleStatusHype
Parameter-Efficient Tuning with Special Token AdaptationCode0
Event Extraction: A Survey—0
Join-Chain Network: A Logical Reasoning View of the Multi-head Attention in Transformer—0
Time Will Change Things: An Empirical Study on Dynamic Language Understanding in Social Media Classification—0
Understanding Prior Bias and Choice Paralysis in Transformer-based Language Representation Models through Four Experimental Probes—0
ParaZh-22M: A Large-Scale Chinese Parabank via Machine Translation—0
Aligning Multilingual Embeddings for Improved Code-switched Natural Language UnderstandingCode0
Improving Event Temporal Relation Classification via Auxiliary Label-Aware Contrastive Learning—0
Are Visual-Linguistic Models Commonsense Knowledge Bases?Code0
A Transformer-based Threshold-Free Framework for Multi-Intent NLU—0
Competence-based Question Generation—0
To What Extent Do Natural Language Understanding Datasets Correlate to Logical Reasoning? A Method for Diagnosing Logical Reasoning.—0
Building Korean Linguistic Resource for NLU Data Generation of Banking App CS Dialog System—0
Improving Commonsense Contingent Reasoning by Pseudo-data and Its Application to the Related Tasks—0
Does Meta-learning Help mBERT for Few-shot Question Generation in a Cross-lingual Transfer Setting for Indic Languages?—0
Evaluating Coreference Resolvers on Community-based Question Answering: From Rule-based to State of the ArtCode0
RSGT: Relational Structure Guided Temporal Relation Extraction—0
EventBERT: Incorporating Event-based Semantics for Natural Language Understanding—0
Machine Reading, Fast and Slow: When Do Models “Understand” Language?—0
TextGraphs-16 Natural Language Premise Selection Task: Zero-Shot Premise Selection with Prompting Generative Language Models—0
Dialog Acts for Task-Driven Embodied Agents—0
Representing Affect Information in Word Embeddings—0
Adaptive Natural Language Generation for Task-oriented Dialogue via Reinforcement LearningCode0
Can Offline Reinforcement Learning Help Natural Language Understanding?—0
Machine Reading, Fast and Slow: When Do Models "Understand" Language?—0
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
Semantically Meaningful Metrics for Norwegian ASR SystemsCode0
Enhancing Semantic Understanding with Self-supervised Methods for Abstractive Dialogue Summarization—0
Dialog Acts for Task Driven Embodied Agents—0
Distilling Multi-Scale Knowledge for Event Temporal Relation Extraction—0
Evaluating N-best Calibration of Natural Language Understanding for Dialogue SystemsCode0
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
CLOWER: A Pre-trained Language Model with Contrastive Learning over Word and Character Representations—0
Adapting Task-Oriented Dialogue Models for Email Conversations—0
Effective Transfer Learning for Low-Resource Natural Language Understanding—0
Coarse-to-Fine: Hierarchical Multi-task Learning for Natural Language Understanding—0
ShortcutLens: A Visual Analytics Approach for Exploring Shortcuts in Natural Language Understanding Dataset—0
DeeperDive: The Unreasonable Effectiveness of Weak Supervision in Document Understanding A Case Study in Collaboration with UiPath Inc—0
A Hybrid Model of Classification and Generation for Spatial Relation Extraction—0
DoRO: Disambiguation of referred object for embodied agents—0
Dynamic Planning in Open-Ended Dialogue using Reinforcement Learning—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
MoEC: Mixture of Expert Clusters—0
Analyzing Bagging Methods for Language Models—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