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 1201–1250 of 1978 papers

TitleStatusHype
Towards a Unified Foundation Model: Jointly Pre-Training Transformers on Unpaired Images and Text—0
Attentive Contextual Carryover for Multi-Turn End-to-End Spoken Language Understanding—0
Detecting Emotion Carriers by Combining Acoustic and Lexical Representations—0
A Puzzle-Based Dataset for Natural Language InferenceCode0
Revisiting the Boundary between ASR and NLU in the Age of Conversational Dialog Systems—0
Few-Shot NLU with Vector Projection Distance and Abstract Triangular CRF—0
Question Answering Survey: Directions, Challenges, Datasets, Evaluation Matrices—0
LoNLI: An Extensible Framework for Testing Diverse Logical Reasoning Capabilities for NLI—0
Probing Linguistic Information For Logical Inference In Pre-trained Language ModelsCode0
DKPLM: Decomposable Knowledge-enhanced Pre-trained Language Model for Natural Language Understanding—0
Multilingual Pre-training with Universal Dependency Learning—0
Towards More Robust Natural Language Understanding—0
Towards Explainable Dialogue System: Explaining Intent Classification using Saliency Techniques—0
Dyna-bAbI: unlocking bAbI's potential with dynamic synthetic benchmarking—0
Simple Contrastive Representation Adversarial Learning for NLP Tasks—0
A Hybrid Neuro-Symbolic approach for Text-Based Games using Inductive Logic Programming—0
DAWSON: Data Augmentation using Weak Supervision On Natural Language—0
BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla—0
EventBERT—0
IDPG: An Instance-Dependent Prompt Generation Method—0
UNICON: Unsupervised Intent Discovery via Semantic-level Contrastive Learning—0
Things not Written in Text: Exploring Spatial Commonsense from Visual Signals—0
RoMe: A Robust Metric for Evaluating Natural Language Generation—0
Stable Natural Language Understanding via Invariant Causal Constraint—0
FewNLU: Benchmarking State-of-the-Art Methods for Few-Shot Natural Language Understanding—0
GLM: General Language Model Pretraining with Autoregressive Blank Infilling—0
The Dark Side of the Language: Pre-trained Transformers in the DarkNet—0
Get Your Model Puzzled: Introducing Crossword-Solving as a New NLP Benchmark—0
KALA: Knowledge-Augmented Language Model Adaptation—0
Probing Position-Aware Attention Mechanism in Long Document Understanding—0
Data Augmentation and Learned Layer Aggregation for Improved Multilingual Language Understanding in Dialogue—0
What Makes Machine Reading Comprehension Questions Difficult? Investigating Variation in Passage Sources and Question Types—0
FeelsGoodMan: Inferring Semantics of Twitch Neologisms—0
CBLUE: A Chinese Biomedical Language Understanding EvaluationBenchmark—0
When More Data Hurts: A Troubling Quirk in Developing Broad-Coverage Natural Language Understanding Systems—0
Knowledge Graph is in Rescue: Task Oriented Dialogue System for Response Generation without NLU and DM—0
Local Structure Matters Most: Perturbation Study in NLU—0
An Enactivist account of Mind Reading in Natural Language Understanding—0
Cross-lingual Adaption Model-Agnostic Meta-Learning for Natural Language Understanding—0
Ontology-based question answering over corporate structured data—0
A Case Study and Qualitative Analysis of Simple Cross-Lingual Opinion MiningCode0
Adapting to the Long Tail: A Meta-Analysis of Transfer Learning Research for Language Understanding TasksCode0
Self-training with Few-shot Rationalization—0
FrameNet-like Annotation of Olfactory Information in Texts—0
Named Entity Recognition in the Romanian Legal DomainCode0
Collaborative Data Relabeling for Robust and Diverse Voice Apps Recommendation in Intelligent Personal Assistants—0
Learning Numeracy: A Simple Yet Effective Number Embedding Approach Using Knowledge GraphCode0
How to Select One Among All ? An Empirical Study Towards the Robustness of Knowledge Distillation in Natural Language Understanding—0
An Investigation into the Contribution of Locally Aggregated Descriptors to Figurative Language IdentificationCode0
Semi-supervised Intent Discovery with Contrastive Learning—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