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

Showing 776800 of 1978 papers

TitleStatusHype
A survey of joint intent detection and slot-filling models in natural language understanding0
From Spatial Relations to Spatial Configurations0
From text to talk: Harnessing conversational corpora for humane and diversity-aware language technology0
From Universal Language Model to Downstream Task: Improving RoBERTa-Based Vietnamese Hate Speech Detection0
From Virtual to Real: A Framework for Verbal Interaction with Robots0
A Review of Winograd Schema Challenge Datasets and Approaches0
Fully Unsupervised Crosslingual Semantic Textual Similarity Metric Based on BERT for Identifying Parallel Data0
Enhancing Semantic Word Representations by Embedding Deeper Word Relationships0
Enhancing Semantic Understanding with Self-supervised Methods for Abstractive Dialogue Summarization0
Can Wikipedia Categories Improve Masked Language Model Pretraining?0
Competence-based Question Generation0
A Review of Text Style Transfer using Deep Learning0
Enhancing Self-Attention with Knowledge-Assisted Attention Maps0
Generating Synthetic Data for Task-Oriented Semantic Parsing with Hierarchical Representations0
Can We Use Probing to Better Understand Fine-tuning and Knowledge Distillation of the BERT NLU?0
Generation-Distillation for Efficient Natural Language Understanding in Low-Data Settings0
Generative Adversarial Networks for Annotated Data Augmentation in Data Sparse NLU0
IBADR: an Iterative Bias-Aware Dataset Refinement Framework for Debiasing NLU models0
GeoGLUE: A GeoGraphic Language Understanding Evaluation Benchmark0
GeoReasoner: Reasoning On Geospatially Grounded Context For Natural Language Understanding0
Get Your Model Puzzled: Introducing Crossword-Solving as a New NLP Benchmark0
Enhancing Performance on Seen and Unseen Dialogue Scenarios using Retrieval-Augmented End-to-End Task-Oriented System0
Can Offline Reinforcement Learning Help Natural Language Understanding?0
ICH-Qwen: A Large Language Model Towards Chinese Intangible Cultural Heritage0
Identifying Bengali Multiword Expressions using Semantic Clustering0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HNNAccuracy90Unverified
2UDSSM-II (ensemble)Accuracy78.3Unverified
3BERT-large 340MAccuracy78.3Unverified
4UDSSM-I (ensemble)Accuracy76.7Unverified
5DSSMAccuracy75Unverified
6UDSSM-IIAccuracy75Unverified
7BERT-base 110M + MASAccuracy68.3Unverified
8USSM + Supervised Deepnet + 3 Knowledge BasesAccuracy66.7Unverified
9Word-level CNN+LSTM (full scoring)Accuracy60Unverified
10Subword-level Transformer LMAccuracy58.3Unverified
#ModelMetricClaimedVerifiedStatus
1BERT (pred POS/lemmas)Tags (Full) Acc82.5Unverified
2BERT (none)Tags (Full) Acc82Unverified
3BERT (gold POS/lemmas)Tags (Full) Acc81Unverified
4GloVe (gold POS/lemmas)Tags (Full) Acc79.3Unverified
5RoBERTa + LinearFull F1 (Preps)78.2Unverified
6GloVe (none)Tags (Full) Acc77.5Unverified
7GloVe (pred POS/lemmas)Tags (Full) Acc77.1Unverified
8SVM (feature-rich, gold syntax)Role F1 (Preps)62.2Unverified
9BiLSTM + MLP (gold syntax)Role F1 (Preps)62.2Unverified
10SVM (feature-rich, auto syntax)Role F1 (Preps)58.2Unverified
#ModelMetricClaimedVerifiedStatus
1CaseLaw-BERTCaseHOLD75.6Unverified
2Legal-BERTCaseHOLD75.1Unverified
3DeBERTaCaseHOLD72.1Unverified
4LongformerCaseHOLD72Unverified
5RoBERTaCaseHOLD71.7Unverified
6BERTCaseHOLD70.7Unverified
7BigBirdCaseHOLD70.4Unverified
#ModelMetricClaimedVerifiedStatus
1ConvBERT-DGAverage74.6Unverified
2ConvBERT-DG + Pre + MultiAverage73.8Unverified
3mslmAverage73.49Unverified
4ConvBERT + Pre + MultiAverage68.22Unverified
5BanLanGenAverage39.16Unverified
#ModelMetricClaimedVerifiedStatus
1ConvBERT + Pre + MultiAverage86.89Unverified
2mslmAverage85.83Unverified
3ConvBERT-DG + Pre + MultiAverage85.34Unverified
#ModelMetricClaimedVerifiedStatus
1MT-DNN-SMARTAverage89.9Unverified
2BERT-LARGEAverage82.1Unverified