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 176200 of 1978 papers

TitleStatusHype
Generate to Understand for RepresentationCode1
Causal Distillation for Language ModelsCode1
Event Time Extraction and Propagation via Graph Attention NetworksCode1
Adapting Pretrained Transformer to Lattices for Spoken Language UnderstandingCode1
From Black Boxes to Conversations: Incorporating XAI in a Conversational AgentCode1
Capabilities of GPT-4 on Medical Challenge ProblemsCode1
Exploring the State of the Art in Legal QA SystemsCode1
BERT for Joint Intent Classification and Slot FillingCode1
FaVIQ: FAct Verification from Information-seeking QuestionsCode1
Cephalo: Multi-Modal Vision-Language Models for Bio-Inspired Materials Analysis and DesignCode1
Causality-aware Concept Extraction based on Knowledge-guided PromptingCode1
AllenNLP: A Deep Semantic Natural Language Processing PlatformCode1
ChatLog: Carefully Evaluating the Evolution of ChatGPT Across TimeCode1
Efficient Long Sequence Modeling via State Space Augmented TransformerCode1
A Surprisingly Robust Trick for Winograd Schema ChallengeCode1
Efficient Long-Text Understanding with Short-Text ModelsCode1
Benchmarking Robustness of Machine Reading Comprehension ModelsCode1
CLINE: Contrastive Learning with Semantic Negative Examples for Natural Language UnderstandingCode1
Active Learning by Acquiring Contrastive ExamplesCode1
A Dataset for Statutory Reasoning in Tax Law Entailment and Question AnsweringCode1
A Survey of RWKVCode1
Generative Biomedical Entity Linking via Knowledge Base-Guided Pre-training and Synonyms-Aware Fine-tuningCode1
CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from TextCode1
Efficient Self-supervised Learning with Contextualized Target Representations for Vision, Speech and LanguageCode1
On the Importance of Effectively Adapting Pretrained Language Models for Active LearningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HNNAccuracy90Unverified
2BERT-large 340MAccuracy78.3Unverified
3UDSSM-II (ensemble)Accuracy78.3Unverified
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