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 551–575 of 1978 papers

TitleStatusHype
A Survey on Temporal Reasoning for Temporal Information Extraction from Text (Extended Abstract)—0
A Multi-layer LSTM-based Approach for Robot Command Interaction Modeling—0
Evaluating Cross-Lingual Transfer Learning Approaches in Multilingual Conversational Agent Models—0
A Survey on Neural Open Information Extraction: Current Status and Future Directions—0
A Modular and Adaptive System for Business Email Compromise Detection—0
CONDA: a CONtextual Dual-Annotated dataset for in-game toxicity understanding and detection—0
A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly—0
Evaluate On-the-job Learning Dialogue Systems and a Case Study for Natural Language Understanding—0
Evaluating Large Language Models on Financial Report Summarization: An Empirical Study—0
Evaluation Metrics for Machine Reading Comprehension: Prerequisite Skills and Readability—0
Compressing Pre-trained Transformers via Low-Bit NxM Sparsity for Natural Language Understanding—0
A Survey on Knowledge-Oriented Retrieval-Augmented Generation—0
A Survey of Sustainability in Large Language Models: Applications, Economics, and Challenges—0
Comprehensive Multi-Dataset Evaluation of Reading Comprehension—0
A Metamodel and Framework for Artificial General Intelligence From Theory to Practice—0
Comprehensive Event Representations using Event Knowledge Graphs and Natural Language Processing—0
Complex-Valued Neural Networks for Data-Driven Signal Processing and Signal Understanding—0
Competence-based Question Generation—0
A Survey of Reinforcement Learning Informed by Natural Language—0
ChemGraph: An Agentic Framework for Computational Chemistry Workflows—0
Error Detection in Large-Scale Natural Language Understanding Systems Using Transformer Models—0
Comparing Test Sets with Item Response Theory—0
A Survey of Knowledge Enhanced Pre-trained Language Models—0
CommonsenseQA 2.0: Exposing the Limits of AI through Gamification—0
A survey of joint intent detection and slot-filling models in natural language understanding—0
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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