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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1001–1050 of 1978 papers

TitleStatusHype
PathGPT: Leveraging Large Language Models for Personalized Route Generation—0
PathologyBERT -- Pre-trained Vs. A New Transformer Language Model for Pathology Domain—0
Pathology Extraction from Chest X-Ray Radiology Reports: A Performance Study—0
Pay Attention when you Pay the Bills. A Multilingual Corpus with Dependency-based and Semantic Annotation of Collocations.—0
Pay More Attention - Neural Architectures for Question-Answering—0
PerSHOP -- A Persian dataset for shopping dialogue systems modeling—0
PersianLLaMA: Towards Building First Persian Large Language Model—0
Personality Trait Detection Using Bagged SVM over BERT Word Embedding Ensembles—0
Personalized Federated Fine-tuning for Heterogeneous Data: An Automatic Rank Learning Approach via Two-Level LoRA—0
Personalized Query Rewriting in Conversational AI Agents—0
Pipeline Analysis for Developing Instruct LLMs in Low-Resource Languages: A Case Study on Basque—0
Point Discriminative Learning for Data-efficient 3D Point Cloud Analysis—0
Points, Paths, and Playscapes: Large-scale Spatial Language Understanding Tasks Set in the Real World—0
Potential, Challenges and Future Directions for Deep Learning in Prognostics and Health Management Applications—0
Predicting Causes of Reformulation in Intelligent Assistants—0
Predictor-Corrector Enhanced Transformers with Exponential Moving Average Coefficient Learning—0
Benchmarking Machine Reading Comprehension: A Psychological Perspective—0
Pre-trained Contextual Embedding of Source Code—0
Pretrained Semantic Speech Embeddings for End-to-End Spoken Language Understanding via Cross-Modal Teacher-Student Learning—0
Pretraining for Conditional Generation with Pseudo Self Attention—0
Pre-training for Spoken Language Understanding with Joint Textual and Phonetic Representation Learning—0
Pre-training Text-to-Text Transformers to Write and Reason with Concepts—0
Pretraining without Wordpieces: Learning Over a Vocabulary of Millions of Words—0
Prior Knowledge Driven Label Embedding for Slot Filling in Natural Language Understanding—0
Prior Omission of Dissimilar Source Domain(s) for Cost-Effective Few-Shot Learning—0
Natural Language Understanding with Privacy-Preserving BERT—0
Privacy-Preserving Language Model Inference with Instance Obfuscation—0
Probabilistic Reasoning via Deep Learning: Neural Association Models—0
Probing Position-Aware Attention Mechanism in Long Document Understanding—0
Probing Taxonomic and Thematic Embeddings for Taxonomic Information—0
ProcTHOR: Large-Scale Embodied AI Using Procedural Generation—0
Programming in Natural Language with fuSE: Synthesizing Methods from Spoken Utterances Using Deep Natural Language Understanding—0
Program Synthesis Benchmark for Visual Programming in XLogoOnline Environment—0
Pro-KD: Progressive Distillation by Following the Footsteps of the Teacher—0
Prompt-based Personality Profiling: Reinforcement Learning for Relevance Filtering—0
Prompt Conditioned VAE: Enhancing Generative Replay for Lifelong Learning in Task-Oriented Dialogue—0
PromptExp: Multi-granularity Prompt Explanation of Large Language Models—0
Role-playing Prompt Framework: Generation and Evaluation—0
Proposition from the Perspective of Chinese Language: A Chinese Proposition Classification Evaluation Benchmark—0
Proto: A Neural Cocktail for Generating Appealing Conversations—0
Pseudo Knowledge Distillation: Towards Learning Optimal Instance-specific Label Smoothing Regularization—0
Pseudo-Masked Language Models for Unified Language Model Pre-Training—0
PTP: Boosting Stability and Performance of Prompt Tuning with Perturbation-Based Regularizer—0
PURPLE: Making a Large Language Model a Better SQL Writer—0
Pushing the boundary on Natural Language Inference—0
QADiscourse - Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines—0
Quantifying the Capabilities of LLMs across Scale and Precision—0
Quantization-Aware and Tensor-Compressed Training of Transformers for Natural Language Understanding—0
Query Tracking for E-commerce Conversational Search: A Machine Comprehension Perspective—0
Question Answering Survey: Directions, Challenges, Datasets, Evaluation Matrices—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