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 1851–1900 of 1978 papers

TitleStatusHype
Lost in Inference: Rediscovering the Role of Natural Language Inference for Large Language Models—0
Low-Resource Adaptation of Neural NLP Models—0
LRC-BERT: Latent-representation Contrastive Knowledge Distillation for Natural Language Understanding—0
Machine Reading, Fast and Slow: When Do Models "Understand" Language?—0
Machine Reading, Fast and Slow: When Do Models “Understand” Language?—0
Machine Translation with Large Language Models: Prompt Engineering for Persian, English, and Russian Directions—0
MaCP: Minimal yet Mighty Adaptation via Hierarchical Cosine Projection—0
Magnitude Pruning of Large Pretrained Transformer Models with a Mixture Gaussian Prior—0
Making Language Models Robust Against Negation—0
Making Neural Machine Reading Comprehension Faster—0
Making the Most of your Model: Methods for Finetuning and Applying Pretrained Transformers—0
MaLLaM -- Malaysia Large Language Model—0
Mandarinograd: A Chinese Collection of Winograd Schemas—0
Markov Logic Networks for Situated Incremental Natural Language Understanding—0
MARRS: Multimodal Reference Resolution System—0
MasonTigers at SemEval-2024 Task 9: Solving Puzzles with an Ensemble of Chain-of-Thoughts—0
Matrix-Transformation Based Low-Rank Adaptation (MTLoRA): A Brain-Inspired Method for Parameter-Efficient Fine-Tuning—0
Maximizing Signal in Human-Model Preference Alignment—0
MCScript: A Novel Dataset for Assessing Machine Comprehension Using Script Knowledge—0
mCSQA: Multilingual Commonsense Reasoning Dataset with Unified Creation Strategy by Language Models and Humans—0
Meaning and understanding in large language models—0
Meaning Beyond Truth Conditions: Evaluating Discourse Level Understanding via Anaphora Accessibility—0
Meaning Representation of Null Instantiated Semantic Roles in FrameNet—0
Measure More, Question More: Experimental Studies on Transformer-based Language Models and Complement Coercion—0
Measuring and Mitigating Local Instability in Deep Neural Networks—0
Measuring and Reducing Gendered Correlations in Pre-trained Models—0
Med-Bot: An AI-Powered Assistant to Provide Accurate and Reliable Medical Information—0
Medical mT5: An Open-Source Multilingual Text-to-Text LLM for The Medical Domain—0
MediTOD: An English Dialogue Dataset for Medical History Taking with Comprehensive Annotations—0
Meta Auxiliary Learning for Low-resource Spoken Language Understanding—0
Meta-Learning with MAML on Trees—0
Meta-Reflection: A Feedback-Free Reflection Learning Framework—0
Meta Semantics: Towards better natural language understanding and reasoning—0
MET-Bench: Multimodal Entity Tracking for Evaluating the Limitations of Vision-Language and Reasoning Models—0
Mining Cross-Cultural Differences and Similarities in Social Media—0
Mitigating Shortcuts in Language Models with Soft Label Encoding—0
MixUp Training Leads to Reduced Overfitting and Improved Calibration for the Transformer Architecture—0
mmT5: Modular Multilingual Pre-Training Solves Source Language Hallucinations—0
Modality: logic, semantics, annotation and machine learning—0
Model-Agnostic Meta-Learning for Natural Language Understanding Tasks in Finance—0
Modeling Biological Processes for Reading Comprehension—0
Modeling Feature Representations for Affective Speech using Generative Adversarial Networks—0
Modeling meaning: computational interpreting and understanding of natural language fragments—0
Modern French Poetry Generation with RoBERTa and GPT-2—0
ModernGBERT: German-only 1B Encoder Model Trained from Scratch—0
MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation—0
MoEC: Mixture of Expert Clusters—0
MolX: Enhancing Large Language Models for Molecular Learning with A Multi-Modal Extension—0
Monash-Summ@LongSumm 20 SciSummPip: An Unsupervised Scientific Paper Summarization Pipeline—0
Monte-Carlo Planning and Learning with Language Action Value Estimates—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