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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 1451–1500 of 1978 papers

TitleStatusHype
Leveraging Large Language Models for Semantic Query Processing in a Scholarly Knowledge Graph—0
Leveraging Semantic Representations Combined with Contextual Word Representations for Recognizing Textual Entailment in Vietnamese—0
Leveraging Sentence-level Information with Encoder LSTM for Semantic Slot Filling—0
Leveraging Syntactic Constructions for Metaphor Identification—0
Lexicon-Free Conversational Speech Recognition with Neural Networks—0
LIDSNet: A Lightweight on-device Intent Detection model using Deep Siamese Network—0
LightMBERT: A Simple Yet Effective Method for Multilingual BERT Distillation—0
Lightweight Transformers for Conversational AI—0
LiLiuM: eBay's Large Language Models for e-commerce—0
LINGO : Visually Debiasing Natural Language Instructions to Support Task Diversity—0
Linguistic features for sentence difficulty prediction in ABSA—0
LLaMAntino: LLaMA 2 Models for Effective Text Generation in Italian Language—0
LLM+AL: Bridging Large Language Models and Action Languages for Complex Reasoning about Actions—0
LLM-assisted Vector Similarity Search—0
LLM-based Weak Supervision Framework for Query Intent Classification in Video Search—0
LLM for SoC Security: A Paradigm Shift—0
LLM-GAN: Construct Generative Adversarial Network Through Large Language Models For Explainable Fake News Detection—0
LLMs' Reading Comprehension Is Affected by Parametric Knowledge and Struggles with Hypothetical Statements—0
Local Structure Matters Most in Most Languages—0
Local Structure Matters Most: Perturbation Study in NLU—0
Logical analysis of natural language semantics to solve the problem of computer understanding—0
Logical forms complement probability in understanding language model (and human) performance—0
Logically Consistent Language Models via Neuro-Symbolic Integration—0
Logic, Language, and Calculus—0
Logic Pre-Training of Language Models—0
Long Short-Term Memory Over Tree Structures—0
LoNLI: An Extensible Framework for Testing Diverse Logical Reasoning Capabilities for NLI—0
Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling—0
LoRTA: Low Rank Tensor Adaptation of Large Language Models—0
LoSparse: Structured Compression of Large Language Models based on Low-Rank and Sparse Approximation—0
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
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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