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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

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Showing 651–700 of 1978 papers

TitleStatusHype
Low-resource Bilingual Dialect Lexicon Induction with Large Language ModelsCode0
Scaling Transformer to 1M tokens and beyond with RMTCode2
Exploring the State of the Art in Legal QA SystemsCode1
LINGO : Visually Debiasing Natural Language Instructions to Support Task Diversity—0
User Adaptive Language Learning Chatbots with a Curriculum—0
Towards preserving word order importance through Forced InvalidationCode0
Uncertainty-Aware Natural Language Inference with Stochastic Weight AveragingCode0
Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4Code1
How to Design Translation Prompts for ChatGPT: An Empirical StudyCode5
Form-NLU: Dataset for the Form Natural Language UnderstandingCode1
PEACH: Pre-Training Sequence-to-Sequence Multilingual Models for Translation with Semi-Supervised Pseudo-Parallel Document GenerationCode0
DERA: Enhancing Large Language Model Completions with Dialog-Enabled Resolving Agents—0
Evaluation of ChatGPT for NLP-based Mental Health Applications—0
ChatGPT as a Factual Inconsistency Evaluator for Text Summarization—0
Error Analysis Prompting Enables Human-Like Translation Evaluation in Large Language ModelsCode1
SwissBERT: The Multilingual Language Model for SwitzerlandCode1
Is BERT Blind? Exploring the Effect of Vision-and-Language Pretraining on Visual Language UnderstandingCode1
Capabilities of GPT-4 on Medical Challenge ProblemsCode1
PanGu-Σ: Towards Trillion Parameter Language Model with Sparse Heterogeneous Computing—0
CTRAN: CNN-Transformer-based Network for Natural Language UnderstandingCode1
A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models—0
A Deep Learning System for Domain-specific Speech Recognition—0
Trustera: A Live Conversation Redaction System—0
Can ChatGPT Replace Traditional KBQA Models? An In-depth Analysis of the Question Answering Performance of the GPT LLM FamilyCode1
Let's Get Personal: Personal Questions Improve SocialBot Performance in the Alexa Prize—0
Comprehensive Event Representations using Event Knowledge Graphs and Natural Language Processing—0
A Hybrid Architecture for Out of Domain Intent Detection and Intent DiscoveryCode1
Model-Agnostic Meta-Learning for Natural Language Understanding Tasks in Finance—0
MathPrompter: Mathematical Reasoning using Large Language ModelsCode1
Understanding Natural Language Understanding Systems. A Critical Analysis—0
How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Tasks—0
A Persian Benchmark for Joint Intent Detection and Slot FillingCode1
Knowledge-enhanced Visual-Language Pre-training on Chest Radiology ImagesCode1
Few-shot Multimodal Multitask Multilingual Learning—0
Bag of Tricks for Effective Language Model Pretraining and Downstream Adaptation: A Case Study on GLUE—0
Scalable Prompt Generation for Semi-supervised Learning with Language Models—0
Role of Bias Terms in Dot-Product Attention—0
Is Multimodal Vision Supervision Beneficial to Language?Code0
EvoText: Enhancing Natural Language Generation Models via Self-Escalation Learning for Up-to-Date Knowledge and Improved Performance—0
Reliable Natural Language Understanding with Large Language Models and Answer Set Programming—0
GLADIS: A General and Large Acronym Disambiguation BenchmarkCode1
The Impacts of Unanswerable Questions on the Robustness of Machine Reading Comprehension Models—0
Can We Use Probing to Better Understand Fine-tuning and Knowledge Distillation of the BERT NLU?—0
Call for Papers -- The BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpusCode1
ViDeBERTa: A powerful pre-trained language model for VietnameseCode1
Probing Taxonomic and Thematic Embeddings for Taxonomic Information—0
Neural Architecture Search: Insights from 1000 PapersCode0
A Cohesive Distillation Architecture for Neural Language Models—0
Counteracts: Testing Stereotypical Representation in Pre-trained Language Models—0
Cross-Model Comparative Loss for Enhancing Neuronal Utility in Language UnderstandingCode0
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All datasetsPDP60STREUSLELexGLUEDialoGLUE fewshotDialoGLUE fullGLUE

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HNNAccuracy90—Unverified
2BERT-large 340MAccuracy78.3—Unverified
3UDSSM-II (ensemble)Accuracy78.3—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