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 901–950 of 1978 papers

TitleStatusHype
Cross-lingual Adaption Model-Agnostic Meta-Learning for Natural Language Understanding—0
Improved and Efficient Conversational Slot Labeling through Question Answering—0
Improved and Efficient Conversational Slot Labeling through Question Answering—0
Implicit Entity Linking in Tweets—0
Cross-Language Assessment of Mathematical Capability of ChatGPT—0
EvoText: Enhancing Natural Language Generation Models via Self-Escalation Learning for Up-to-Date Knowledge and Improved Performance—0
Creation Of A ChatBot Based On Natural Language Proccesing For Whatsapp—0
Improving Natural Language Understanding by Reverse Mapping Bytepair Encoding—0
Imagination-Augmented Natural Language Understanding—0
Image Captioning at Will: A Versatile Scheme for Effectively Injecting Sentiments into Image Descriptions—0
Improving Romanian BioNER Using a Biologically Inspired System—0
Improving Small-Scale Large Language Models Function Calling for Reasoning Tasks—0
Augmenting Named Entity Recognition with Commonsense Knowledge—0
Image-based Natural Language Understanding Using 2D Convolutional Neural Networks—0
CLIPPO: Image-and-Language Understanding from Pixels Only—0
Improving the Language Understanding Capabilities of Large Language Models Using Reinforcement Learning—0
I like fish, especially dolphins: Addressing Contradictions in Dialogue Modeling—0
Explicit Contextual Semantics for Text Comprehension—0
Audrey: A Personalized Open-Domain Conversational Bot—0
An Approach for Auto Generation of Labeling Functions for Software Engineering Chatbots—0
ADEPT: An Adjective-Dependent Plausibility Task—0
IDPG: An Instance-Dependent Prompt Generation Method—0
Incorporating Explicit Knowledge in Pre-trained Language Models for Passage Re-ranking—0
IDPG: An Instance-Dependent Prompt Generation Method—0
Incorporating Syntax and Frame Semantics in Neural Network for Machine Reading Comprehension—0
Identifying Motion Entities in Natural Language and A Case Study for Named Entity Recognition—0
Counteracts: Testing Stereotypical Representation in Pre-trained Language Models—0
RNN based Incremental Online Spoken Language Understanding—0
Identifying Bengali Multiword Expressions using Semantic Clustering—0
Cost-Based Goal Recognition Meets Deep Learning—0
A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLU—0
Identifying and Resolving Annotation Changes for Natural Language Understanding—0
Identification of Entailment and Contradiction Relations between Natural Language Sentences: A Neurosymbolic Approach—0
Corpus Generation for Voice Command in Smart Home and the Effect of Speech Synthesis on End-to-End SLU—0
ICH-Qwen: A Large Language Model Towards Chinese Intangible Cultural Heritage—0
Industry Scale Semi-Supervised Learning for Natural Language Understanding—0
Task-specific Compression for Multi-task Language Models using Attribution-based Pruning—0
Inferring Compact Representations for Efficient Natural Language Understanding of Robot Instructions—0
IBADR: an Iterative Bias-Aware Dataset Refinement Framework for Debiasing NLU models—0
InfoPrompt: Information-Theoretic Soft Prompt Tuning for Natural Language Understanding—0
Core Building Blocks: Next Gen Geo Spatial GPT Application—0
COREALMLIB: An ALM Library Translated from the Component Library—0
Attentive Task-Agnostic Meta-Learning for Few-Shot Text Classification—0
A Deep Learning System for Domain-specific Speech Recognition—0
A Cohesive Distillation Architecture for Neural Language Models—0
HyperPrompt: Prompt-based Task-Conditioning of Transformers—0
Hyperparameter-free Continuous Learning for Domain Classification in Natural Language Understanding—0
CopyBERT: A Unified Approach to Question Generation with Self-Attention—0
Hypernymy Detection for Low-Resource Languages via Meta Learning—0
Convo: What does conversational programming need? An exploration of machine learning interface design—0
Show:102550
← PrevPage 19 of 40Next →
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