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
SV-LLM: An Agentic Approach for SoC Security Verification using Large Language Models—0
SymGPT: Auditing Smart Contracts via Combining Symbolic Execution with Large Language Models—0
Synergizing Machine Learning & Symbolic Methods: A Survey on Hybrid Approaches to Natural Language Processing—0
Syntactic Structure Distillation Pretraining For Bidirectional Encoders—0
Syntax-Infused Transformer and BERT models for Machine Translation and Natural Language Understanding—0
Synthesize, Partition, then Adapt: Eliciting Diverse Samples from Foundation Models—0
TableFormer: Robust Transformer Modeling for Table-Text Encoding—0
TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning—0
TALKPLAY: Multimodal Music Recommendation with Large Language Models—0
Taming the Beast: Learning to Control Neural Conversational Models—0
TapWeight: Reweighting Pretraining Objectives for Task-Adaptive Pretraining—0
Targeted Adversarial Training for Natural Language Understanding—0
Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM—0
Target Model Agnostic Adversarial Attacks with Query Budgets on Language Understanding Models—0
Task-adaptive Pre-training and Self-training are Complementary for Natural Language Understanding—0
Task Calibration: Calibrating Large Language Models on Inference Tasks—0
Task Selection Policies for Multitask Learning—0
Teach Me How to Learn: A Perspective Review towards User-centered Neuro-symbolic Learning for Robotic Surgical Systems—0
Team Flow at DRC2022: Pipeline System for Travel Destination Recommendation Task in Spoken Dialogue—0
Team Solomon at SemEval-2020 Task 4: Be Reasonable: Exploiting Large-scale Language Models for Commonsense Reasoning—0
TED: Accelerate Model Training by Internal Generalization—0
Temporal Embeddings and Transformer Models for Narrative Text Understanding—0
Temporal Reasoning in AI systems—0
Temporal Relation Extraction with a Graph-Based Deep Biaffine Attention Model—0
TESS: A Multi-intent Parser for Conversational Multi-Agent Systems with Decentralized Natural Language Understanding Models—0
TexSmart: A System for Enhanced Natural Language Understanding—0
TextGraphs-16 Natural Language Premise Selection Task: Zero-Shot Premise Selection with Prompting Generative Language Models—0
Text Is Not All You Need: Multimodal Prompting Helps LLMs Understand Humor—0
GeoHard: Towards Measuring Class-wise Hardness through Modelling Class Semantics—0
Textual Entailment Recognition with Semantic Features from Empirical Text Representation—0
Textual Inference and Meaning Representation in Human Robot Interaction—0
The Dark Side of Human Feedback: Poisoning Large Language Models via User Inputs—0
The Dark Side of the Language: Pre-trained Transformers in the DarkNet—0
The Dark Side of the Language: Pre-trained Transformers in the DarkNet—0
The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design—0
The Effectiveness of Intermediate-Task Training for Code-Switched Natural Language Understanding—0
The Effect of Data Ordering in Image Classification—0
The Impacts of Unanswerable Questions on the Robustness of Machine Reading Comprehension Models—0
The Inductive Bias of In-Context Learning: Rethinking Pretraining Example Design—0
The Limits of ChatGPT in Extracting Aspect-Category-Opinion-Sentiment Quadruples: A Comparative Analysis—0
The Massively Multilingual Natural Language Understanding 2022 (MMNLU-22) Workshop and Competition—0
The Microsoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understanding—0
The Negochat Corpus of Human-agent Negotiation Dialogues—0
The NLP Cookbook: Modern Recipes for Transformer based Deep Learning Architectures—0
The Singleton Fallacy: Why Current Critiques of Language Models Miss the Point—0
The state-of-the-art in web-scale semantic information processing for cloud computing—0
The Twins Corpus of Museum Visitor Questions—0
The Unreasonable Effectiveness of the Baseline: Discussing SVMs in Legal Text Classification—0
The Unstoppable Rise of Computational Linguistics in Deep Learning—0
Things not Written in Text: Exploring Spatial Commonsense from Visual Signals—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