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CTRAN: CNN-Transformer-based Network for Natural Language Understanding. (arXiv:2303.10606v1 [cs.CL]) 

Toward Artificial Empathy for Human-Centered Design: A Framework. (arXiv:2303.10583v1 [cs.HC]) 

Extracting Incidents, Effects, and Requested Advice from MeToo Posts. (arXiv:2303.10573v1 [cs.CL]) 

How People Respond to the COVID-19 Pandemic on Twitter: A Comparative Analysis of Emotional Expressions from US and India. (arXiv:2303.10560v1 [cs.CL]) 

Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning. (arXiv:2303.10512v1 [cs.CL]) 

A Deep Learning System for Domain-specific speech Recognition. (arXiv:2303.10510v1 [cs.CL]) 

Is Prompt All You Need? No. A Comprehensive and Broader View of Instruction Learning. (arXiv:2303.10475v1 [cs.CL]) 

SPDF: Sparse Pre-training and Dense Fine-tuning for Large Language Models. (arXiv:2303.10464v1 [cs.LG]) 

GazeReader: Detecting Unknown Word Using Webcam for English as a Second Language (ESL) Learners. (arXiv:2303.10443v1 [cs.HC]) 

Stop Words for Processing Software Engineering Documents: Do they Matter?. (arXiv:2303.10439v1 [cs.SE]) 

NoisyHate: Benchmarking Content Moderation Machine Learning Models with Human-Written Perturbations Online. (arXiv:2303.10430v1 [cs.LG]) 

A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models. (arXiv:2303.10420v1 [cs.CL]) 

A Graph-Guided Reasoning Approach for Open-ended Commonsense Question Answering. (arXiv:2303.10395v1 [cs.CL]) 

Powerful and Extensible WFST Framework for RNN-Transducer Losses. (arXiv:2303.10384v1 [eess.AS]) 

An Empirical Study of Pre-trained Language Models in Simple Knowledge Graph Question Answering. (arXiv:2303.10368v1 [cs.CL]) 

Exploring Partial Knowledge Base Inference in Biomedical Entity Linking. (arXiv:2303.10330v1 [cs.CL]) 

Revisiting Automatic Question Summarization Evaluation in the Biomedical Domain. (arXiv:2303.10328v1 [cs.CL]) 

On the rise of fear speech in online social media. (arXiv:2303.10311v1 [cs.SI]) 

Feedback Effect in User Interaction with Intelligent Assistants: Delayed Engagement, Adaption and Drop-out. (arXiv:2303.10255v1 [cs.HC]) 

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