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PALI-NLP at SemEval-2022 Task 4: Discriminative Fine-tuning of Deep Transformers for Patronizing and Condescending Language Detection ...
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Compilable Neural Code Generation with Compiler Feedback ...
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GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records ...
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Mental Disorders on Online Social Media Through the Lens of Language and Behaviour: Analysis and Visualisation ...
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FALCON: Fast Visual Concept Learning by Integrating Images, Linguistic descriptions, and Conceptual Relations ...
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Rethinking Offensive Text Detection as a Multi-Hop Reasoning Problem ...
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Chain-based Discriminative Autoencoders for Speech Recognition ...
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Unsupervised word-level prosody tagging for controllable speech synthesis ...
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AI-Based Automated Speech Therapy Tools for persons with Speech Sound Disorders: A Systematic Literature Review ...
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UBERT: A Novel Language Model for Synonymy Prediction at Scale in the UMLS Metathesaurus ...
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gTLO: A Generalized and Non-linear Multi-Objective Deep Reinforcement Learning Approach ...
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Inferring Lexicographically-Ordered Rewards from Preferences ...
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Cross-Platform Difference in Facebook and Text Messages Language Use: Illustrated by Depression Diagnosis ...
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Abstract:
How does language differ across one's Facebook status updates vs. one's text messages (SMS)? In this study, we show how Facebook and SMS use differs in psycho-linguistic characteristics and how these differences drive downstream analyses with an illustration of depression diagnosis. We use a sample of consenting participants who shared Facebook status updates, SMS data, and answered a standard psychological depression screener. We quantify domain differences using psychologically driven lexical methods and find that language on Facebook involves more personal concerns, experiences, and content features while the language in SMS contains more informal and style features. Next, we estimate depression from both text domains, using a depression model trained on Facebook data, and find a drop in accuracy when predicting self-reported depression assessments from the SMS-based depression estimates. Finally, we evaluate a simple domain adaption correction based on words driving the cross-platform differences and ... : 5 pages, 1 figure ...
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Keyword:
Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences; Human-Computer Interaction cs.HC; Machine Learning cs.LG
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URL: https://arxiv.org/abs/2202.01802 https://dx.doi.org/10.48550/arxiv.2202.01802
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Improving Word Translation via Two-Stage Contrastive Learning ...
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New Core-Guided and Hitting Set Algorithms for Multi-Objective Combinatorial Optimization ...
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Research on Dual Channel News Headline Classification Based on ERNIE Pre-training Model ...
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Improving Zero-Shot Event Extraction via Sentence Simplification ...
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Language Models Explain Word Reading Times Better Than Empirical Predictability ...
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