Neural Language Modeling for Named Entity Recognition

Proceedings of the 28th International Conference on Computational Linguistics, pages 696–709 Barcelona, Spain (Online), December 8-13, 2020 696 Identifying Depressive Symptoms from Tweets: Figurative Language Enabled Multitask Learning Framework WARNING: This paper contains examples which are depressive in nature. Shweta Yadav∗, Jainish Chauhan†, Joy Prakash Sain‡, Krishnaprasad Thirunarayan‡ Amit Sheth§, and Jeremiah Schumm‡ ∗ LHNCBC, U.S. National Library of Medicine, MD, USA †Indian Institute of Technology Gandhinagar, India ‡ Wright State University, OH, USA §University of South Carolina, SC, USA ∗shweta.shweta@nih.gov, †chauhan.jainish@iitgn.ac.in, §AMIT@sc.edu ‡{sain.9,t.k.prasad,jeremiah.schumm}@wright.edu Abstract Existing studies on using social media for deriving mental health status of users focus on the depression detection task. However, for case management and referral to psychiatrists, health- care workers require practical and scalable depressive disorder screening and triage system. This study aims to design and evaluate a decision support system (DSS) to reliably determine the depressive triage level by capturing fine-grained depressive symptoms expressed in user tweets through the emulation of Patient Health Questionnaire-9 (PHQ-9) that is routinely used in clin- ical practice. The reliable detection of depressive symptoms from tweets is challenging because the 280-character limit on tweets incentivizes the use of creative artifacts in the utterances and figurative usage contributes to effective expression. We propose a novel BERT based robust multi-task learning framework to accurately identify the depressive symptoms using the auxil- iary task of figurative usage detection. Specifically, our proposed novel task sharing mechanism, co-task aware attention, enables automatic selection of optimal information across the BERT lay- ers and tasks by soft-sharing of parameters. Our results show that modeling figurative usage can demonstrably improve the model’s robustness and reliability for distinguishing the depression symptoms. 1 Introduction The recent survey conducted by WHO shows that a total 322 million people in the world are living with depression. At its most severe, depression can lead to suicide and is responsible for 850, 000 deaths every year (WHO and others, 2017). Early detection and appropriate treatment can encourage remission and prevent relapse (Halfin, 2007). However, the stigma coupled with the depression makes patients reluctant to seek support or provide truthful answers to physicians (Haselton et al., 2015). Additionally, clinical diagnosis is dependent on the self-reports of the patient’s behavior, which requires them to reflect and re- call from the past, that may have obscured over time. In contrast, social media offers unique platform for people to share their experiences in the moment, express emotions and stress in their raw intensity, and seek social and emotional support for resilience. As such, the depression studies based on social media offer unique advantages over scheduled surveys or interviews (Coppersmith et al., 2014; De Choud- hury and De, 2014; Manikonda and De Choudhury, 2017; De Choudhury et al., 2016). Social media self-narratives contain large amounts of implicit and reliable information expressed in real-time, that are essential for practitioners to glean and understand user’s behavior outside of the controlled clinical environment. Majority of these existing studies have formulated the social media depression detection task as a binary classification problem …

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