Mental Health Disorders continue plaguing humans worldwide. Aggravating this situation is the severe shortage of qualified and competent mental health professionals (MHPs), which underlines the need for developing Virtual Assistants (VAs) that can assist MHPs. The data+ML for automation can come from platforms that allow visiting and posting messages in peer-to-peer anonymous manner for sharing their experiences (frequently stigmatized) and seeking support. In this paper, we propose a VA that can act as the first point of contact and comfort for mental health patients. We curate a dataset, Motivational VA: MotiVAte comprising of 7k dyadic conversations collected from a peer-to-peer support platform. The system employs two mechanisms: (i) Mental Illness Classification: an attention based BERT classifier that outputs the mental disorder category out of the 4 categories, viz., Major Depressive Disorder (MDD), Anxiety, Obsessive Compulsive Disorder (OCD) and Post-traumatic Stress Disorder (PTSD), based on the input ongoing dialog between the support seeker and the VA; and (ii) Mental Illness Conditioned Motivational Dialogue Generation (MI-MDG): a sentiment driven Reinforcement Learning (RL) based motivational response generator. The empirical evaluation demonstrates the system capability by way of outperforming several baselines.

This research work addresses a new multi-class classification task (fifth task) provided at the fifth Social Media Mining for Health Applications (SMM4H) workshop. This automatic tweet classification task involves distinguishing three classes of tweets that mention birth defects. We propose a novel two view based CNN-BiGRU based architectures for this task. Experimental evaluation of our proposed architecture over the validation set gives encouraging results as it improves by approximately 7% over our single view model for the fifth task. Code of our proposed framework is made available on Github

In this paper, we present the IIIT Bhagalpur and IIT Patna team’s effort to solve the three shared tasks namely, CL-SciSumm 2020, CL-LaySumm 2020, LongSumm 2020 at SDP 2020. The theme of these tasks is to generate medium-scale, lay and long summaries, respectively, for scientific articles. For the first two tasks, unsupervised systems are developed, while for the third one, we develop a supervised system.The performances of all the systems were evaluated on the associated datasets with the shared tasks in term of well-known ROUGE metric.

In this paper, we present IIITBH team’s effort to solve the second shared task of the 6th Workshop on Noisy User-generated Text (W-NUT)i.e Identification of informative COVID-19 English Tweets. The central theme of the task is to develop a system that automatically identify whether an English Tweet related to the novel coronavirus (COVID-19) is Informative or not. Our approach is based on exploiting semantic information from both max pooling and average pooling, to this end we propose two models.