The onset of the COVID-19 pandemic has brought the mental health of people under risk. Social counselling has gained remarkable significance in this environment. Unlike general goal-oriented dialogues, a conversation between a patient and a therapist is considerably implicit, though the objective of the conversation is quite apparent. In such a case, understanding the intent of the patient is imperative in providing effective counselling in therapy sessions, and the same applies to a dialogue system as well. In this work, we take forward a small but an important step in the development of an automated dialogue system for mental-health counselling. We develop a novel dataset, named HOPE, to provide a platform for the dialogue-act classification in counselling conversations. We identify the requirement of such conversation and propose twelve domain-specific dialogue-act (DAC) labels. We collect ~ 12.9K utterances from publicly-available counselling session videos on YouTube, extract their transcripts, clean, and annotate them with DAC labels. Further, we propose SPARTA, a transformer-based architecture with a novel speaker- and time-aware contextual learning for the dialogue-act classification. Our evaluation shows convincing performance over several baselines, achieving state-of-the-art on HOPE. We also supplement our experiments with extensive empirical and qualitative analyses of SPARTA.

Reference resolution is an important problem that has enormous practical implications in daily life, for example in recovering the intended meaning in communication when the environment is noisy (acoustic noise in the spoken channel, or clutter / occlusion in the visual world). Recent literature indicates that cross-modal processing of all the contributive modalities improves the reference resolution in such settings. In this paper, we investigate the contribution of the eye-tracking methodology, a substantial but underrepresented component of face-to-face communication in NLP systems, to recover the meaning in noisy settings. We integrate gaze features into state-of-the-art language models and test the model on data where parts of the sentences are masked, mimicking noise in the acoustic channel. The results indicate that eye movements can compensate for the missing information in the situation and support communication when language and visual modality fail.