Politeness is a multifaceted concept influenced by individual perceptions of what is considered polite or impolite. With this objective, we introduce a novel task - Politeness Cause Elicitation and Intensity Tagging (PCEIT). This task focuses on conversations and aims to identify the underlying reasons behind the use of politeness and gauge the degree of politeness conveyed. To address this objective, we create HING-POEM, a new conversational dataset in Hinglish (a blend of Hindi and English) for mental health and legal counseling of crime victims. The rationale for the domain selection lies in the paramount importance of politeness in mental health and legal counseling of crime victims to ensure a compassionate and cordial atmosphere for them. We enrich the HING-POEM dataset by annotating it with politeness labels, politeness causal spans, and intensity values at the level of individual utterances. In the context of the introduced PCEIT task, we present PAANTH (Politeness CAuse ElicitAion and INtensity Tagging in Hinglish), a comprehensive framework based on Contextual Enhanced Attentive Convolution Transformer. We conduct extensive quantitative and qualitative evaluations to establish the effectiveness of our proposed approach using the newly constructed dataset. Our approach is compared against state-of-the-art baselines, and these analyses help demonstrate the superiority of our method.

Due to its growing impact on public opinion, hate speech on social media has garnered increased attention. While automated methods for identifying hate speech have been presented in the past, they have mostly been limited to analyzing textual content. The interpretability of such models has received very little attention, despite the social and legal consequences of erroneous predictions. In this work, we present a novel problem of Distress Identification and Cause Extraction (DICE) from multimodal online posts. We develop a multi-task deep framework for the simultaneous detection of distress content and identify connected causal phrases from the text using emotional information. The emotional information is incorporated into the training process using a zero-shot strategy, and a novel mechanism is devised to fuse the features from the multimodal inputs. Furthermore, we introduce the first-of-its-kind Distress and Cause annotated Multimodal (DCaM) dataset of 20,764 social media posts. We thoroughly evaluate our proposed method by comparing it to several existing benchmarks. Empirical assessment and comprehensive qualitative analysis demonstrate that our proposed method works well on distress detection and cause extraction tasks, improving F1 and ROS scores by 1.95% and 3%, respectively, relative to the best-performing baseline. The code and the dataset can be accessed from the following link: https://www.iitp.ac.in/~ai-nlp-ml/resources.html\#DICE.