Sarcasm primarily involves saying something but “meaning the opposite” or “meaning something completely different” in order to convey a particular tone or mood. In both the above cases, the “meaning” is reflected by the communicative intention of the speaker, known as dialogue acts. In this paper, we seek to investigate a novel phenomenon of analyzing sarcasm in the context of dialogue acts with the hypothesis that the latter helps to understand the former better. Toward this aim, we extend the multi-modal MUStARD dataset to enclose dialogue acts for each dialogue. To demonstrate the utility of our hypothesis, we develop a dialogue act-aided multi-modal transformer network for sarcasm identification (MM-SARDAC), leveraging interrelation between these tasks. In addition, we introduce an order-infused, multi-modal infusion mechanism into our proposed model, which allows for a more intuitive combined modality representation by selectively focusing on relevant modalities in an ordered manner. Extensive empirical results indicate that dialogue act-aided sarcasm identification achieved better performance compared to performing sarcasm identification alone. The dataset and code are available at https://github.com/mohit2b/MM-SARDAC.

Over the last few years, artificial intelligence-based clinical assistance has gained immense popularity and demand in telemedicine, including automatic disease diagnosis. Patients often describe their signs and symptoms to doctors using visual aids, which provide vital evidence for identifying a medical condition. In addition to learning from our experiences, we learn from well-established theories/ knowledge. With the motivation of leveraging visual cues and medical knowledge, we propose a transformer-based, knowledge-infused multi-modal medical dialogue generation (KI-MMDG) framework. In addition, we present a discourse-aware image identifier (DII) that recognizes signs and their severity by leveraging the current conversation context in addition to the image of the signs. We first curate an empathy and severity-aware multi-modal medical dialogue (ES-MMD) corpus in English, which is annotated with intent, symptoms, and visual signs with severity information. Experimental results show the superior performance of the proposed KI-MMDG model over uni-modal and non-knowledge infused generative models, demonstrating the importance of visual signs and knowledge infusion in symptom investigation and diagnosis. We also observed that the DII model surpasses the existing state-of-the-art model by 7.84%, indicating the crucial significance of dialogue context for identifying a sign image surfaced during conversations. The code and dataset are available at https://github.com/NLP-RL/KI-MMDG.

The advancement of Artificial Intelligence is pivotal in reshaping healthcare, enhancing diagnostic precision, and facilitating personalized treatment strategies. One major challenge for healthcare professionals is quickly navigating through long clinical documents to provide timely and effective solutions. Doctors often struggle to draw quick conclusions from these extensive documents. To address this issue and save time for healthcare professionals, an effective summarization model is essential. Most current models assume the data is only text-based. However, patients often include images of their medical conditions in clinical documents. To effectively summarize these multimodal documents, we introduce EDI-Summ, an innovative Image-Guided Encoder-Decoder Model. This model uses modality-aware contextual attention on the encoder and an image cross-attention mechanism on the decoder, enhancing the BART base model to create detailed visual-guided summaries. We have tested our model extensively on three multimodal clinical benchmarks involving multimodal question and dialogue summarization tasks. Our analysis demonstrates that EDI-Summ outperforms state-of-the-art large language and vision-aware models in these summarization tasks. Disclaimer: The work includes vivid medical illustrations, depicting the essential aspects of the subject matter.

Task-oriented conversational agents are gaining immense popularity and success in a wide range of tasks, from flight ticket booking to online shopping. However, the existing systems presume that end-users will always have a pre-determined and servable task goal, which results in dialogue failure in hostile scenarios, such as goal unavailability. On the other hand, human agents accomplish users’ tasks even in a large number of goal unavailability scenarios by persuading them towards a very similar and servable goal. Motivated by the limitation, we propose and build a novel end-to-end multi-modal persuasive dialogue system incorporated with a personalized persuasive module aided goal controller and goal persuader. The goal controller recognizes goal conflicting/unavailability scenarios and formulates a new goal, while the goal persuader persuades users using a personalized persuasive strategy identified through dialogue context. We also present a novel automatic evaluation metric called Persuasiveness Measurement Rate (PMeR) for quantifying the persuasive capability of a conversational agent. The obtained improvements (both quantitative and qualitative) firmly establish the superiority and need of the proposed context-guided, personalized persuasive virtual agent over existing traditional task-oriented virtual agents. Furthermore, we also curated a multi-modal persuasive conversational dialogue corpus annotated with intent, slot, sentiment, and dialogue act for e-commerce domain.