Explaining (Sarcastic) Utterances to Enhance Affect Understanding in Multimodal Dialogues

Conversations emerge as the primary media for exchanging ideas and conceptions. From the listener's perspective, identifying various affective qualities, such as sarcasm, humour, and emotions, is paramount for comprehending the true connotation of the emitted utterance. However, one of the major hurdles faced in learning these affect dimensions is the presence of figurative language, viz. irony, metaphor, or sarcasm. We hypothesize that any detection system constituting the exhaustive and explicit presentation of the emitted utterance would improve the overall comprehension of the dialogue. To this end, we explore the task of Sarcasm Explanation in Dialogues, which aims to unfold the hidden irony behind sarcastic utterances. We propose MOSES, a deep neural network, which takes a multimodal (sarcastic) dialogue instance as an input and generates a natural language sentence as its explanation. Subsequently, we leverage the generated explanation for various natural language understanding tasks in a conversational dialogue setup, such as sarcasm detection, humour identification, and emotion recognition. Our evaluation shows that MOSES outperforms the state-of-the-art system for SED by an average of ~2% on different evaluation metrics, such as ROUGE, BLEU, and METEOR. Further, we observe that leveraging the generated explanation advances three downstream tasks for affect classification - an average improvement of ~14% F1-score in the sarcasm detection task and ~2% in the humour identification and emotion recognition task. We also perform extensive analyses to assess the quality of the results.Comment: Accepted at AAAI 2023. 11 Pages; 14 Tables; 3 Figure

Intent Identification and Entity Extraction for Healthcare Queries in Indic Languages

Scarcity of data and technological limitations for resource-poor languages in developing countries like India poses a threat to the development of sophisticated NLU systems for healthcare. To assess the current status of various state-of-the-art language models in healthcare, this paper studies the problem by initially proposing two different Healthcare datasets, Indian Healthcare Query Intent-WebMD and 1mg (IHQID-WebMD and IHQID-1mg) and one real world Indian hospital query data in English and multiple Indic languages (Hindi, Bengali, Tamil, Telugu, Marathi and Gujarati) which are annotated with the query intents as well as entities. Our aim is to detect query intents and extract corresponding entities. We perform extensive experiments on a set of models in various realistic settings and explore two scenarios based on the access to English data only (less costly) and access to target language data (more expensive). We analyze context specific practical relevancy through empirical analysis. The results, expressed in terms of overall F1 score show that our approach is practically useful to identify intents and entities

Medical Entity Linking using Triplet Network

Entity linking (or Normalization) is an essential task in text mining that maps the entity mentions in the medical text to standard entities in a given Knowledge Base (KB). This task is of great importance in the medical domain. It can also be used for merging different medical and clinical ontologies. In this paper, we center around the problem of disease linking or normalization. This task is executed in two phases: candidate generation and candidate scoring. In this paper, we present an approach to rank the candidate Knowledge Base entries based on their similarity with disease mention. We make use of the Triplet Network for candidate ranking. While the existing methods have used carefully generated sieves and external resources for candidate generation, we introduce a robust and portable candidate generation scheme that does not make use of the hand-crafted rules. Experimental results on the standard benchmark NCBI disease dataset demonstrate that our system outperforms the prior methods by a significant margin.Comment: ClinicalNLP@NAACL 201

InteractiveIE: Towards Assessing the Strength of Human-AI Collaboration in Improving the Performance of Information Extraction

Learning template based information extraction from documents is a crucial yet difficult task. Prior template-based IE approaches assume foreknowledge of the domain templates; however, real-world IE do not have pre-defined schemas and it is a figure-out-as you go phenomena. To quickly bootstrap templates in a real-world setting, we need to induce template slots from documents with zero or minimal supervision. Since the purpose of question answering intersect with the goal of information extraction, we use automatic question generation to induce template slots from the documents and investigate how a tiny amount of a proxy human-supervision on-the-fly (termed as InteractiveIE) can further boost the performance. Extensive experiments on biomedical and legal documents, where obtaining training data is expensive, reveal encouraging trends of performance improvement using InteractiveIE over AI-only baseline.Comment: Version

Future Practicability of Android Application Development with New Android Libraries and Frameworks

Abstract: Today we are in the major technological verge and in this situation we have to choose a side from PC devices and Mobile devices. As known from the market reviews users of different ends mostly prefer Mobile devices for their daily usage and because of that mobile technology is growing at a vast range and very rapidly. And the most triggered software of this technology we all know is Android.The possibility or future scopes for Android are beyond imagination. The large and speedy growth of Android always makes Developers work in different aspects and explore more and more about this technology. In This paper I propose the future of Android Development on the prospect of Different Android Frameworks and Libraries currently available at the market which are constantly helping the developers to work more and more on this particular platform with their own styles. This paper also contains differential diagnosis of those Frameworks in respect to their usages and other different possibilities. So that it can help developer more into Android and help it grow bigger and better

Multi-Objective Few-shot Learning for Fair Classification

In this paper, we propose a general framework for mitigating the disparities of the predicted classes with respect to secondary attributes within the data (e.g., race, gender etc.). Our proposed method involves learning a multi-objective function that in addition to learning the primary objective of predicting the primary class labels from the data, also employs a clustering-based heuristic to minimize the disparities of the class label distribution with respect to the cluster memberships, with the assumption that each cluster should ideally map to a distinct combination of attribute values. Experiments demonstrate effective mitigation of cognitive biases on a benchmark dataset without the use of annotations of secondary at-tribute values (the zero-shot case) or with the use of a small number of attribute value annotations (the few-shot case)

Synthesis and Characterizations of Novel Quinoline Derivatives Having Mixed Ligand Activities at the J and I Receptors: Potential Therapeutic Efficacy Against Morphine Dependence

Based on an established 3D pharmacophore, a series of quinoline derivatives were synthesized. The opioidergic properties of these compounds were determined by a competitive binding assay using 125I-Dynorphine, 3H-DAMGO and 125I-DADLE for j, l, and d receptors, respectively. Results showed varying degree of activities of the compounds to j and l opioid receptors with negligible interactions at the d receptor. The compound, S4 was the most successful in inhibiting the two most prominent quantitative features of naloxone precipitated withdrawal symptoms - stereotyped jumping and body weight loss. Determination of IC50 of S4 revealed a greater affinity towards l compared to j receptor. In conclusion, quinoline derivatives of S4 like structure offer potential tool for treatment of narcotic addicti