Browsing by Author "Nasim, Mehwish"
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- MDKG: Graph-Based Medical Knowledge-Guided Dialogue GenerationNaseem, Usman; Thapa, Surendrabikram; Zhang, Qi; Hu, Liang; Nasim, Mehwish (ACM, 2023-07-19)Medical dialogue systems (MDS) have shown promising abilities to diagnose through a conversation with a patient like a human doctor would. However, current systems are mostly based on sequence modeling, which does not account for medical knowledge. This makes the systems more prone to misdiagnosis in case of diseases with limited information. To overcome this issue, we present MDKG, an end-to-end dialogue system for medical dialogue generation (MDG) specifically designed to adapt to new diseases by quickly learning and evolving a meta-knowledge graph that allows it to reason about disease-symptom correlations. Our approach relies on a medical knowledge graph to extract disease-symptom relationships and uses a dynamic graph-based meta-learning framework to learn how to evolve the given knowledge graph to reason about disease-symptom correlations. Our approach incorporates medical knowledge and hence reduces the need for a large number of dialogues. Evaluations show that our system outperforms existing approaches when tested on benchmark datasets.
- RUHate-MM: Identification of Hate Speech and Targets using Multimodal Data from Russia-Ukraine CrisisThapa, Surendrabikram; Jafri, Farhan; Rauniyar, Kritesh; Nasim, Mehwish; Naseem, Usman (ACM, 2024-05-13)During the conflict between Ukraine and Russia, hate speech targeted toward specific groups was widespread on different social media platforms. With most social platforms allowing multimodal content, the use of multimodal content to express hate speech is widespread on the Internet. Although there has been considerable research in detecting hate speech within unimodal content, the investigation into multimodal content remains insufficient. The limited availability of annotated multimodal datasets further restricts our ability to explore new methods to interpret and identify hate speech and its targets. The availability of annotated datasets for hate speech detection during political events, such as invasions, are even limited. To fill this gap, we introduce a comprehensive multimodal dataset consisting of 20,675 posts related to the Russia- Ukraine crisis, which were manually annotated as either ‘Hate Speech’ or ‘No Hate Speech’. Additionally, we categorize the hate speech data into three targets: ‘Individual’, ‘Organization’, and ‘Community’. Our benchmarked evaluations show that there is still room for improvement in accurately identifying hate speech and its targets. We hope that the availability of this dataset and the evaluations performed on it will encourage the development of new methods for identifying hate speech and its targets during political events like invasions and wars. The dataset and resources are made available at https://github.com/Farhan-jafri/Russia-Ukraine.