Farhad Bin Siddique


2020

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CAiRE-COVID: A Question Answering and Query-focused Multi-Document Summarization System for COVID-19 Scholarly Information Management
Dan Su | Yan Xu | Tiezheng Yu | Farhad Bin Siddique | Elham Barezi | Pascale Fung
Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020

We present CAiRE-COVID, a real-time question answering (QA) and multi-document summarization system, which won one of the 10 tasks in the Kaggle COVID-19 Open Research Dataset Challenge, judged by medical experts. Our system aims to tackle the recent challenge of mining the numerous scientific articles being published on COVID-19 by answering high priority questions from the community and summarizing salient question-related information. It combines information extraction with state-of-the-art QA and query-focused multi-document summarization techniques, selecting and highlighting evidence snippets from existing literature given a query. We also propose query-focused abstractive and extractive multi-document summarization methods, to provide more relevant information related to the question. We further conduct quantitative experiments that show consistent improvements on various metrics for each module. We have launched our website CAiRE-COVID for broader use by the medical community, and have open-sourced the code for our system, to bootstrap further study by other researches.

2017

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Zara Returns: Improved Personality Induction and Adaptation by an Empathetic Virtual Agent
Farhad Bin Siddique | Onno Kampman | Yang Yang | Anik Dey | Pascale Fung
Proceedings of ACL 2017, System Demonstrations

2016

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Real-Time Speech Emotion and Sentiment Recognition for Interactive Dialogue Systems
Dario Bertero | Farhad Bin Siddique | Chien-Sheng Wu | Yan Wan | Ricky Ho Yin Chan | Pascale Fung
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Zara The Supergirl: An Empathetic Personality Recognition System
Pascale Fung | Anik Dey | Farhad Bin Siddique | Ruixi Lin | Yang Yang | Yan Wan | Ho Yin Ricky Chan
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

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Zara: A Virtual Interactive Dialogue System Incorporating Emotion, Sentiment and Personality Recognition
Pascale Fung | Anik Dey | Farhad Bin Siddique | Ruixi Lin | Yang Yang | Dario Bertero | Yan Wan | Ricky Ho Yin Chan | Chien-Sheng Wu
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations

Zara, or ‘Zara the Supergirl’ is a virtual robot, that can exhibit empathy while interacting with an user, with the aid of its built in facial and emotion recognition, sentiment analysis, and speech module. At the end of the 5-10 minute conversation, Zara can give a personality analysis of the user based on all the user utterances. We have also implemented a real-time emotion recognition, using a CNN model that detects emotion from raw audio without feature extraction, and have achieved an average of 65.7% accuracy on six different emotion classes, which is an impressive 4.5% improvement from the conventional feature based SVM classification. Also, we have described a CNN based sentiment analysis module trained using out-of-domain data, that recognizes sentiment from the speech recognition transcript, which has a 74.8 F-measure when tested on human-machine dialogues.