ABSTRACT
Stance detection aims at inferring from text whether the author is in favor of, against, or neutral towards a target entity. Most of the existing studies consider different target entities separately. However, in many scenarios, stance targets are closely related, such as several candidates in a general election and different brands of the same product. Multi-target stance detection, in contrast, aims at jointly detecting stances towards multiple related targets. As stance expression regarding a target can provide additional information to help identify the stances towards other related targets, modeling expressions regarding multiple targets jointly is beneficial for improving the overall performance compared to single-target scheme. In this paper, we propose a dynamic memory-augmented network DMAN for multi-target stance detection. DMAN utilizes a shared external memory, which is dynamically updated through the learning process, to capture and store stance-indicative information for multiple related targets. It then jointly predicts stances towards these targets in a multitask manner. Experimental results show the effectiveness of our DMAN model.
- Alex Graves, Greg Wayne, and Ivo Danihelka . 2014. Neural Turing Machines. arXiv preprint arXiv:1410.5401 (2014).Google Scholar
- Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom . 2015. Teaching Machines to Read and Comprehend. In Proceedings of NIPS. 1693--1701. Google ScholarDigital Library
- Cheng Li, Xiaoxiao Guo, and Qiaozhu Mei . 2017. Deep Memory Networks for Attitude Identification. Proceedings of WSDM. 671--680. Google ScholarDigital Library
- Saif Mohammad, Svetlana Kiritchenko, Parinaz Sobhani, Xiaodan Zhu, and Colin Cherry . 2016. SemEval-2016 Task 6: Detecting Stance in Tweets. Proceedings of SemEval. 31--41.Google ScholarCross Ref
- Parinaz Sobhani . 2017. Stance Detection and Analysis in Social Media. Ph.D. Dissertation. bibinfoschoolUniversité d'Ottawa/University of Ottawa.Google Scholar
- Parinaz Sobhani, Diana Inkpen, and Xiaodan Zhu . 2017. A Dataset for Multi-Target Stance Detection. In Proceedings of EACL. 551--557.Google ScholarCross Ref
- Rupesh K Srivastava, Klaus Greff, and Jürgen Schmidhuber . 2015. Training Very Deep Networks. In Proceedings of NIPS. 2377--2385. Google ScholarDigital Library
- Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston, and Rob Fergus . 2015. End-to-End Memory Networks. In Proceedings of NIPS. 2440--2448. Google ScholarDigital Library
- Zhao Xu, Romain Vial, and Kristian Kersting . 2017. Graph Enhanced Memory Networks for Sentiment Analysis Proceedings of ECML-PKDD. 374--389.Google Scholar
Index Terms
- Multi-Target Stance Detection via a Dynamic Memory-Augmented Network
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