Reshef Shilon


2019

pdf bib
Exploring Deep Multimodal Fusion of Text and Photo for Hate Speech Classification
Fan Yang | Xiaochang Peng | Gargi Ghosh | Reshef Shilon | Hao Ma | Eider Moore | Goran Predovic
Proceedings of the Third Workshop on Abusive Language Online

Interactions among users on social network platforms are usually positive, constructive and insightful. However, sometimes people also get exposed to objectionable content such as hate speech, bullying, and verbal abuse etc. Most social platforms have explicit policy against hate speech because it creates an environment of intimidation and exclusion, and in some cases may promote real-world violence. As users’ interactions on today’s social networks involve multiple modalities, such as texts, images and videos, in this paper we explore the challenge of automatically identifying hate speech with deep multimodal technologies, extending previous research which mostly focuses on the text signal alone. We present a number of fusion approaches to integrate text and photo signals. We show that augmenting text with image embedding information immediately leads to a boost in performance, while applying additional attention fusion methods brings further improvement.

2012

pdf
Incorporating Linguistic Knowledge in Statistical Machine Translation: Translating Prepositions
Reshef Shilon | Hanna Fadida | Shuly Wintner
Proceedings of the Workshop on Innovative Hybrid Approaches to the Processing of Textual Data

2010

pdf
Machine Translation between Hebrew and Arabic: Needs, Challenges and Preliminary Solutions
Reshef Shilon | Nizar Habash | Alon Lavie | Shuly Wintner
Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Student Research Workshop

Hebrew and Arabic are related but mutually incomprehensible languages with complex morphology and scarce parallel corpora. Machine translation between the two languages is therefore interesting and challenging. We discuss similarities and differences between Hebrew and Arabic, the benefits and challenges that they induce, respectively, and their implications for machine translation. We highlight the shortcomings of using English as a pivot language and advocate a direct, transfer-based and linguistically-informed (but still statistical, and hence scalable) approach. We report preliminary results of such a system that we are currently developing.