Temporality as Seen through Translation: A Case Study on Hindi Texts

Temporality as Seen through Translation: A Case Study on Hindi Texts Sabyasachi Kamila† sabysachi.pcs16@iitp.ac.in Sukanta Sen† sukanta.pcs15@iitp.ac.in Mohammad Hasanuzzaman∗ hasanuzzaman.im@gmail.com Asif Ekbal† asif@iitp.ac.in Andy Way∗ andy.way@adaptcentre.ie Pushpak Bhattacharyya† pb@iitp.ac.in †Department of Computer Science and Engineering, Indian Institute of Technology Patna, Patna, India ∗ADAPT Centre, School of Computing, Dublin City University, Dublin, Ireland Abstract Temporality has significantly contributed to various aspects of Natural Language Processing applications. In this paper, we determine the extent to which temporal orientation is preserved when a sentence is translated manually and automatically from the Hindi language to the English language. We show that the manually and automatically identified temporal orientation in English translated (both manual and automatic) sentences provides a good match with the temporal orientation of the Hindi texts. We also find that the task of manual temporal annotation becomes difficult in the translated texts while the automatic temporal processing system man- ages to correctly capture temporal information from the translations. 1 Introduction There is a considerable academic and commercial interest in processing time infor- mation in text, where that information is expressed either explicitly, implicitly, or connotatively. Recognizing such information and exploiting it for Natural Language Processing (NLP) and Information Retrieval (IR) tasks are important features that can significantly improve the functionality of NLP/IR applications such as event time- line generation, question answering, and automatic summarization (Mani et al., 2005; Campos et al., 2014). Earlier studies on temporal information processing have mainly focused on iden- tifying temporal expressions fostered by TempEval challenges (Verhagen et al., 2010; UzZaman et al., 2013). More recently, new trends have emerged in the context of human temporal orientation, which refers to individual differences in the relative emphasis one places on the past, present, or future (Zimbardo and Boyd, 2015). Past studies have es- tablished consistent links between temporal orientation and demographic factors such as age, sex, gender, education, and psychological traits (Webley and Nyhus, 2006; Adams and Nettle, 2009; Schwartz et al., 2013; Zimbardo and Boyd, 2015). In order to create a measure of user-level human temporal orientation measure, a message-level1 temporal 1Only the English message is considered from microblogs. Proceedings of MT Summit XVI, vol.1: Research Track Nagoya, Sep. 18-22, 2017 | p. 324 classifier of past, present, and future is used. For instance, the following microblog post “can’t wait to get a pint tonight” is automatically tagged as future by the temporal classifier. Successful features include timexes, specific temporal (past, present, future) words from a commercial dictionary, but also n-grams. Many tasks in NLP are language-dependent, i.e. the same approach cannot be ap- plied across different languages. In this case, one naive way of temporality detection is to translate the text automatically into the desired language and then apply any tempo- rality detector system. However, Machine Translation (MT) itself is a challenging task and often the meaning, sentiment (Salameh et al., 2015; Lohar et al., 2017), temporarily of a text may not be preserved in the target language. In this paper, we discuss the degree of preservation …

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