TDDC: Timely Disclosure Documents Corpus

14611

Authors

nan

Abstract

AbstractIn this paper, we describe the details of the Timely Disclosure Documents Corpus (TDDC). TDDC was prepared by manually aligning the sentences from past Japanese and English timely disclosure documents in PDF format published by companies listed on the Tokyo Stock Exchange. TDDC consists of approximately 1.4 million parallel sentences in Japanese and English. TDDC was used as the official dataset for the 6th Workshop on Asian Translation to encourage the development of machine translation.

Keywords

nan

Full Content

Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), pages 363–368 Marseille, 11–16 May 2020 c⃝ European Language Resources Association (ELRA), licensed under CC-BY-NC 363 A Process-oriented Dataset of Revisions during Writing Rianne Conijn1,4, Emily Dux Speltz2, Menno van Zaanen3, Luuk van Waes4, and Evgeny Chukharev-Hudilainen2 1. Department of Cognitive Science and Artificial Intelligence, Tilburg University, The Netherlands 2. Department of English, Iowa State University, United States 3. South African Centre for Digital Language Resources, North-West University, South Africa 4. Department of Management, University of Antwerp, Belgium m.a.conijn@uvt.nl, {endux, evgeny}@iastate.edu, menno.vanzaanen@nwu.ac.za, luuk.vanwaes@uantwerpen.be Abstract Revision plays a major role in writing and the analysis of writing processes. Revisions can be analyzed using a product-oriented approach (focusing on a finished product, the text that has been produced) or a process-oriented approach (focusing on the process that the writer followed to generate this product). Although several language resources exist for the product-oriented approach to revisions, there are hardly any resources available yet for an in-depth analysis of the process of revisions. Therefore, we provide an extensive dataset on revisions made during writing (accessible via hdl.handle.net/10411/VBDYGX). This dataset is based on keystroke data and eye tracking data of 65 students from a variety of backgrounds (undergraduate and graduate English as a first language and English as a second language students) and a variety of tasks (argumentative text and academic abstract). In total, 7,120 revisions were identified in the dataset. For each revision, 18 features have been manually annotated and 31 features have been au- tomatically extracted. As a case study, we show two potential use cases of the dataset. In addition, future uses of the dataset are described. Keywords: Writing analytics, writing process, keystroke logging, revision 1. Introduction Revision plays a major role in writing (Flower and Hayes, 1980; Scardamalia and Bereiter, 1983; Fitzgerald, 1987). Revisions are defined as: “changes at any point in the writ- ing process” (Fitzgerald, 1987, p. 484). Hence, revisions do not necessarily have to correct an error, but they can be any change in the text produced so far. This broad defini- tion results in a large diversity of types of revisions, rang- ing from the revision of a typo to a major restructuring of the text. Each type of revision can have a different effect on the written product or writing quality (Fitzgerald, 1987; Barkaoui, 2016). Likewise, different backgrounds of writ- ers and different tasks can result in different types of re- visions, and different ways in which these are made. For example, spelling and grammar revisions are made more often by second language (L2) compared to first language (L1) writers (Stevenson et al., 2006). Therefore, it is impor- tant to be able to analyze revisions separately and in depth. In writing research, revisions have been analyzed using a product-oriented and a process-oriented approach (Lind- gren and Sullivan, 2006b). With the product-oriented ap- proach, revisions are identified by comparing the differ- ences between two products, such as two drafts, see e.g., Min (2006). The process-oriented …

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Labeled Text

AbstractIn this paper, we describe the details of the Timely Disclosure Documents Corpus (TDDC). TDDC was prepared by manually aligning the sentences from past Japanese and English timely disclosure documents in PDF format published by companies listed on the Tokyo Stock Exchange. TDDC consists of approximately 1.4 million parallel sentences in Japanese and English. TDDC was used as the official dataset for the 6th Workshop on Asian Translation to encourage the development of machine translation.

Publication Details

Paper ID:
14611
Year:
2020
Publication:
TIPSTER
Publication Type:
conf
DOI:
nan