Browsing Conference papers by Author "Aizawa, Akiko"
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Carl, Michael; Lacruz, Isabel; Yamada, Masaru; Aizawa, Akiko (Frederiksberg, 2016)[More information][Less information]
Abstract: Spoken language applications are becoming increasingly operational and are used in many computer applications today. Translation dictation is a mode of translation by which a translator reads a source text and speaks out its translation, instead of typing it. Translation dictation is thus a method of translation situated in between interpretation, where the interpreter hears a text and speaks out the translation (e.g., during conference interpreting) and conventional translation by which a written source text is translated mainly using the keyboard. It is close to sight translation. Translation Dictation was a technique used in some translation bureaus in the 1960s and 1970s (Gingold, 1978) but it has been used less frequently since the mid-80s, as professional translators started using micro-computers (Zapata and Kirkedal, 2015). Already, the ALPAC report (Pierce et al., 1966) mentioned that “productivity of human translators might be as much as four times higher when dictating” as compared to writing, and with today´s increasing quality of voice recognition this mode of translation is experiencing a come-back. The usage of Automatic Speech Recognition (ASR) systems provides an efficient means to produce texts, and our experiments suggest that for some translators and types of text translations it might become even more efficient than post-editing of machine translation. In this paper we describe the ENJA15 translation study and corpus. The ENJA15 corpus is a collection of translation process data that was collected in a collaborative effort by CRITT and NII. The ENJA15 data is part of a bigger data set which will enable us to compare human translation production processes across different languages, different translation modes, including from-scratch translation, machine translation post-editing and translation dictation. URI: http://hdl.handle.net/10398/9281 Files in this item: 1
Michael Cral_2016_03.pdf (265.1Kb) -
Carl, Michael; Lacruz, Isabel; Yamada, Masaru; Aizawa, Akiko (Frederiksberg, 2016)[More information][Less information]
Abstract: By the mid-1980s it was clear that unrestricted high quality machine translation would not be achievable in the foreseeable future and alternative directions to the then dominant rule-based paradigm were proposed. The appropriate level of linguistic representation was difficult to determine, hard to compute; and translation relations were incomplete, error prone and time consuming to formalize within the current-state rule-based translation formalisms. At the same time, with the upcoming availability of Personal Computers (PCs), more translations were produced in electronic form. As translators produce translations daily, implicitly solving those translation problems that are so hard to formalize, Isabelle (1992) said that “Existing translations contain more solutions to more translation problems than any other existing resource.“ New horizons for using MT were thus sought, which led to a number of different paradigms, some of which are briefly described. URI: http://hdl.handle.net/10398/9280 Files in this item: 1
Michael Cral_2016_02.pdf (261.8Kb) -
Lacruz, Isabel; Carl, Michael; Yamada, Masaru; Aizawa, Akiko (Frederiksberg, 2016)[More information][Less information]
Abstract: Traditionally, attempts to measure Machine Translation (MT) quality have focused on how close output is to a “gold standard” translation. TER (Translation Error Rate) is one standard measure that can be generated automatically. It is the normalized length of the shortest path (smallest number of edits per word) needed to convert MT output to an average of “ideal” translations (Snover et al., 2006). MT quality has now improved so much that post-edited (or in some cases, raw) MT output is routinely used in many applications in place of from-scratch translations. Despite the translators’ continued resistance to post-editing, there is increasing evidence that productivity is greater when translators post-edit rather than translate from scratch (e.g., Green et al., 2013). Machine-assisted alternatives to post-editing, such as Interactive Translation Prediction (see for example Sanchis- Trilles et al., 2014) are also making rapid advances. Because of these changing paradigms, alternative ways of measuring MT quality are being developed. Under many circumstances, perfect accuracy is not necessary: it is enough for MT output to be “good enough.” The end-user of the raw product should be able to use it with little effort, and the posteditor should easily be able to produce a satisfactory product. MT utility is determined by the effect the MT output has on the actual effort expended by the user, while MT adequacy is determined by the anticipated demand the MT output places on the user. Adequacy has been measured by human judgments along Likert scales, as well as by automatic metrics such as TER. In the context of post-editing, TER is modified to HTER, to measure the discrepancy between MT output and the final post-edited product. Thus, HTER measures the smallest number of necessary edits per word during post-editing. URI: http://hdl.handle.net/10398/9279 Files in this item: 1
Michael Cral_2016_01.pdf (467.9Kb)
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