Edit distances do not describe editing, but they can be useful for translation process research
Translation process research (TPR) aims at describing what translators do, and one of the technical dimensions of translators' work is editing (applying detailed changes to text) In this presentation, we will analyze how different methods for process data collection describe editing. We will review keyloggers used in typical TPR applications, track changes used by word processors, and edit rates based on estimation of edit distances. The purpose of this presentation is to discuss the limitations of these methods when describing editing behavior, and to incentivize researchers in looking for ways to present process data in simplified formats, closer to those that describe product data. 1 Research background The technical dimension of translation, revision and post editing is characterized by writing actions. Editing, part of this technical dimension, is a set of actions that is applied to pre existing text. This implies that editing cannot be analyzed in the same way as translating or writing from scratch. We see editing as being composed of four actions: delete, insert, move and replace (do Carmo 2017) This presentation discusses the implications of this definition of editing and of different methods to describe it. If we want to know which words were edited and how, we need data that accurately describes the actions performed. After we have that data, we may extract from it features that can be used to (c) 2019 The authors. This article is licensed under a Creative Commons 4.0 licence, no derivative works, attribution, CCBY ND. train computational models that predict editing patterns and behaviors. TPR tools, like Translog II (Carl, 2012) and Inputlog (Leijten and Van Waes, 2013) use keylogging to collect process data, in a character and chronological base. However, it has been shown that it is not straightforward to convert TPR data into word based sequences of edit actions (do Carmo et al. 2018; Leijten et al. 2012) The main reason for this is the fact that process data is not linear: it includes incomplete, repeated, wrong actions, scattered edits, and other process components that cannot be associated with the words that survive in the final edited versions. Word processors and translation tools often incorporate track change features that record editing, but these too are not straightforwardly converted into editing data. Product data seems to describe a simpler reality, so simpler methods may be used. Edit distances appeared in the 1960's as methods to identify and correct errors of spelling in text typing (Damerau, 1964) and errors in computer code (Levenshtein, 1966) These edit distances evolved into metrics like WER Word Error Rate (Popovic and Ney, 2007) and TER Translation Edit Rate (Snover et al. 2006) both of which have several variants. Edit rates identify differences between two versions of a text, and they have been extensively used in applications like automatic post editing (do Carmo et al, 2019) and quality estimation of machine translation (Specia et al. 2018) In these applications, they are seen as good predictors of the editing required by texts or sentences. MEMENTO Dublin, Aug. 20, 2019 p. 1