Unit Completion for a Computer-aided Translation Typing System

Unit Completion for a Computer-aided Translation System Philippe Langlais, George Foster and Guy Lapalme RALI / DIRO Universit6 de Montreal C.P. 6128, succursale Centre-ville Montral (Qubec), Canada, H3C 3J7 { f elipe,f o ster, lapalme } @iro. umontreal, ca Typing Abstract This work is in the context of TRANSTYPE, a sys- tem that observes its user as he or she types a trans- lation and repeatedly suggests completions for the text already entered. The user may either accept, modify, or ignore these suggestions. We describe the design, implementation, and performance of a pro- totype which suggests completions of units of texts that are longer than one word. 1 Introduction TRANSTYPE is part of a project set up to explore an appealing solution to Interactive Machine Trans- lation (IMT). In constrast to classical IMT systems, where the user's role consists mainly of assisting the computer to analyse the source text (by answering questions about word sense, ellipses, phrasal attach- ments, etc), in TRANSTYPE the interaction is direct- ly concerned with establishing the target text. Our interactive translation system works as fol- lows: a translator selects a sentence and begins typ- ing its translation. After each character typed by the translator, the system displays a proposed com- pletion, which may either be accepted using a spe- cial key or rejected by continuing to type. Thus the translator remains in control of the translation process and the machine must continually adapt it- s suggestions in response to his or her input. We are currently undertaking a study to measure the extent to which our word-completion prototype can improve translator productivity. The conclusions of this study will be presented elsewhere. The first version of TrtANSTYPE (Foster et al., 1997) only proposed completions for the current word. This paper deals with predictions which ex- tend to the next several words in the text. The po- tential gain from multiple-word predictions can be appreciated in the one-sentence translation task re- ported in table 1, where a hypothetical user saves over 60% of the keystrokes needed to produce a translation in a word completion scenario, and about 85% in a "unit" completion scenario. In all the figures that follow, we use different fonts to differentiate the various input and output: italics are used for the source text, sans-serif for characters typed by the user and typewriter-like for charac- ters completed by the system. The first few lines of the table 1 give an idea of how TransType functions. Let us assume the unit s- cenario (see column 2 of the table) and suppose that the user wants to produce the sentence "Ce projet de loi est examin~ ~ la chambre des communes" as a translation for the source sentence "This bill is ex- amined in the house of commons". The first hypoth- esis that the system produces before the user enters a character is loi (law). As this is not a good guess from TRANSTYPE the user types the first character (c) of the words he or …

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