An SMT-driven Authoring Tool
Sriram Venkatapathy, Shachar Mirkin
This paper presents a tool for assisting users in composing texts in a language they do not
know. While Machine Translation (MT) is pretty useful for understanding texts in an unfamiliar
language, current MT technology has yet to reach the stage where it can be used reliably
without a post-editing step. This work attempts to make a step towards achieving this goal.
We propose a tool that provides suggestions for the continuation of the text in the source
language (language that the user knows), thus creating texts that can be translated to the
target language (language that the user does not know). In terms of functionality, our tool
resembles text prediction applications. However , the target language, through a Statistical
Machine Translation (SMT) model, drives the composition and not only the source language.
We present the user interface and describe the considerations that underline the suggestion
process. A simulation of user interaction shows that composition speed can be substantially
reduced and provides initial positive feedback as to the ability to generate better translations.
24th International Conference on Computational Linguistics IIT Bombay, Mumbai, India, 8-15 December, 2012.