Correcting Automatic Translations through Collaborations between MT and Monolingual Target-Language Users

Proceedings of the 12th Conference of the European Chapter of the ACL, pages 60–68, Athens, Greece, 30 March – 3 April 2009. c⃝2009 Association for Computational Linguistics Correcting Automatic Translations through Collaborations between MT and Monolingual Target-Language Users Joshua S. Albrecht and Rebecca Hwa and G. Elisabeta Marai Department of Computer Science University of Pittsburgh {jsa8,hwa,marai}@cs.pitt.edu Abstract Machine translation (MT) systems have improved significantly; however, their out- puts often contain too many errors to com- municate the intended meaning to their users. This paper describes a collabora- tive approach for mediating between an MT system and users who do not under- stand the source language and thus cannot easily detect translation mistakes on their own. Through a visualization of multi- ple linguistic resources, this approach en- ables the users to correct difficult transla- tion errors and understand translated pas- sages that were otherwise baffling. 1 Introduction Recent advances in machine translation (MT) have given us some very good translation systems. They can automatically translate between many languages for a variety of texts; and they are widely accessible to the public via the web. The quality of the MT outputs, however, is not reliably high. People who do not understand the source language may be especially baffled by the MT out- puts because they have little means to recover from translation mistakes. The goal of this work is to help monolingual target-language users to obtain better translations by enabling them to identify and overcome er- rors produced by the MT system. We argue for a human-computer collaborative approach because both the users and the MT system have gaps in their abilities that the other could compensate. To facilitate this collaboration, we propose an inter- face that mediates between the user and the MT system. It manages additional NLP tools for the source language and translation resources so that the user can explore this extra information to gain enough understanding of the source text to correct MT errors. The interactions between the users and the MT system may, in turn, offer researchers in- sights into the translation process and inspirations for better translation models. We have conducted an experiment in which we asked non-Chinese speakers to correct the outputs of a Chinese-English MT system for several short passages of different genres. They performed the correction task both with the help of the visual- ization interface and without. Our experiment ad- dresses the following questions: • To what extent can the visual interface help the user to understand the source text? • In what way do factors such as the user’s backgrounds, the properties of source text, and the quality of the MT system and other NLP resources impact that understanding? • What resources or strategies are more help- ful to the users? What research directions do these observations suggest in terms of im- proving the translation models? Through qualitative and quantitative analysis of the user actions and timing statistics, we have found that users of the interface achieved a more accurate understanding …

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