Using Machine Translation to Provide Target-Language Edit Hints in Computer Aided Translation Based on Translation Memories

This paper explores the use of general-purpose machine translation (MT) in assisting the users of computer-aided translation (CAT) systems based on translation memory (TM) to identify the target words in the translation proposals that need to be changed (either replaced or removed) or kept unedited, a task we term as "word-keeping recommendation". MT is used as a black box to align source and target sub-segments on the fly in the translation units (TUs) suggested to the user. Source-language (SL) and target-language (TL) segments in the matching TUs are segmented into overlapping sub-segments of variable length and machine-translated into the TL and the SL, respectively. The bilingual sub-segments obtained and the matching between the SL segment in the TU and the segment to be translated are employed to build the features that are then used by a binary classifier to determine the target words to be changed and those to be kept unedited. In this approach, MT results are never presented to the translator. Two approaches are presented in this work: one using a word-keeping recommendation system which can be trained on the TM used with the CAT system, and a more basic approach which does not require any training. Experiments are conducted by simulating the translation of texts in several language pairs with corpora belonging to different domains and using three different MT systems. We compare the performance obtained to that of previous works that have used statistical word alignment for word-keeping recommendation, and show that the MT-based approaches presented in this paper are more accurate in most scenarios. In particular, our results confirm that the MT-based approaches are better than the alignment-based approach when using models trained on out-of-domain TMs. Additional experiments were performed to check how dependent the MT-based recommender is on the language pair and MT system used for training. These experiments confirm a high degree of reusability of the recommendation models across various MT systems, but a low level of reusability across language pairs.This work is supported by the Spanish government through projects TIN2009-14009-C02-01 and TIN2012-32615

Translators and machine translation : book of presentations

El Tradumàtica Research Group està format, entre d'altres, per: Olga Torres-Hostench, Adrià Martín-Mor, Pilar Cid-Leal, Ramon Piqué Huerta, Anna Aguilar-Amat, Marisa Presas, Pilar Sánchez-Gijón, Inna Kozlov

Literary Post-editing and the Question of Copyright

Translation poses a challenge to copyright laws, which extend protection to works based on the originality of expression rather than the ideas expressed, because translations convey the ideas of the original in a different language and therefore also use different expressions. Technologization of translation has further increased this complexity, as tools such as translation memories and machine translation and post-editing practices are starting to also emerge in literary translation, calling for a more detailed investigation of the literary post-editor’s role and ownership of the text. Post-editing of machine-translated output could give rise to copyright protection, but this depends on the level of intervention and whether the post-edited translation is deemed sufficiently original. This article aims to investigate questions of originality, creativity and textual ownership in literary post-editing. We examine two cases where a literary text was machine translated, post-edited and then published. Our research materials consist of the peritexts surrounding the published translations and three epitexts: one publisher’s website, a research article written by one of the post-editors to describe the experience, and an interview with the other post-editor. Through a qualitative content analysis of these materials, we examine how they reflect the post-editors’ approach to post-editing, personal input in the process and textual ownership of the post-edited target text. The findings suggest that the two post-editors have different approaches to post-editing, leading them to differing perceptions of their own creative input and relationship with the final text.

Proceedings of the 17th Annual Conference of the European Association for Machine Translation

Proceedings of the 17th Annual Conference of the European Association for Machine Translation (EAMT

Deep interactive text prediction and quality estimation in translation interfaces

The output of automatic translation systems is usually destined for human consumption. In most cases, translators use machine translation (MT) as the first step in the process of creating a fluent translation in a target language given a text in a source language. However, there are many possible ways for translators to interact with MT. The goal of this thesis is to investigate new interactive designs and interfaces for translation. In the first part of the thesis, we present pilot studies which investigate aspects of the interactive translation process, building upon insights from Human-Computer Interaction (HCI) and Translation Studies. We developed HandyCAT, an open-source platform for translation process research, which was used to conduct two user studies: an investigation into interactive machine translation and evaluation of a novel component for post-editing. We then propose new models for quality estimation (QE) of MT, and new models for es- timating the confidence of prefix-based neural interactive MT (IMT) systems. We present a series of experiments using neural sequence models for QE and IMT. We focus upon token-level QE models, which can be used as standalone components or integrated into post-editing pipelines, guiding users in selecting phrases to edit. We introduce a strong recurrent baseline for neural QE, and show how state of the art automatic post-editing (APE) models can be re-purposed for word-level QE. We also propose an auxiliary con- fidence model, which can be attached to (I)-MT systems to use the model’s internal state to estimate confidence about the model’s predictions. The third part of the thesis introduces lexically constrained decoding using grid beam search (GBS), a means of expanding prefix-based interactive translation to general lexical constraints. By integrating lexically constrained decoding with word-level QE, we then suggest a novel interactive design for translation interfaces, and test our hypotheses using simulated editing. The final section focuses upon designing an interface for interactive post-editing, incorporating both GBS and QE. We design components which introduce a new way of interacting with translation models, and test these components in a user-study

HANDLING MULTILINGUAL CONTENT IN DIGITAL MEDIA: A CRITICAL ANALYSIS

This document expresses and analyzes the need to define a generic method for representing multilingual information in multimedia data. It describes the basic requirements that would bear upon such representations and establishes the potential link with ISO committee TC 37/SC 4 (Language Resource Management) and with XMT (eXtended MPEG-4 Textual format)