A Corpus-based Contrastive Analysis for Defining Minimal Semantics of Inter-sentential Dependencies for Machine Translation

Inter-sentential dependencies such as discourse connectives or pronouns have an impact on the translation of these items. These dependencies have classically been analyzed within complex theoretical frameworks, often monolingual ones, and the resulting fine-grained descriptions, although relevant to translation, are likely beyond reach of statistical machine translation systems. Instead, we propose an approach to search for a minimal, feature-based characterization of translation divergencies due to inter-sentential dependencies, in the case of discourse connectives and pronouns, based on contrastive analyses performed on the Europarl corpus. In addition, we show how to automatically assign labels to connectives and pronouns, and how to use them for statistical machine translation

Discourse-level Annotation over Europarl for Machine Translation: Connectives and Pronouns

This paper describes methods and results for the annotation of two discourse-level phenomena, connectives and pronouns, over a multilingual parallel corpus. Excerpts from Europarl in English and French have been annotated with disambiguation information for connectives and pronouns, for about 3600 tokens. This data is then used in several ways: for cross-linguistic studies, for training automatic disambiguation software, and ultimately for training and testing discourse-aware statistical machine translation systems. The paper presents the annotation procedures and their results in detail, and overviews the first systems trained on the annotated resources and their use for machine translation

Integrating post-editing with Dragon speech recognizer: a use case in an international organization

In international organizations, the growing demand for translations has increased the need for postediting. In this paper, we will explore the possibilities of using speech in the translation process by conducting a pilot post-editing experiment with three professional translators in an international organization. Our experiment consisted of comparing three translation methods: dictating the translation with machine translation (MT) as an inspiration, i.e., respeaking translation (RES), postediting MT suggestions by typing (PET), and post-editing MT suggestions using speech (SPE). The speech recognizer used for this experiment was Dragon. BLEU and HTER scores were used to compare the three methods. Our study shows that the translators made more edits using the RES method, whereas with SPE the resulting translations were closer to the reference, according to the BLEU score, and required fewer edits. The time taken to translate was the shortest with SPE, followed by PET and RES methods. To the best of our knowledge, this is the first quantitative study to be conducted using post-editing and dictation together, where both reference translations, as well as revised, post-edited MT translation outputs, are used to perform a detailed analysis of the different possible translation methods that can be used in international organizations

Surveying the potential of using speech technologies for post-editing purposes in the context of international organizations: What do professional translators think?

The present study has surveyed professional translators working in six international organizations in order to know more about their views and attitudes with regard to new translation workflows involving two different types of technologies, i.e. machine translation and speech recognition. The main aim of this survey was to identify how feasible it is to implement new post-editing workflows in an international organization using speech as an input method to edit inaccurate machine translation outputs. Overall, the results suggest that the surveyed translators do not hold a negative view on the use of ASR as part of their translation workflow, which provides a promising first step towards investigating the integration of speech based post-editing to translation workflows for productivity and ergonomic gains

Integrating Speech in Post-Editing (PE)-Comparison of two PE Interfaces

Translation services are useful for a variety of industries and organisations, of which international organisations play a major role. International Organisations require translating documents into multiple official languages, with high accuracy, within a limited time frame. Post-editing of Machine Translation (MT) is known to allow translating large volumes of translations while saving costs and time. Workflows in the translation industry have experienced a significant transformation and it is in this new context that speech technology is likely to contribute to further innovation. Preliminary studies show that provided that the ASR and MT output are of high quality and that the translators are competent with using software (Computer Assisted Translation (CAT) tools, MT suggestions and ASR toolkits such as Dragon), speech based post-editing can be a promising approach which can result in performance gains in the translation workflow. In this paper we explore how different post-editing methods using speech and keyboard will perform in two different translation interfaces. Recent research encourages studies on multi-modal post-editing CAT environments combining different input possibilities including pen, touch and speech. However in international organisations, current CAT interfaces still heavily focus on traditional mouse and keyboard input to support PE operations. This opens up the question on whether using different modalities in different translation interfaces can be integrated to the translation process workflow in international organisations, which is an unexplored area of research so far.</p