Computer-assisted translation using speech recognition

Vidal, Enrique; Casacuberta, Francisco; Rodríguez, Luis; Civera, Jorge; Martínez-Hinarejos, Carlos D.

doi:10.1109/tsa.2005.857788

Cited by 31 publications

(16 citation statements)

References 20 publications

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“…Such hybrid systems have access to the source text and use probabilistic machine translation models to improve voice recognition. Similar efforts have continued over the years, highlighting the various challenges of voice recognition/machine translation integration (Désilets, Stojanovic, Lapointe, Rose, & Reddy, 2008;Reddy & Rose, 2010;Rodriguez, Reddy, & Rose, 2012;Vidal, Casacuberta, Rodríguez, Civera, & Martínez Hinarejos, 2006), and the potential benefits of using voice input for human translation purposes (GarciaMartinez et al, 2014;Mesa-Lao, 2014). Likewise, translation trainers and researchers have made further efforts to evaluate the performance of students and professionals when using off-the-shelf voice recognition systems for straight dictation (Dragsted & Hansen, 2009;Dragsted, Mees, & Hansen, 2011;Mees, Dragsted, Hansen, & Jakobsen, 2013); to introduce them to this technology (Romero-Fresco, 2012); and to assess and analyze professional translators' needs and opinions vis-à-vis voice recognition technology (Ciobanu, 2014;Zapata, 2012Zapata, , 2016.…”

Section: Life Beyond the Keyboardmentioning

confidence: 92%

Translating On the Go? Investigating the Potential of Multimodal Mobile Devices for Interactive Translation Dictation

Zapata¹

2016

tradumatica

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This article provides a general overview of interactive translation dictation (ITD), an emerging translation technique that involves interacting with multimodal voice-and-touchenabled devices such as touch-screen computers, tablets and smartphones. The author discusses the interest in integrating new techniques and technologies into the translation sector, provides a brief description of a recent experiment investigating the potential and challenges of ITD and outlines avenues for future work.Keywords: interactive translation dictation, multimodal interaction, voice recognition, ubiquitous computing, mobile computing, cloud computing RESUMAquest article proveeix un panorama general sobre la traducció dictada interactiva (TDI), tècnica de traducció emergent que implica interactuar amb dispositius multimodals activats amb la veu i el tacte com ara els ordinadors de pantalla tàctil, les tauletes i els telèfons intel·ligents. L'autor examina l'interès d'integrar noves tècnicas i tecnologies al sector de la traducció, proveeix una breu descripció d'un experiment recent que investiga el potencial i els reptes de la TDI, i conclou indicant algunes avingudes per a la recerca futura.Paraules clau: traducció dictada interactiva, interacció multimodal, reconeixement de la veu, computació ubiqua, computació mòbil, computació en núvol RESUMENEste artículo provee un panorama general sobre la traducción dictada interactiva (TDI), técnica de traducción emergente que implica interactuar con dispositivos multimodales activados con la voz y el tacto tales como los ordenadores de pantalla táctil, las tabletas y los teléfonos inteligentes. El autor examina el interés en integrar nuevas técnicas y tecnologías al sector de la traducción, provee una breve descripción de un experimento reciente que investiga el potencial y los retos de la TDI, y concluye indicando algunas avenidas para investigaciones futuras.Palabras clave: traducción dictada interactiva, interacción multimodal, reconocimiento vocal, informática ubicua, informática móvil, informática de nube

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Section: Life Beyond the Keyboardmentioning

confidence: 92%

Translating On the Go? Investigating the Potential of Multimodal Mobile Devices for Interactive Translation Dictation

Zapata¹

2016

tradumatica

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“…In this direction, multi-modal systems involving the use of speech interaction are proposed and studied in Vidal et al 11 with encouraging results.…”

Section: Interactive Machine Translationmentioning

confidence: 99%

Human interaction for high-quality machine translation

et al. 2009

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Introduction Translation from a source language into a target language has become a very important activity in recent years, both in official institutions (such as the United Nations and the EU, or in the parliaments of multilingual countries like Canada and Spain), as well as in the private sector (for example, to translate user's manuals or newspapers articles). Prestigious clients such as these cannot make do with approximate translations; for all kinds of reasons, ranging from the legal obligations to good marketing practice, they require target-language texts of the highest quality. The task of producing such high-quality translations is a demanding and time-consuming one that is generally conferred to expert human translators. The problem is that, with growing globalization, the demand for high-quality translation has been steadily increasing, to the point where there are just not enough qualified translators available today to satisfy it. This has dramatically raised the need for improved machine translation (MT) technologies. The field of MT has undergone something of a revolution over the last 15 years, with the adoption of empirical, data-driven techniques originally inspired by the success of automatic speech recognition. Given the requisite corpora, it is now possible to develop new MT systems in a fraction of the time and with much less effort than was previously required under the formerly dominant rule-based paradigm. As for the quality of the translations produced by this new generation of MT systems, there has also been considerable progress; generally speaking, however, it remains well below that of human translation. No one would seriously consider directly using the output of even the best of these systems to translate a CV or a corporate Web site, for example, without submitting the machine translation to a careful human revision. As a result, those who require publication-quality translation are forced to make a diffcult choice between systems that are fully automatic but whose output must be attentively post-edited, and computer-assisted translation systems (or CAT tools for short) that allow for high quality but to the detriment of full automation. Currently, the best known CAT tools are translation memory (TM) systems. These systems recycle sentences that have previously been translated, either within the current document or earlier in other documents. This is very useful for highly repetitive texts, but not of much help for the vast majority of texts composed of original materials. Since TM systems were first introduced, very few other types of CAT tools have been forthcoming. Notable exceptions are the TransType system and its successor TransType2 (TT2). These systems represent a novel rework-ing of the old idea of interactive machine translation (IMT). Initial efforts on TransType are described in detail in Foster; suffice it to say here the system's principal novelty lies in the fact the human-machine interaction focuses on the drafting of the target text, rather than on the disambiguation of the source text, as in all former IMT systems. In the TT2 project, this idea was further developed. A full-fledged MT engine was embedded in an interactive editing environment and used to generate suggested completions of each target sentence being translated. These completions may be accepted or amended by the translator; but once validated, they are exploited by the MT engine to produce further, hopefully improved suggestions. This is in marked contrast with traditional MT, where typically the system is first used to produce a complete draft translation of a source text, which is then post-edited (corrected) offline by a human translator. TT2's interactive approach offers a significant advantage over traditional post-editing. In the latter paradigm, there is no way for the system, which is off-line, to benefit from the user's corrections; in TransType, just the opposite is true. As soon as the user begins to revise an incorrect segment, the system immediately responds to that new information by proposing an alternative completion to the target segment, which is compatible with the prefix that the user has input. Another notable feature of the work described in this article is the importance accorded to a formal treatment of human-machine interaction, something that is seldom considered in the now-prevalent framework of statistical pattern recognition.

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“…In the context of machine-aided human translation and human-aided machine translation, different scenarios have been investigated where human translators are brought into the loop interacting with a computer through a variety of input modalities to improve the efficiency and accuracy of the translation process (e.g., Dragsted et al 2011, Toselli et al 2011, Vidal 2006. ASR systems have the potential to improve the productivity and comfort of performing computer-based tasks for a wide variety of users, allowing them to enter both text and commands into the computer using just their voice.…”

Section: Introductionmentioning

confidence: 99%

Proceedings of the EACL 2014 Workshop on Humans and Computer-assisted Translation

Koehn¹,

Germann²

2014

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ii IntroductionThe quality of automatic translation of human languages has improved tremendously over the past decade or so. While they still do not achieve publication-quality performance in most cases, state-of-the-art machine translation systems can now deliver a level of quality that make the post-editing of raw machine output by human translators a viable and cost-effective alternative to translation from scratch. Moreover, computerized workflow management can improve consistency in translation, in particular with respect to terminology, and can give translators easy access to dictionaries, glossaries and databases of past translations.Much research in the machine translation community in the past has focused on improving fully automatic MT, but interest in integrating information technology -and specifically machine translation technology -into the translator's workflow is growing in many areas of research: in machine translation research as to how best to provide useful information to the human translator, in translation tool development as to how to make the best use of this information, and in translation process studies in understanding the cognitive and physical processes that take place when humans post-edit or interact with computer-produced translations.This workshop brings together researchers investigating issues in human-computer interaction in the context of translation from a variety of research angles. We have been able to assemble a wonderful roster of talks, posters and system demonstrations that nicely illustrate the current state of research, and we look forward to a productive day of learning and fruitful discussions. This paper proposes to use Word Confidence Estimation (WCE) information to improve MT outputs via N-best list reranking. From the confidence label assigned for each word in the MT hypothesis, we add six scores to the baseline loglinear model in order to re-rank the N-best list. Firstly, the correlation between the WCE-based sentence-level scores and the conventional evaluation scores (BLEU, TER, TERp-A) is investigated. Then, the N-best list re-ranking is evaluated over different WCE system performance levels: from our real and efficient WCE system (ranked 1st during last WMT 2013 Quality Estimation Task) to an oracle WCE (which simulates an interactive scenario where a user simply validates words of a MT hypothesis and the new output will be automatically re-generated). The results suggest that our real WCE system slightly (but significantly) improves the baseline while the oracle one extremely boosts it; and better WCE leads to better MT quality.

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Computer-assisted translation using speech recognition

Cited by 31 publications

References 20 publications

Translating On the Go? Investigating the Potential of Multimodal Mobile Devices for Interactive Translation Dictation

Translating On the Go? Investigating the Potential of Multimodal Mobile Devices for Interactive Translation Dictation

Human interaction for high-quality machine translation

Proceedings of the EACL 2014 Workshop on Humans and Computer-assisted Translation

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