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Deep interactive text prediction and quality estimation in translation interfaces
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In: Hokamp, Christopher M. (2018) Deep interactive text prediction and quality estimation in translation interfaces. PhD thesis, Dublin City University. (2018)
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Abstract:
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.
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Keyword:
Computational linguistics; Machine learning; Machine translating
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URL: http://doras.dcu.ie/22664/
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