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

Exploiting Data-Driven Hybrid Approaches to Translation in the EXPERT Project

Technologies have transformed the way we work, and this is also applicable to the translation industry. In the past thirty to thirty-five years, professional translators have experienced an increased technification of their work. Barely thirty years ago, a professional translator would not have received a translation assignment attached to an e-mail or via an FTP and yet, for the younger generation of professional translators, receiving an assignment by electronic means is the only reality they know. In addition, as pointed out in several works such as Folaron (2010) and Kenny (2011), professional translators now have a myriad of tools available to use in the translation process.Published versio

Low-Resource Unsupervised NMT:Diagnosing the Problem and Providing a Linguistically Motivated Solution

Unsupervised Machine Translation hasbeen advancing our ability to translatewithout parallel data, but state-of-the-artmethods assume an abundance of mono-lingual data. This paper investigates thescenario where monolingual data is lim-ited as well, finding that current unsuper-vised methods suffer in performance un-der this stricter setting. We find that theperformance loss originates from the poorquality of the pretrained monolingual em-beddings, and we propose using linguis-tic information in the embedding train-ing scheme. To support this, we look attwo linguistic features that may help im-prove alignment quality: dependency in-formation and sub-word information. Us-ing dependency-based embeddings resultsin a complementary word representationwhich offers a boost in performance ofaround 1.5 BLEU points compared to stan-dardWORD2VECwhen monolingual datais limited to 1 million sentences per lan-guage. We also find that the inclusion ofsub-word information is crucial to improv-ing the quality of the embedding

Multi-modal post-editing of machine translation

As MT quality continues to improve, more and more translators switch from traditional translation from scratch to PE of MT output, which has been shown to save time and reduce errors. Instead of mainly generating text, translators are now asked to correct errors within otherwise helpful translation proposals, where repetitive MT errors make the process tiresome, while hard-to-spot errors make PE a cognitively demanding activity. Our contribution is three-fold: first, we explore whether interaction modalities other than mouse and keyboard could well support PE by creating and testing the MMPE translation environment. MMPE allows translators to cross out or hand-write text, drag and drop words for reordering, use spoken commands or hand gestures to manipulate text, or to combine any of these input modalities. Second, our interviews revealed that translators see value in automatically receiving additional translation support when a high CL is detected during PE. We therefore developed a sensor framework using a wide range of physiological and behavioral data to estimate perceived CL and tested it in three studies, showing that multi-modal, eye, heart, and skin measures can be used to make translation environments cognition-aware. Third, we present two multi-encoder Transformer architectures for APE and discuss how these can adapt MT output to a domain and thereby avoid correcting repetitive MT errors.Angesichts der stetig steigenden Qualität maschineller Übersetzungssysteme (MÜ) post-editieren (PE) immer mehr Übersetzer die MÜ-Ausgabe, was im Vergleich zur herkömmlichen Übersetzung Zeit spart und Fehler reduziert. Anstatt primär Text zu generieren, müssen Übersetzer nun Fehler in ansonsten hilfreichen Übersetzungsvorschlägen korrigieren. Dennoch bleibt die Arbeit durch wiederkehrende MÜ-Fehler mühsam und schwer zu erkennende Fehler fordern die Übersetzer kognitiv. Wir tragen auf drei Ebenen zur Verbesserung des PE bei: Erstens untersuchen wir, ob andere Interaktionsmodalitäten als Maus und Tastatur das PE unterstützen können, indem wir die Übersetzungsumgebung MMPE entwickeln und testen. MMPE ermöglicht es, Text handschriftlich, per Sprache oder über Handgesten zu verändern, Wörter per Drag & Drop neu anzuordnen oder all diese Eingabemodalitäten zu kombinieren. Zweitens stellen wir ein Sensor-Framework vor, das eine Vielzahl physiologischer und verhaltensbezogener Messwerte verwendet, um die kognitive Last (KL) abzuschätzen. In drei Studien konnten wir zeigen, dass multimodale Messung von Augen-, Herz- und Hautmerkmalen verwendet werden kann, um Übersetzungsumgebungen an die KL der Übersetzer anzupassen. Drittens stellen wir zwei Multi-Encoder-Transformer-Architekturen für das automatische Post-Editieren (APE) vor und erörtern, wie diese die MÜ-Ausgabe an eine Domäne anpassen und dadurch die Korrektur von sich wiederholenden MÜ-Fehlern vermeiden können.Deutsche Forschungsgemeinschaft (DFG), Projekt MMP