Online Learning for Effort Reduction in Interactive Neural Machine Translation

[EN] Neural machine translation systems require large amounts of training data and resources. Even with this, the quality of the translations may be insufficient for some users or domains. In such cases, the output of the system must be revised by a human agent. This can be done in a post-editing stage or following an interactive machine translation protocol. We explore the incremental update of neural machine translation systems during the post-editing or interactive translation processes. Such modifications aim to incorporate the new knowledge, from the edited sentences, into the translation system. Updates to the model are performed on-the-fly, as sentences are corrected, via online learning techniques. In addition, we implement a novel interactive, adaptive system, able to react to single-character interactions. This system greatly reduces the human effort required for obtaining high-quality translations. In order to stress our proposals, we conduct exhaustive experiments varying the amount and type of data available for training. Results show that online learning effectively achieves the objective of reducing the human effort required during the post-editing or the interactive machine translation stages. Moreover, these adaptive systems also perform well in scenarios with scarce resources. We show that a neural machine translation system can be rapidly adapted to a specific domain, exclusively by means of online learning techniques.The authors wish to thank the anonymous reviewers for their valuable criticisms and suggestions. The research leading to these results has received funding from the Generalitat Valenciana under grant PROMETEOII/2014/030 and from TIN2015-70924-C2-1-R. We also acknowledge NVIDIA Corporation for the donation of GPUs used in this work.Peris-Abril, Á.; Casacuberta Nolla, F. (2019). Online Learning for Effort Reduction in Interactive Neural Machine Translation. Computer Speech & Language. 58:98-126. https://doi.org/10.1016/j.csl.2019.04.001S981265

A user-study on online adaptation of neural machine translation to human post-edits

© 2018, Springer Nature B.V. The advantages of neural machine translation (NMT) have been extensively validated for offline translation of several language pairs for different domains of spoken and written language. However, research on interactive learning of NMT by adaptation to human post-edits has so far been confined to simulation experiments. We present the first user study on online adaptation of NMT to user post-edits in the domain of patent translation. Our study involves 29 human subjects (translation students) whose post-editing effort and translation quality were measured on about 4500 interactions of a human post-editor and an NMT system integrating an online adaptive learning algorithm. Our experimental results show a significant reduction in human post-editing effort due to online adaptation in NMT according to several evaluation metrics, including hTER, hBLEU, and KSMR. Furthermore, we found significant improvements in BLEU/TER between NMT outputs and professional translations in granted patents, providing further evidence for the advantages of online adaptive NMT in an interactive setup

Machine translation for everyone: Empowering users in the age of artificial intelligence

Language learning and translation have always been complementary pillars of multilingualism in the European Union. Both have been affected by the increasing availability of machine translation (MT): language learners now make use of free online MT to help them both understand and produce texts in a second language, but there are fears that uninformed use of the technology could undermine effective language learning. At the same time, MT is promoted as a technology that will change the face of professional translation, but the technical opacity of contemporary approaches, and the legal and ethical issues they raise, can make the participation of human translators in contemporary MT workflows particularly complicated. Against this background, this book attempts to promote teaching and learning about MT among a broad range of readers, including language learners, language teachers, trainee translators, translation teachers, and professional translators. It presents a rationale for learning about MT, and provides both a basic introduction to contemporary machine-learning based MT, and a more advanced discussion of neural MT. It explores the ethical issues that increased use of MT raises, and provides advice on its application in language learning. It also shows how users can make the most of MT through pre-editing, post-editing and customization of the technology

A Neural, Interactive-predictive System for Multimodal Sequence to Sequence Tasks

We present a demonstration of a neural interactive-predictive system for tackling multimodal sequence to sequence tasks. The system generates text predictions to different sequence to sequence tasks: machine translation, image and video captioning. These predictions are revised by a human agent, who introduces corrections in the form of characters. The system reacts to each correction, providing alternative hypotheses, compelling with the feedback provided by the user. The final objective is to reduce the human effort required during this correction process. This system is implemented following a client-server architecture. For accessing the system, we developed a website, which communicates with the neural model, hosted in a local server. From this website, the different tasks can be tackled following the interactive-predictive framework. We open-source all the code developed for building this system. The demonstration in hosted in http://casmacat.prhlt.upv.es/interactive-seq2seq.Comment: ACL 2019 - System demonstration

Online Learning in Neural Machine Translation

[EN] High quality translations are in high demand these days. Although machine translation offers acceptable performance, it is not sufficient in some cases and human supervision is required. In order to ease the translation task of the human, machine translation systems take part in this process. When a sentence in the source language needs to be translated, it is fed to the system which outputs a hypothesis translation. The human then, corrects this hypothesis (also known as post-editing) in order to obtain a high quality translation. Being able to transfer the knowledge that a human translator exhibit when post-editing a translation to the machine translation system is a desirable feature, as it has been proven that a more accurate machine translation system helps to increase the efficiency of the post-editing process. Because the post-editing scenario requires an already trained system, online learning techniques are suited for this task. In this work, three online learning algorithms have been proposed and applied to a neural machine translation sys- tem in a post-editing scenario. They rely on the Passive-Aggressive online learn- ing approach in which the model is updated after every sample in order to fulfil a correctness criterion while remembering previously learned information. The goal is to adapt and refine an already trained system with new samples on-the- fly as the post-editing process takes place (hence, the update time must be kept under control). Moreover, these new algorithms are compared with well-stablished online learning variants of the stochastic gradient descent algorithm. Results show im- provements on the translation quality of the system after applying these algo- rithms, reducing human effort in the post-editing process.[ES] La traducción de gran calidad está muy demandada en la actualidad. A pesar de que la traducción automática ofrece unas prestaciones aceptables, en algunos casos no es suficiente y es necesaria la supervisión humana. Para facilitar la tarea de traducción del humano, los sistemas de traducción automática toman parte en este proceso. Cuando una nueva oración en el idioma origen necesita ser tradu- cida, esta se introduce en el sistema, el cual obtiene como salida una hipótesis de traducción. El humano entonces, corrige esta hipótesis (también conocido como post-editar) para obtener una traducción de mayor calidad. Ser capaz de transfe- rir el conocimiento que el humano exhibe cuando realiza la tarea de post-edición al sistema de traducción automática es una característica deseable puesto que se ha demostrado que un sistema de traducción mas preciso ayuda a aumentar la eficiencia del proceso de post-edición. Debido a que el proceso de post-edición requiere un sistema ya entrenado, las técnicas de aprendizaje en línea son las adecuadas para esta tarea. En este traba- jo, se proponen tres algoritmos de aprendizaje en línea aplicados a un traductor automático neuronal en un escenario de post-edición. Estos algoritmos se basan en la aproximación en línea Passive-Aggressive en la cual el modelo se actualiza después de cada muestra con el objetivo de cumplir un criterio de corrección a la vez que manteniendo información previa aprendida. El objetivo es adaptar y refinar un sistema ya entrenado con nuevas muestras al vuelo mientras el pro- ceso de post-edición se lleva a cabo (por tanto, el tiempo de actualización debe mantenerse bajo control). Además, estos algoritmos se comparan con otras bien conocidas variantes en línea del algoritmo de descenso por gradiente estocástico. Los resultados mues- tran una mejora en la calidad de las traducciones después de aplicar estos algo- ritmos, reduciendo así el esfuerzo humano en el proceso de post-edición.[CA] La traducció de gran qualitat es troba molt demanada en l’actualitat. Tot i que la traducció automàtica oferix unes prestacions acceptables, en alguns casos no és suficient i és necessària la supervisió humana. Per a facilitar la tasca de traducció de l’humà, els sistemes de traducció automàtica prenen part en aquest procés. Quan una nova oració en el llenguatge origen necessita ser traduïda, esta s’introduïx en el sistema, el qual obté com a eixida una hipòtesi de traducció. Llavors, l’humà corregix aquesta hipòtesi (també conegut com a post-editar) per a obtindre una traducció de major qualitat. Ser capaços de transferir el coneixement que l’ humà exhibix quan realitza la tasca de post-edició al sistema de traducció automàtica és una característica desitjable ja que s’ha demostrat que un sistema de traducció mes precís ajuda a augmentar l‘eficiència del procés de post-edició. Pel fet que el procés de post-edició requerix un sistema ja entrenat, les tècniques d’aprenentatge en línia són les adequades per aquesta tasca. En este treball, es proposen tres algoritmes d’aprenentatge en línia aplicats a un traductor automàtic neuronal en un escenari de post-edició. Estos algoritmes es basen en l’aproximació en línia Passive-Aggressive en la qual el model s’actualitza després de cada mostra amb l’objectiu de complir un criteri de correcció al mateix temps que manté informació prèvia apresa. L’objectiu és adaptar i refinar un sistema ja entrenat amb noves mostres al vol mentre el procés de post-edició es du a terme (per tant, el temps d’actualització ha de mantenir-se controlat). A més, estos algoritmes es comparen amb altres ben conegudes variants en línia de l’algoritme de descens per gradient estocàstic. Els resultats mostren una millora en la qualitat de les traduccions després d’aplicar estos algoritmes, reduint així l’esforç humà en el procés de post-edició.Cebrián Chuliá, L. (2017). Aprendizaje en línea en traducción automática basada en redes neuronales. http://hdl.handle.net/10251/86299TFG

Improving the translation environment for professional translators

When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side. This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project