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

Segment-based interactive-predictive machine translation

[EN] Machine translation systems require human revision to obtain high-quality translations. Interactive methods provide an efficient human¿computer collaboration, notably increasing productivity. Recently, new interactive protocols have been proposed, seeking for a more effective user interaction with the system. In this work, we present one of these new protocols, which allows the user to validate all correct word sequences in a translation hypothesis. Thus, the left-to-right barrier from most of the existing protocols is broken. We compare this protocol against the classical prefix-based approach, obtaining a significant reduction of the user effort in a simulated environment. Additionally, we experiment with the use of confidence measures to select the word the user should correct at each iteration, reaching the conclusion that the order in which words are corrected does not affect the overall effort.The research leading to these results has received funding from the Ministerio de Economia y Competitividad (MINECO) under Project CoMUN-HaT (Grant Agreement TIN2015-70924-C2-1-R), and Generalitat Valenciana under Project ALMAMATER (Ggrant Agreement PROMETEOII/2014/030).Domingo-Ballester, M.; Peris-Abril, Á.; Casacuberta Nolla, F. (2017). Segment-based interactive-predictive machine translation. Machine Translation. 31(4):163-185. https://doi.org/10.1007/s10590-017-9213-3S163185314Alabau V, Bonk R, Buck C, Carl M, Casacuberta F, García-Martínez M, González-Rubio J, Koehn P, Leiva LA, Mesa-Lao B, Ortiz-Martínez D, Saint-Amand H, Sanchis-Trilles G, Tsoukala C (2013) CASMACAT: an open source workbench for advanced computer aided translation. Prague Bull Math Linguist 100:101–112Alabau V, Rodríguez-Ruiz L, Sanchis A, Martínez-Gómez P, Casacuberta F (2011) On multimodal interactive machine translation using speech recognition. In: Proceedings of the International Conference on Multimodal Interaction, pp 129–136Alabau V, Sanchis A, Casacuberta F (2014) Improving on-line handwritten recognition in interactive machine translation. Pattern Recognit 47(3):1217–1228Apostolico A, Guerra C (1987) The longest common subsequence problem revisited. Algorithmica 2:315–336Azadi F, Khadivi S (2015) Improved search strategy for interactive machine translation in computer assisted translation. In: Proceedings of Machine Translation Summit XV, pp 319–332Bahdanau D, Cho K, Bengio Y (2015) Neural machine translation by jointly learning to align and translate. In: Proceedings of the International Conference on Learning Representations. arXiv:1409.0473Barrachina S, Bender O, Casacuberta F, Civera J, Cubel E, Khadivi S, Lagarda A, Ney H, Tomás J, Vidal E, Vilar J-M (2009) Statistical approaches to computer-assisted translation. Comput Linguist 35:3–28Brown PF, Pietra VJD, Pietra SAD, Mercer RL (1993) The mathematics of statistical machine translation: parameter estimation. Comput Linguist 19(2):263–311Chen SF, Goodman J (1996) An empirical study of smoothing techniques for language modeling. In: Proceedings of the Annual Meeting on Association for Computational Linguistics, pp 310–318Cheng S, Huang S, Chen H, Dai X, Chen J (2016) PRIMT: a pick-revise framework for interactive machine translation. In: Proceedings of the North American Chapter of the Association for Computational Linguistics, pp 1240–1249Dale R (2016) How to make money in the translation business. Nat Lang Eng 22(2):321–325Domingo M, Peris, Á, Casacuberta F (2016) Interactive-predictive translation based on multiple word-segments. In: Proceedings of the Annual Conference of the European Association for Machine Translation, pp 282–291Federico M, Bentivogli L, Paul M, Stüker S (2011) Overview of the IWSLT 2011 evaluation campaign. In: International Workshop on Spoken Language Translation, pp 11–27Foster G, Isabelle P, Plamondon P (1997) Target-text mediated interactive machine translation. Mach Transl 12:175–194González-Rubio J, Benedí J-M, Casacuberta F (2016) Beyond prefix-based interactive translation prediction. In: Proceedings of the SIGNLL Conference on Computational Natural Language Learning, pp 198–207González-Rubio J, Ortiz-Martínez D, Casacuberta F (2010) On the use of confidence measures within an interactive-predictive machine translation system. In: Proceedings of the Annual Conference of the European Association for Machine TranslationKnowles R, Koehn P (2016) Neural interactive translation prediction. In: Proceedings of the Association for Machine Translation in the Americas, pp 107–120Koehn P (2005) Europarl: a parallel corpus for statistical machine translation. In: Proceedings of the Machine Translation Summit, pp 79–86Koehn P (2010) Statistical machine translation. Cambridge University Press, CambridgeKoehn P, Hoang H, Birch A, Callison-Burch C, Federico M, Bertoldi N, Cowan B, Shen W, Moran C, Zens R, Dyer C, Bojar O, Constantin A, Herbst E (2007) Moses: open source toolkit for statistical machine translation. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp 177–180Koehn P, Och FJ, Marcu D (2003) Statistical phrase-based translation. In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, pp 48–54Koehn P, Tsoukala C, Saint-Amand H (2014) Refinements to interactive translation prediction based on search graphs. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp 574–578Marie B, Max A (2015) Touch-based pre-post-editing of machine translation output. In: Proceedings of the conference on empirical methods in natural language processing, pp 1040–1045Nepveu L, Lapalme G, Langlais P, Foster G (2004) Adaptive language and translation models for interactive machine translation. In: Proceedings of the conference on empirical method in natural language processing, pp 190–197Nielsen J (1993) Usability engineering. Morgan Kaufmann Publishers Inc, BurlingtonOch F J (2003) Minimum error rate training in statistical machine translation. In: Proceedings of the annual meeting of the association for computational linguistics, pp 160–167Och FJ, Ney H (2002) Discriminative training and maximum entropy models for statistical machine translation. In: Proceedings of the annual meeting of the association for computational linguistics, pp 295–302Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Comput Linguist 29(1):19–51Ortiz-Martínez D (2016) Online learning for statistical machine translation. Comput Linguist 42(1):121–161Papineni K, Roukos S, Ward T, Zhu W-J (2002) BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the annual meeting of the association for computational linguistics, pp 311–318Peris Á, Domingo M, Casacuberta F (2017) Interactive neural machine translation. Comput Speech Lang. 45:201–220Sanchis-Trilles G, Ortiz-Martínez D, Civera J, Casacuberta F, Vidal E, Hoang H (2008) Improving interactive machine translation via mouse actions. In: Proceedings of the conference on empirical methods in natural language processing, pp 485–494Snover M, Dorr B, Schwartz R, Micciulla L, Makhoul J (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the Association for Machine Translation in the Americas, pp 223–231Stolcke A (2002) SRILM—an extensible language modeling toolkit. In: Proceedings of the international conference on spoken language processing, pp 257–286Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. NIPS 27:3104–3112Tiedemann J (2009) News from OPUS—a collection of multilingual parallel corpora with tools and interfaces. Recent Adv Nat Lang Process 5:237–248Tomás J, Casacuberta F(2006) Statistical phrase-based models for interactive computer-assisted translation. In: Proceedings of the international conference on computational linguistics/Association for Computational Linguistics, pp 835–841Torregrosa D, Forcada ML, Pérez-Ortiz JA (2014) An open-source web-based tool for resource-agnostic interactive translation prediction. Prague Bull Math Linguist 102:69–80Tseng H, Chang P, Andrew G, Jurafsky D, Manning C (2005) A conditional random field word segmenter. In: Proceedings of the special interest group of the association for computational linguistics workshop on Chinese language processing, pp 168–171Ueffing N, Ney H (2005) Application of word-level confidence measures in interactive statistical machine translation. In: Proceedings of the European Association for Machine Translation, pp 262–270Vogel S, Ney H, Tillmann C (1996) HMM-based word alignment in statistical translation. Proc Conf Comput Linguist 2:836–841Wuebker J, Green S, DeNero J, Hasan S, Luong M-T(2016) Models and inference for prefix-constrained machine translation. In: Proceedings of the annual meeting of the association for the computational linguistics, pp 66–75Zens R, Och FJ, Ney H (2002) Phrase-based statistical machine translation. In: Proceedings of the annual German conference on advances in artificial intelligence 2479:18–3

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

Modelling source- and target-language syntactic Information as conditional context in interactive neural machine translation

In interactive machine translation (MT), human translators correct errors in auto- matic translations in collaboration with the MT systems, which is seen as an effective way to improve the productivity gain in translation. In this study, we model source- language syntactic constituency parse and target-language syntactic descriptions in the form of supertags as conditional con- text for interactive prediction in neural MT (NMT). We found that the supertags significantly improve productivity gain in translation in interactive-predictive NMT (INMT), while syntactic parsing somewhat found to be effective in reducing human efforts in translation. Furthermore, when we model this source- and target-language syntactic information together as the con- ditional context, both types complement each other and our fully syntax-informed INMT model shows statistically significant reduction in human efforts for a French– to–English translation task in a reference- simulated setting, achieving 4.30 points absolute (corresponding to 9.18% relative) improvement in terms of word prediction accuracy (WPA) and 4.84 points absolute (corresponding to 9.01% relative) reduc- tion in terms of word stroke ratio (WSR) over the baseline

A Data-driven Approach Towards Human-robot Collaborative Problem Solving in a Shared Space

We are developing a system for human-robot communication that enables people to communicate with robots in a natural way and is focused on solving problems in a shared space. Our strategy for developing this system is fundamentally data-driven: we use data from multiple input sources and train key components with various machine learning techniques. We developed a web application that is collecting data on how two humans communicate to accomplish a task, as well as a mobile laboratory that is instrumented to collect data on how two humans communicate to accomplish a task in a physically shared space. The data from these systems will be used to train and fine-tune the second stage of our system, in which the robot will be simulated through software. A physical robot will be used in the final stage of our project. We describe these instruments, a test-suite and performance metrics designed to evaluate and automate the data gathering process as well as evaluate an initial data set.Comment: 2017 AAAI Fall Symposium on Natural Communication for Human-Robot Collaboratio

NMT-Keras: a Very Flexible Toolkit with a Focus on Interactive NMT and Online Learning

[EN] We present NMT-Keras, a flexible toolkit for training deep learning models, which puts a particular emphasis on the development of advanced applications of neural machine translation systems, such as interactive-predictive translation protocols and long-term adaptation of the translation system via continuous learning. NMT-Keras is based on an extended version of the popular Keras library, and it runs on Theano and TensorFlow. State-of-the-art neural machine translation models are deployed and used following the high-level framework provided by Keras. Given its high modularity and flexibility, it also has been extended to tackle different problems, such as image and video captioning, sentence classification and visual question answering.Much of our Keras fork and the Multimodal Keras Wrapper libraries were developed together with Marc Bolaños. We also acknowledge the rest of contributors to these open-source projects. The research leading this work received funding from grants PROMETEO/2018/004 and CoMUN-HaT - TIN2015-70924-C2-1-R. We finally acknowledge NVIDIA Corporation for the donation of GPUs used in this work.Peris-Abril, Á.; Casacuberta Nolla, F. (2018). NMT-Keras: a Very Flexible Toolkit with a Focus on Interactive NMT and Online Learning. The Prague Bulletin of Mathematical Linguistics. 111:113-124. https://doi.org/10.2478/pralin-2018-0010S11312411

Some Contributions to Interactive Machine Translation and to the Applications of Machine Translation for Historical Documents

[ES] Los documentos históricos son una parte importante de nuestra herencia cultural. Sin embargo, debido a la barrera idiomática inherente en el lenguaje humano y a las propiedades lingüísticas de estos documentos, su accesibilidad está principalmente restringida a los académicos. Por un lado, el lenguaje humano evoluciona con el paso del tiempo. Por otro lado, las convenciones ortográficas no se crearon hasta hace poco y, por tanto, la ortografía cambia según el período temporal y el autor. Por estas razones, el trabajo de los académicos es necesario para que los no expertos puedan obtener una comprensión básica de un documento determinado. En esta tesis abordamos dos tareas relacionadas con el procesamiento de documentos históricos. La primera tarea es la modernización del lenguaje que, a fin de hacer que los documentos históricos estén más accesibles para los no expertos, tiene como objetivo reescribir un documento utilizando la versión moderna del idioma original del documento. La segunda tarea es la normalización ortográfica. Las propiedades lingüísticas de los documentos históricos mencionadas con anterioridad suponen un desafío adicional para la aplicación efectiva del procesado del lenguaje natural en estos documentos. Por lo tanto, esta tarea tiene como objetivo adaptar la ortografía de un documento a los estándares modernos a fin de lograr una consistencia ortográfica. Ambas tareas las afrontamos desde una perspectiva de traducción automática, considerando el idioma original de un documento como el idioma fuente, y su homólogo moderno/normalizado como el idioma objetivo. Proponemos varios enfoques basados en la traducción automática estadística y neuronal, y llevamos a cabo una amplia experimentación que ratifica el potencial de nuestras contribuciones -en donde los enfoques estadísticos arrojan resultados iguales o mejores que los enfoques neuronales para la mayoría de los casos-. En el caso de la tarea de modernización del lenguaje, esta experimentación incluye una evaluación humana realizada con la ayuda de académicos y un estudio con usuarios que verifica que nuestras propuestas pueden ayudar a los no expertos a obtener una comprensión básica de un documento histórico sin la intervención de un académico. Como ocurre con cualquier problema de traducción automática, nuestras aplicaciones no están libres de errores. Por lo tanto, para obtener modernizaciones/normalizaciones perfectas, un académico debe supervisar y corregir los errores. Este es un procedimiento común en la industria de la traducción. La metodología de traducción automática interactiva tiene como objetivo reducir el esfuerzo necesario para obtener traducciones de alta calidad uniendo al agente humano y al sistema de traducción en un proceso de corrección cooperativo. Sin embargo,la mayoría de los protocolos interactivos siguen una estrategia de izquierda a derecha. En esta tesis desarrollamos un nuevo protocolo interactivo que rompe con esta barrera de izquierda a derecha. Hemos evaluado este nuevo protocolo en un entorno de traducción automática, obteniendo grandes reducciones del esfuerzo humano. Finalmente, dado que este marco interactivo es de aplicación general a cualquier problema de traducción, lo hemos aplicado -nuestro nuevo protocolo junto con uno de los protocolos clásicos de izquierda a derecha- a la modernización del lenguaje y a la normalización ortográfica. Al igual que en traducción automática, el marco interactivo logra disminuir el esfuerzo requerido para corregir los resultados de un sistema automático.[CA] Els documents històrics són una part important de la nostra herència cultural. No obstant això, degut a la barrera idiomàtica inherent en el llenguatge humà i a les propietats lingüístiques d'aquests documents, la seua accessibilitat està principalment restringida als acadèmics. D'una banda, el llenguatge humà evoluciona amb el pas del temps. D'altra banda, les convencions ortogràfiques no es van crear fins fa poc i, per tant, l'ortografia canvia segons el període temporal i l'autor. Per aquestes raons, el treball dels acadèmics és necessari perquè els no experts puguen obtindre una comprensió bàsica d'un document determinat. En aquesta tesi abordem dues tasques relacionades amb el processament de documents històrics. La primera tasca és la modernització del llenguatge que, a fi de fer que els documents històrics estiguen més accessibles per als no experts, té per objectiu reescriure un document utilitzant la versió moderna de l'idioma original del document. La segona tasca és la normalització ortogràfica. Les propietats lingüístiques dels documents històrics mencionades amb anterioritat suposen un desafiament addicional per a l'aplicació efectiva del processat del llenguatge natural en aquests documents. Per tant, aquesta tasca té per objectiu adaptar l'ortografia d'un document als estàndards moderns a fi d'aconseguir una consistència ortogràfica. Dues tasques les afrontem des d'una perspectiva de traducció automàtica, considerant l'idioma original d'un document com a l'idioma font, i el seu homòleg modern/normalitzat com a l'idioma objectiu. Proposem diversos enfocaments basats en la traducció automàtica estadística i neuronal, i portem a terme una àmplia experimentació que ratifica el potencial de les nostres contribucions -on els enfocaments estadístics obtenen resultats iguals o millors que els enfocaments neuronals per a la majoria dels casos-. En el cas de la tasca de modernització del llenguatge, aquesta experimentació inclou una avaluació humana realitzada amb l'ajuda d'acadèmics i un estudi amb usuaris que verifica que les nostres propostes poden ajudar als no experts a obtindre una comprensió bàsica d'un document històric sense la intervenció d'un acadèmic. Com ocurreix amb qualsevol problema de traducció automàtica, les nostres aplicacions no estan lliures d'errades. Per tant, per obtindre modernitzacions/normalitzacions perfectes, un acadèmic ha de supervisar i corregir les errades. Aquest és un procediment comú en la indústria de la traducció. La metodologia de traducció automàtica interactiva té per objectiu reduir l'esforç necessari per obtindre traduccions d'alta qualitat unint a l'agent humà i al sistema de traducció en un procés de correcció cooperatiu. Tot i això, la majoria dels protocols interactius segueixen una estratègia d'esquerra a dreta. En aquesta tesi desenvolupem un nou protocol interactiu que trenca amb aquesta barrera d'esquerra a dreta. Hem avaluat aquest nou protocol en un entorn de traducció automàtica, obtenint grans reduccions de l'esforç humà. Finalment, atès que aquest marc interactiu és d'aplicació general a qualsevol problema de traducció, l'hem aplicat -el nostre nou protocol junt amb un dels protocols clàssics d'esquerra a dreta- a la modernització del llenguatge i a la normalitzaciò ortogràfica. De la mateixa manera que en traducció automàtica, el marc interactiu aconsegueix disminuir l'esforç requerit per corregir els resultats d'un sistema automàtic.[EN] Historical documents are an important part of our cultural heritage. However,due to the language barrier inherent in human language and the linguistic properties of these documents, their accessibility is mostly limited to scholars. On the one hand, human language evolves with the passage of time. On the other hand, spelling conventions were not created until recently and, thus, orthography changes depending on the time period and author. For these reasons, the work of scholars is needed for non-experts to gain a basic understanding of a given document. In this thesis, we tackle two tasks related with the processing of historical documents. The first task is language modernization which, in order to make historical documents more accessible to non-experts, aims to rewrite a document using the modern version of the document's original language. The second task is spelling normalization. The aforementioned linguistic properties of historical documents suppose an additional challenge for the effective natural language processing of these documents. Thus, this task aims to adapt a document's spelling to modern standards in order to achieve an orthography consistency. We affront both task from a machine translation perspective, considering a document's original language as the source language, and its modern/normalized counterpart as the target language. We propose several approaches based on statistical and neural machine translation, and carry out a wide experimentation that shows the potential of our contributions¿with the statistical approaches yielding equal or better results than the neural approaches in most of the cases. For the language modernization task, this experimentation includes a human evaluation conducted with the help of scholars and a user study that verifies that our proposals are able to help non-experts to gain a basic understanding of a historical document without the intervention of a scholar. As with any machine translation problem, our applications are not error-free. Thus, to obtain perfect modernizations/normalizations, a scholar needs to supervise and correct the errors. This is a common procedure in the translation industry. The interactive machine translation framework aims to reduce the effort needed for obtaining high quality translations by embedding the human agent and the translation system into a cooperative correction process. However, most interactive protocols follow a left-to-right strategy. In this thesis, we developed a new interactive protocol that breaks this left-to-right barrier. We evaluated this new protocol in a machine translation environment, obtaining large reductions of the human effort. Finally, since this interactive framework is of general application to any translation problem, we applied it¿our new protocol together with one of the classic left-to-right protocols¿to language modernization and spelling normalization. As with machine translation, the interactive framework diminished the effort required for correcting the outputs of an automatic system.The research leading to this thesis has been partially funded by Ministerio de Economía y Competitividad (MINECO) under projects SmartWays (grant agreement RTC-2014-1466-4), CoMUN-HaT (grant agreement TIN2015-70924-C2-1-R) and MISMISFAKEnHATE (grant agreement PGC2018-096212-B-C31); Generalitat Valenciana under projects ALMAMATER (grant agreement PROMETEOII/2014/030) and DeepPattern (grant agreement PROMETEO/2019/121); the European Union through Programa Operativo del Fondo Europeo de Desarrollo Regional (FEDER) from Comunitat Valenciana (2014–2020) under project Sistemas de frabricación inteligentes para la indústria 4.0 (grant agreement ID-IFEDER/2018/025); and the PRHLT research center under the research line Machine Learning Applications.Domingo Ballester, M. (2022). Some Contributions to Interactive Machine Translation and to the Applications of Machine Translation for Historical Documents [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/181231TESI