INTEGRATING MACHINE TRANSLATION INTO MOOCS
Sheila Castilho, Federico Gaspari, Joss Moorkens, Andy Way
ADAPT Centre, Dublin City University (IRELAND)
This paper presents TraMOOC (Translation for Massive Open Online Courses), a European research
project developed with the intention of empowering international learners in the digital multilingual
world by providing reliable machine translation (MT) specifically tailored to MOOCs from English into
11 languages (Bulgarian, Chinese, Croatian, Czech, Dutch, German, Greek, Italian, Polish,
Portuguese, and Russian). The paper describes how the project is addressing the challenges involved
in developing an innovative, high-quality MT service for producing accurate translations of
heterogeneous multi-genre MOOC materials, encompassing subtitles of video lectures, assignments,
tutorials, and social web text posted on student blogs and fora. Based on the results of a large-scale
and multi-method evaluation conducted as part of the TraMOOC project, we offer a reflection on how
to best integrate state-of-the-art MT into MOOC platforms. The conclusion summarizes the key
lessons learned, that can be applied by the wider community of international professionals with an
interest in the multilingual aspects of innovative education and new learning technologies.
Keywords: MOOCs, machine translation (MT), translation, e-learning, distance learning.
1.1 Background and motivation of the study
Massive Open Online Courses (MOOCs) offer valuable learning opportunities in several disciplines to
many students, to a large extent regardless of their background, location, and personal circumstances
. Views about the actual potential of MOOCs inevitably vary, mostly depending on the subjects
being taught and on the pedagogic attitudes of the instructors (, ,  and ), but MOOCs are
gradually starting to have an impact on teaching practice, at least for some disciplines (see, e.g., ).
One widely held view is that MOOCs may represent effective means of disseminating knowledge and
training to disadvantaged communities or individual students living in remote areas, with limited or no
access to traditional teaching and learning facilities, such as colleges, public libraries, qualified
teaching staff, technical equipment or laboratories . However, rather surprisingly, there is growing
evidence that MOOC participants are in fact predominantly already qualified professionals from
privileged backgrounds mostly based in high-income, industrialized countries (e.g. ,  and ).
One explanation of this seeming failure of MOOCs’ original intended mission of broadening access to
education and training is that this disappointing situation hinges significantly on language-related
limitations. MOOCs are typically available in one language that is shared between tutors and students,
which has the added bonus of enabling interactions on social platforms and fora accompanying formal
instruction . However, language barriers impede broad use of high-quality MOOC materials across
national and language boundaries, severely limiting peer-to-peer as well as student-instructor
interactions alongside the more formal components of MOOC-based instruction: English is often
chosen as the common language of MOOCs with international reach; this, however, is far from ideal,
especially because it prevents large groups of potential users from fully engaging in a fulfilling MOOC
experience, thus wasting precious learning opportunities for innumerable motivated students around
the world. In an increasingly globalized and mobile society, in which academic institutions as well as
individual trainers are under growing pressure to seize the opportunities offered by internationalization,
there is a strong need for high-quality digital teaching and learning resources to be distributed across
linguistic and cultural boundaries .
Against this background, the paper reports the experience of the international research project
TraMOOC (Translation for Massive Open Online Courses, whose official website can be visited at
http://tramooc.eu/). The paper is structured as follows: after these introductory remarks on the
background and motivation of the study, Section 1.2 provides more detail on the project, emphasizing
its aims and expected outcomes. Section 2 describes the evolution of the main approaches to MT
system design, from the traditional rule-based architecture to the more recent statistical and neural
Proceedings of EDULEARN17 Conference
3rd-5th July 2017, Barcelona, Spain
paradigms, that are now competing to be recognized as the state-of-the-art. Section 3 discusses the
application of MT for MOOCs, highlighting the difficulties inherent in the types of texts that form a
MOOC, and Section 4 details our development and evaluation of MT systems within the TraMOOC
project. Finally, Section 5 concludes by summarizing the key lessons learned from this work that can
be useful to the wider community of instructors and institutions interested in delivering innovative and
effective education opportunities via MOOCs to multilingual students, also outlining some possibilities
for future work in the rapidly evolving area at the crossroads of MOOCs and MT.
1.2 The TraMOOC project: aims and expected outcomes
One issue that cuts across all MOOCs with significant impact on their uptake and effectiveness is that
of the language(s) of instruction: this, in itself, is a crucial factor in restricting or, on the contrary,
widening access to education and training delivered via MOOCs . Making MOOC contents
available in multiple languages has obvious benefits, and there have already been attempts to support
language diversity within MOOCs with a European focus . In the ambitious attempt to address the
numerous and complex challenges entailed by this endeavour, TraMOOC aims at developing high-
quality MT of the multifarious text genres typically included in MOOCs from English into 9 European
(i.e. Bulgarian, Croatian, Czech, Dutch, German, Greek, Italian, Polish and Portuguese) and 2 so-
called BRIC languages (namely, Chinese and Russian). While these diverse target languages
constitute strong use cases in the MOOC space, some of them have been proven difficult to translate
into, which is further compounded by the weak or fragmentary support in terms of language resources
and processing tools that are required to build some of the relevant MT systems. This scenario poses
significant research and development challenges to the TraMOOC project consortium.
The main outcome of the project lies in the development of a high-quality semi-automated MT platform
for all types of textual data normally encountered in MOOCs, which typically range from subtitles of
video lectures to instructions for completing assignments, presentation slides, posts shared on student
blogs and comments sent to course fora. The core of the final service will be open-source and some
premium add-on services are expected to be commercialized, including MT support for additional
target languages of interest to the users, MT post-editing, transcription and subtitling of video-based
course contents, as well as professional translation. The ultimate goal is to turn the MOOC translation
service into a platform enabling the integration of any MT system chosen by the users, for any desired
language, for the educational domain.
2 THE EVOLUTION OF MACHINE TRANSLATION SYSTEM DESIGN
MT has made substantial progress over the course of its history. Until the mid-1990s, rule-based MT
systems were the norm: these required significant investments and huge resources to be built,
including skilled computational linguists and programmers. This meant that MT systems were
available only for a limited number of well-resourced languages with substantial commercial interest.
In the late 1990s, a new data-driven paradigm emerged in MT system development, namely statistical
MT (SMT), which quickly became the dominant approach in both research and market-oriented
commercial applications. The principle underlying this approach is to do away with explicit linguistic
rules altogether. In contrast, translation patterns (i.e. correspondences between phrases in the source
and in the target languages) are inferred automatically from the analysis of parallel corpora, i.e. huge
collections of sentence-aligned professional (i.e. human-quality) translations. SMT systems estimate
the degree of probability for the correspondence of short bilingual chunks of text extracted from the
analysis of the parallel corpora, and subsequently generate the output in the target language based on
complex statistical calculations.
SMT systems can be built much faster and at a fraction of the cost of traditional rule-based ones, for
many more language pairs, using open-source development toolkits, such as Moses . In addition,
SMT systems can be customized much more effectively than rule-based ones to different domains
and text types. More recently, the neural approach has emerged as a promising further development
in MT system design, attracting interest not only from academic researchers, but also from players in
the language, translation and localization industry, because neural MT (NMT) systems have
outperformed SMT systems for a number of language pairs in recent comparative evaluations. Simply
put, NMT exploits neural networks and deep learning techniques drawn from artificial intelligence to
map entire sentences from the source to the target language all at once, instead of breaking them
down into smaller units (typically individual words, or fixed sequences of a few words), as is the case
in SMT. This offers some advantages, although it is still debated whether NMT is superior to SMT.
3 MACHINE TRANSLATION FOR MOOCS
Several recent studies address the crucial issue of evaluating and improving the quality, effectiveness
and success of MOOCs (see, e.g.,  and ), and research has also been devoted to evaluating
the level of engagement afforded by MOOCs (e.g. ). This body of work provides, either implicitly or
explicitly, indications concerning good practice . What is conspicuously absent from this
substantial body of work is the language dimension of MOOC-based instruction, especially when, as is
often the case, MOOCs have the ambition of being delivered internationally, to course takers with
different linguistic and cultural backgrounds: this necessarily raises the issue of how to effectively
translate these digital teaching and learning resources, so that their eventual multilingual nature
contributes to their overall value for students, rather than detracting from it.
We regard this as a major gap in the MOOC literature, and contend that the language used to impart
knowledge and support interactions associated with MOOCs is a key factor in the quality,
effectiveness and success of learning experiences for international students, which should receive
more attention, and this paper wishes to represent a first step in this direction. The broad questions
addressed in the work reported here are whether the time is ripe for the integration of MT into
MOOCs, and how to best go about selecting the most effective MT solution for this purpose.
A particularly interesting application domain that has recently emerged for MT concerns user-
generated content (UGC) . Successful techniques have been developed, for example, for the
domain adaption of MT systems to deal with user comments in the e-commerce scenario , with
several experiments showing the feasibility of this rather challenging task, even though it is certainly
hard to obtain high-quality MT output in this area. UGC is also found in typical MOOC data, and the
TraMOOC project aims at providing reliable MT for it, too, which is extremely challenging, because
UGC is often poorly formulated, with relatively frequent spelling mistakes and grammatical
inaccuracies, and more generally sub-standard, or non-conventional, language.
4 MACHINE TRANSLATION FOR TRAMOOC
For the TraMOOC project, we undertook to evaluate which of the two leading approaches to MT
system design competing to be the state-of-the-art in the field, namely SMT or NMT (see Section 2), is
better suited to be integrated into a MOOC platform to effectively deliver digital learning resources
multilingually. The overall study is reported in more detail in .
The SMT and the NMT systems used for this evaluation were built using state-of-the-art procedures,
aimed at guaranteeing the highest possible quality; in particular, for the statistical approach, a phrase-
based architecture was used, while the NMT systems generally followed the settings of . All the
systems were trained on a variable mix of general, i.e. out-of-domain, and in-domain educational data,
due to the different resources available for each language combination. The general training data
ranged from a minimum of 21.30 million sentence pairs for EN-RU, to a maximum of almost 32 million
sentence pairs for EN-PT; the much smaller in-domain training data sets consisted of a minimum of
approximately 140000 sentence pairs for EN-EL, going up to 2.31 million sentence pairs for EN-RU.
Four sets of 250 English sentences each were translated into German, Greek, Portuguese and
Russian using the SMT and NMT systems. Our evaluation is based on a set of four widely used
automatic MT quality evaluation metrics: HTER (Translation Error Rate) , BLEU , METEOR
 and chrF . For the human assessment, we have selected the following state-of-the-art metrics:
fluency and adequacy, post-editing, error annotation and ranking. Professional translators were asked
to rate the translations according to those metrics and to post-edit the sentences. These procedures
are widely used in the MT field in order to assess the quality of a given MT system.
The results of this large-scale evaluation, which are reported in full in , show that NMT receives
higher scores than SMT with all four automatic evaluation metrics (even though improvements for
Portuguese are very limited), and side-by-side ranking also shows a clear preference for NMT output
across the board, for all the language pairs and MOOC domains covered in this comparative study.
We can also conclude that NMT offers improvements in terms of fluency and word order errors over
SMT, mostly due to its better handling of word reordering. In addition, fewer sentences translated with
the NMT systems include errors, and NMT seems to perform better than SMT on morphologically rich
and highly inflected target languages.
In contrast, however, adequacy does not show marked improvements with NMT, and the situation is
mixed for errors of omission, addition and mistranslation, so much so that overall NMT does not entail
noticeable reductions in post-editing effort. Moreover, in-depth investigations of automatic MT
evaluation metric scores reveal that the performance of NMT tends to degrade for longer sentences
(more than 20 tokens), where SMT appears to be more reliable: the sentence length of the MOOC text
to be translated is one of the factors to be considered in order to decide which MT system provides the
best quality. Based on this evidence, for the final stages of the TraMOOC project the decision was
made to favour the NMT approach over SMT for the language pairs under consideration, as there are
indications that this approach holds the greatest potential for quality going forward. However, applying
MT to new language pairs and other MOOC domains may present different challenges, which is why
we are hesitant to make broader conclusive generalizations.
5 CONCLUSIONS AND FUTURE WORK
This paper has discussed the outcomes of a large-scale, multi-method evaluation, comparing the
quality of SMT and NMT output for MOOC data in a diverse set of language combinations of interest
to the TraMOOC project, i.e. from English into German, Greek, Portuguese and Russian. The
evaluation involved four state-of-the-art automatic evaluation metrics (i.e. HTER, BLEU, METEOR,
and chrF), as well as a range of more labour-intensive manual methods (fluency and adequacy, post-
editing, error annotation and ranking). In conclusion, consistently with other application domains, the
large-scale multi-method evaluations based on our MOOC data suggest that the emerging neural
approach to MT offers some noticeable advantages over the competing and well-established SMT
Our findings also show that, while NMT represents an improvement over SMT in some areas, further
work is still required to consolidate the current promising performance before NMT can be recognized
as the new state-of-the-art in MT . As far as our own work in TraMOOC is concerned, subsequent
planned evaluations include the identification of source-language phenomena that are likely to cause
particularly serious errors in the output, depending on the target languages and the MT system type.
Another avenue for further research consists in task-based evaluations, which would provide a useful
addition to the range of evaluation methods that have already been applied in preparation for this
paper: task-based evaluation involves exposing real users to MOOC content machine-translated into
one of TraMOOC’s target languages, and then assessing their understanding and knowledge of that
translated material. Their performance can be judged against the baseline of students using the same
original English-language MOOC in preparation for identical tests, to give an indication of how
effective and successful the application of the MT system concerned is for the specific MOOC domain.
The application of MT to the various types of texts that incorporate a MOOC is undoubtedly a complex
task. At the end of the TraMOOC project we hope to provide a roadmap for using automatic translation
and user post-editing of MOOC materials, as well as a platform via which this work may be carried out
using state-of-the-art MT technology, as part of the ultimate aim of making MOOC resources more
accessible to non-English-speaking users. The results of our evaluations of NMT quality using MOOC
texts have so far been promising. This work is continuing at a larger scale, with results feeding back to
the MT development team, in the hope of facilitating multilingual MOOC resources that are
comprehensible and beneficial to global end users.
The TraMOOC project has received funding from the European Union’s Horizon 2020 research and
innovation programme under grant agreement № 644333. The ADAPT Centre for Digital Content
Technology at Dublin City University is funded under the Science Foundation Ireland Research
Centres Programme (Grant 13/RC/2106) and is co-funded under the European Regional Development
 M. Nanfito, MOOCs: Opportunities, Impacts, and Challenges. Massive Open Online Courses in
Colleges and Universities. CreateSpace Independent Publishing Platform, 2014.
 S.D. Krause, and C. Lowe (eds), Invasion of the MOOCs: The Promises and Perils of Massive
Open Online Courses. Parlor Press, 2014.
 D.G. Glance, M. Forsey, and M. Riley, “The pedagogical foundations of massive open online
courses,” in First Monday, vol. 18, no. 5, 2013. Retrieved from
 R. Kop, H. Fournier, and J. Mak, “A pedagogy of abundance or a pedagogy to support human
beings? Participant support on massive open online courses,” in International Review of
Research in Open and Distance Learning, vol. 12, no. 7, pp.74–93, 2011.
 J. Mackness, M. Waite, G. Roberts, and E. Lovegrove, “Learning in a small, task-oriented,
connectivist MOOC: Pedagogical issues and implications for higher education,” in The
International Review of Research in Open and Distance Learning, vol. 14, no. 4, 2013.
Retrieved from www.irrodl.org/index.php/irrodl/article/view/1548.
 E.A. Monske, and K.L. Blair (eds), Handbook of Research on Writing and Composing in the Age
of MOOCs. IGI Global, 2017.
 B. Wildavsky, “MOOCs in the Developing World: Hope or Hype?,” in International Higher
Education, no. 80, pp. 23–25, 2015. Retrieved from
 G. Christensen, A. Steinmetz, B, Alcorn, A. Bennett, D. Woods, and E.J. Emanuel, “The MOOC
Phenomenon: Who Takes Massive Open Online Courses and Why?,” in Social Science
Research Network, 2013. Retrieved from https://ssrn.com/abstract=2350964.
 T. Liyanagunawardena, S. Williams, and A. Adams, “The impact and reach of MOOCs: a
developing country’s perspective,” in eLearning Papers, no. 33, 2013. Retrieved from
 D. Laurillard, “The educational problem that MOOCs could solve: professional development for
teachers of disadvantaged students,” in Research in Learning Technology, vol. 24, no. 1, 2016.
Retrieved from www.tandfonline.com/doi/full/10.3402/rlt.v24.29369.
 S. Mak, R. Williams, and J. Mackness, “Blogs and forums as communication and learning tools
in a MOOC,” in Proceedings of the 7th International Conference on Networked Learning 2010 (L.
Dirckinck-Holmfeld, V. Hodgson, C. Jones, M. de Laat, D. McConnell, and T. Ryberg, eds.), pp.
275–284, 2010. Retrieved from
 C. Yeager, B. Hurley-Dasgupta, and C.A. Bliss, “cMOOCs and Global Learning: An Authentic
Alternative,” in Journal of Asynchronous Learning Networks, vol. 17, no. 2, pp. 133–147, 2013.
Retrieved from http://files.eric.ed.gov/fulltext/EJ1018269.pdf.
 T. Beaven, A. Cormas-Quinn, M. Hauck, B. de los Arcos, and T. Lewis, “The Open Translation
MOOC: creating online communities to transcend linguistic barriers,” in Journal of Interactive
Media in Education, 2013. Retrieved from http://jime.open.ac.uk/articles/10.5334/2013-18/.
 F. Brouns, N. Serrano Martínez-Santos, J. Civera, M. Kalz, and A. Juan, “Supporting language
diversity of European MOOCs with the EMMA platform,” in Proceedings of the European MOOC
Stakeholder Summit 2015 – Research Track, pp. 157–165, 2015. Retrieved from
 P. Koehn, H. Hoang, A. Birch, C. Callison-Burch, M. Federico, N. Bertoldi, B. Cowan, W. Shen,
C. Moran, R. Zens, C. Dyer, O. Bojar, A. Constantin, and E. Herbst, “Moses: Open Source
Toolkit for Statistical Machine Translation,” in Proceedings of the ACL 2007 Demo and Poster
Sessions, pp. 177–180, 2008.
 M.J. Israel, “Effectiveness of Integrating MOOCs in Traditional Classrooms for Undergraduate
Students,” in The International Review of Research in Open and Distributed Learning, vol. 16,
no. 5, 2015. Retrieved from www.irrodl.org/index.php/irrodl/article/view/2222/3402.
 D. Gamage, I. Perera, and S. Fernando, “A framework to analyze effectiveness of eLearning in
MOOC: Learners’ perspective,” in Proceedings of the 8th International Conference on Ubi-Media
Computing (UMEDIA), Colombo, Sri Lanka: IEEE, 2015.
 C. Milligan, A. Littlejohn, and A. Margaryan, “Patterns of Engagement in Connectivist MOOCs,”
in Journal of Online Learning and Teaching, vol. 9, no. 2, pp. 149–159, 2013. Retrieved from
 M. Bali, “MOOC Pedagogy: Gleaning Good Practice from Existing MOOCs,” in Journal of Online
Learning and Teaching, vol. 10, no. 1, pp. 44–57, 2014. Retrieved from
 A. Way, “Traditional and Emerging Use-Cases for Machine Translation,” in Proceedings of
Translating and the Computer 35, 2013.
 M. Fernández-Barrera, V. Popescu, A. Toral, F. Gaspari, and K. Choukri, “Enhancing Cross-
border EU e-commerce through Machine Translation: Needed Language Resources,
Challenges and Opportunities,” in Proceedings of the 10th Language Resources and Evaluation
Conference, pp. 4550–4556, Paris: European Language Resources Association, 2016.
 S. Castilho, J. Moorkens, F. Gaspari, I. Calixto, J. Tinsley, and A. Way, “Is Neural Machine
Translation the New State of the Art?,” in The Prague Bulletin of Mathematical Linguistics, vol.
 R. Sennrich, B. Haddow, and A. Birch, “Edinburgh Neural Machine Translation Systems for
WMT 16,” in Proceedings of the First Conferenc e on Machine Translation, vol. 2, Shared Task
Papers, pp. 371–376, 2016.
 M. Snover, B. Dorr, R. Schwartz, L. Micciulla, and J. Makhoul, “A study of translation edit rate
with targeted human annotation,” in Proceedings of the Conference of the Association for
Machine Translation in the Americas, pp. 233–231, 2006.
 K. Papineni, S. Roukos, T. Ward, and W. Zhu, “BLEU: A Method for Automatic Evaluation of
Machine Translation,” in Proceedings of the 40th Annual Meeting of the Association for
Computational Linguistics, pp. 311–318, 2002.
 A. Lavie, and A. Agarwal, “METEOR: An Automatic Metric for MT Evaluation with High Levels of
Correlation with Human Judgments,” in Proceedings of the Workshop on Statistical Machine
Translation, pp. 228–231, 2007.
 M. Popović, “chrF: character n-gram F-score for automatic MT evaluation,” in Proceedings of
the 10th Workshop on Statistical Machine Translation, pages 392–395, 2015.