acl acl2012 acl2012-138 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Andrejs Vasiljevs ; Raivis Skadins ; Jorg Tiedemann
Abstract: Machine Translation Raivis Skadiņš TILDE Vienbas gatve 75a, Riga LV-1004, LATVIA raivi s . s kadins @ ti lde . lv Jörg Tiedemann Uppsala University Box 635, Uppsala SE-75 126, SWEDEN j org .t iedemann@ l ingfi l .uu . se the Universities of Copenhagen, and Uppsala. Edinburgh, Zagreb, To facilitate the creation and usage of custom SMT systems we have created a cloud-based platform for do-it-yourself MT. The platform is developed in the EU collaboration project LetsMT! . This system demonstration paper presents the motivation in developing the LetsMT! platform, its main features, architecture, and an evaluation in a practical use case. 1
Reference: text
sentIndex sentText sentNum sentScore
1 : A Cloud-Based Platform for Do-It-Yourself AndrTejIsL VDaEs iļjevs Vienbas gatve 75a, Riga LV-1004, LATVIA andre j s @ t i lde . [sent-2, score-0.065]
2 com Abstract Machine Translation Raivis Skadiņš TILDE Vienbas gatve 75a, Riga LV-1004, LATVIA raivi s . [sent-3, score-0.034]
3 Edinburgh, Zagreb, To facilitate the creation and usage of custom SMT systems we have created a cloud-based platform for do-it-yourself MT. [sent-9, score-0.171]
4 The platform is developed in the EU collaboration project LetsMT! [sent-10, score-0.195]
5 This system demonstration paper presents the motivation in developing the LetsMT! [sent-12, score-0.038]
6 develops a user-driven MT “factory in the cloud” enabling web users to get customised MT that better fits their needs. [sent-16, score-0.114]
7 Harnessing the huge potential of the web together with open statistical machine translation (SMT) technologies, LetsMT! [sent-17, score-0.138]
8 has created an online collaborative platform for data sharing and MT building. [sent-18, score-0.137]
9 project is to facilitate the use of open source SMT tools and to involve users in the collection of training data. [sent-20, score-0.158]
10 project extends the use of existing state- of-the-art SMT methods by providing them as cloud-based services. [sent-22, score-0.058]
11 An easy-to-use web interface empowers users to participate in data collection and MT customisation to increase the quality, domain coverage, and usage of MT. [sent-23, score-0.159]
12 project partners are companies TILDE (coordinator), Moravia, and SemLab, and 43 2 LetsMT! [sent-25, score-0.097]
13 , 201 1) gathers public and user-provided MT training data and enables generation of multiple MT systems by combining and prioritising this data. [sent-28, score-0.082]
14 Users can upload their parallel corpora to an online repository and generate user-tailored SMT systems based on data selected by the user. [sent-29, score-0.185]
15 Authenticated users with appropriate permissions can also store private corpora that can be seen and used only by this user (or a designated user group). [sent-30, score-0.247]
16 repository is kept in internal format, and only its metadata is provided to the user. [sent-32, score-0.166]
17 The uploaded data can only be used for SMT training. [sent-34, score-0.059]
18 A user creates SMT system definition by specifying a few basic parameters like system name, source/target languages, domain, and choosing corpora (parallel for translation models or monolingual for language models) to use for the particular system. [sent-36, score-0.229]
19 Tuning and evaluation data can be automatically extracted from the training corpora or specified by the user. [sent-37, score-0.032]
20 The access level of the system can also be specified - whether it will be public or accessible only to the particular user or user group. [sent-38, score-0.255]
21 t hIat ncu r 7e3n0tl y comnitlali on s parallel sentences in almost 50 languages. [sent-46, score-0.053]
22 been When the system is specified, the user can begin training it. [sent-50, score-0.127]
23 Progress of the training can be monitored on the dynamic training chart (Figure 1). [sent-51, score-0.125]
24 It provides a detailed visualisation of the training process showing (i) steps queued for execution of a particular training task, (ii) current execution status of active training steps, and (iii) steps where any errors have occurred. [sent-52, score-0.213]
25 The training chart remains available after the training to facilitate analysis of the performed trainings. [sent-53, score-0.096]
26 The last step of the training task is automatic evaluation using BLEU, NIST, TER, and METEOR scores. [sent-54, score-0.032]
27 A successfully trained SMT system can be started and used for translation in several ways: on the translation webpage of LetsMT! [sent-55, score-0.23]
28 plug-ins in computerassisted translation (CAT) tools for professional translation; integrating the LetsMT! [sent-57, score-0.131]
29 plug-ins for IE and FireFox to integrate translation into the browsers; using LetsMT! [sent-59, score-0.096]
30 allows for several system instances to run simultaneously to speed up translation and balance the workload from numerous translation requests. [sent-62, score-0.23]
31 user authentication and authorisation mechanisms control access rights to private 44 3 SMT Training and Decoding Facilities The SMT training and decoding facilities of LetsMT! [sent-64, score-0.269]
32 One of the important achievements of the project is the adaptation of the Moses toolkit to fit into the rapid training, updating, and interactive access environment of the LetsMT! [sent-66, score-0.138]
33 , 2007) provides a complete statistical translation system distributed under the LGPL license. [sent-69, score-0.176]
34 Moses includes all of the components needed to preprocess data and to train language and translation models. [sent-70, score-0.096]
35 While the use of the software is not closely monitored, Moses is known to be in commercial use by companies such as Systran (Dugast et al. [sent-72, score-0.039]
36 SMT training is automated using the Moses experiment management system (Koehn, 2010). [sent-82, score-0.07]
37 , 2009); a server mode version of the Moses decoder and multithreaded decoding; multiple translation models; distributed language models (Brants et al. [sent-86, score-0.221]
38 Many improvements in the Moses experiment management system were implemented to speed up SMT system training and to use the full potential of the HPC cluster. [sent-88, score-0.108]
39 We revised and improved Moses training routines (i) by finding tasks that are executed sequentially but can be executed in parallel and (ii) by splitting big training tasks into smaller ones and executing them in parallel. [sent-89, score-0.175]
40 It has (i) an interface layer implementing the user interface and APIs with external systems, (ii) an application logic layer for the system logic, (iii) a data storage layer consisting of file and database storage, and (iv) a high performance computing (HPC) cluster. [sent-92, score-0.906]
41 system performs various time and resource consuming tasks; these tasks are defined by the application logic and data storage and are sent to the HPC cluster for execution. [sent-94, score-0.43]
42 Human users can access the system through web browsers by using the LetsMT! [sent-98, score-0.205]
43 External systems such as Computer Aided Translation (CAT) tools and web browser plug-ins can access the LetsMT! [sent-100, score-0.13]
44 The public API is available through both REST/JSON and SOAP protocol web services. [sent-102, score-0.131]
45 An HTTPS protocol is used to ensure secure user authentication and secure data transfer. [sent-103, score-0.229]
46 45 The application logic layer contains a set of modules responsible for the main functionality and logic of the system. [sent-104, score-0.396]
47 It receives queries and commands from the interface layer and prepares answers or performs tasks using data storage and the HPC cluster. [sent-105, score-0.34]
48 This layer contains several modules such as the Resource Repository Adapter, the User Manager, the SMT Training Manager, etc. [sent-106, score-0.195]
49 The interface layer accesses the application logic layer through the REST/JSON and SOAP protocol web services. [sent-107, score-0.492]
50 The same protocols are used for communication between modules in the application logic layer. [sent-108, score-0.193]
51 As training data may change (for example, grow), the RR is based on a versioncontrolled file system (currently we use SVN as the backend system). [sent-110, score-0.267]
52 A key-value store is used to keep metadata and statistics about training data and trained SMT systems. [sent-111, score-0.156]
53 Modules from the application logic layer and HPC cluster access RR through a REST-based web service interface. [sent-112, score-0.388]
54 Modules from the application logic and data storage layers create jobs and send them to the HPC cluster for execution. [sent-114, score-0.37]
55 The HPC cluster is responsible for accepting, scheduling, dispatching, and managing remote and distributed execution of large numbers of standalone, parallel, or interactive jobs. [sent-115, score-0.134]
56 It also manages and schedules the allocation of distributed resources such as processors, memory, and disk space. [sent-116, score-0.076]
57 HPC cluster is based on the Oracle Grid Engine (SGE). [sent-118, score-0.053]
58 The majority of services run on Linux platforms (Moses, RR, data processing tools, etc. [sent-121, score-0.055]
59 The Web server and application logic services run on a Microsoft Windows platform. [sent-123, score-0.222]
60 The system hardware architecture is designed to be highly scalable. [sent-124, score-0.112]
61 system including the HPC cluster is hosted by Amazon Web Services infrastructure, which provides easy access to on-demand computing and storage resources. [sent-128, score-0.313]
62 system has to store and process large amounts of SMT training data (parallel and monolingual corpora) as well as trained models of SMT systems. [sent-130, score-0.129]
63 The general architecture of the Resource Repository is illustrated in Figure 3. [sent-133, score-0.043]
64 It is implemented in terms of a modular package that can easily be installed in a distributed environment. [sent-134, score-0.042]
65 RR services are provided via Web API’s and secure HTTP requests. [sent-135, score-0.102]
66 Data storage can be distributed over several servers as is illustrated in Figure 3. [sent-136, score-0.24]
67 Storage servers communicate with the central database server that manages all metadata records attached to resources in the RR. [sent-137, score-0.176]
68 Data resources are organised in slots that correspond to file systems with user-specific branches. [sent-138, score-0.06]
69 Currently, the RR package implements two storage backends: a plain file system and a version-controlled file system based on subversion (SVN). [sent-139, score-0.365]
70 Furthermore, they keep track of modifications and file histories to make it possible to backtrack to prior revisions. [sent-142, score-0.06]
71 Another interesting feature is the possibility to create cheap copies of entire branches that can be used to enable data modifications by other users without compromising data integrity for others. [sent-144, score-0.033]
72 In general, the RR implementation is modular, other storage backends may be added later, and each individual slot can use its own backend type. [sent-146, score-0.306]
73 We decided to integrate a modern key-value store into the platform in order to allow a maximum of flexibility. [sent-148, score-0.196]
74 In contrast to traditional relational databases, key-value stores allow the storage of arbitrary data sets based on pairs of keys and values without being restricted to a pre-defined schema or a fixed data model. [sent-149, score-0.214]
75 In particular, we use the table mode of TokyoCabinet that supports storage of arbitrary data records connected to a single key in the database. [sent-151, score-0.234]
76 We use resource URL’s in our repository to define unique keys in the database, and data records attached to these keys may include any number of key-value pairs. [sent-152, score-0.242]
77 In this way, we can add any kind of information to each addressable resource in the RR. [sent-153, score-0.051]
78 The software also supports keys with unordered lists of values, which is useful for metadata such as languages (in a data collection) and for many other purposes. [sent-154, score-0.14]
79 Using TokyoCabinet as our backend, we implemented a key-value store for metadata in the RR that can easily be extended and queried from the frontend of the LetsMT! [sent-157, score-0.183]
80 Yet another important feature of the RR is the collection of import modules that take care of validation and conversion of user-provided SMT training material. [sent-159, score-0.224]
81 Pre-aligned parallel data can be uploaded in TMX, XLIFF, and Moses formats. [sent-163, score-0.112]
82 We also support the upload of compressed archives in zip and tar format. [sent-165, score-0.062]
83 Furthermore, our system also includes tools for automatic sentence alignment. [sent-167, score-0.073]
84 Finally, we also integrated a general batchqueuing system (SGE) to run off-line processes such as import jobs. [sent-170, score-0.156]
85 In this way, we further increase the scalability of the system by taking the load off repository servers. [sent-171, score-0.17]
86 Data uploads automatically trigger appropriate import jobs that will be queued on the grid engine using a dedicated job web-service API. [sent-172, score-0.279]
87 6 Evaluation for Usage in Localisation One of the usage scenarios particularly targeted by the project is application in the localisation and translation industry. [sent-173, score-0.288]
88 Localisation companies usually have collected significant amounts of parallel data in the form of translation memories. [sent-174, score-0.188]
89 They are interested in using this data to create customised MT engines that can increase productivity of translators. [sent-175, score-0.141]
90 In addition to translation candidates from translation memories, translators receive translation suggestions provided by the selected MT engine running on LetsMT! [sent-179, score-0.362]
91 As part of the system evaluation, project partner Moravia used the LetsMT! [sent-181, score-0.096]
92 platform to train and evaluate SMT systems for Polish and Czech. [sent-182, score-0.137]
93 9M parallel sentences coming from Moravia translation memories in the IT and tech domain part of the Czech National Corpus. [sent-184, score-0.18]
94 5M parallel sentences from Moravia production data in the IT domain. [sent-188, score-0.053]
95 For evaluation of English-Latvian translation, TILDE created a MT system using a significantly larger corpus of 5. [sent-191, score-0.038]
96 project is on track to fulfill its goal to democratise the creation and usage of custom SMT systems. [sent-199, score-0.092]
97 The architecture of the platform and Resource Repository enables scalability of the system and very large amounts of data to be handled in a variety of formats. [sent-202, score-0.249]
98 Evaluation shows a strong increase in translation productivity by using LetsMT! [sent-203, score-0.198]
99 project has received funding from the ICT Policy Support Programme (ICT PSP), Theme 5 – Multilingual web, grant agreement 250456. [sent-206, score-0.058]
100 Selective addition of corpus-extracted phrasal lexical rules to a rule-based machine translation system. [sent-212, score-0.096]
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