RuLearn: an Open-source Toolkit for the Automatic Inference of Shallow-transfer Rules for Machine Translation

This paper presents ruLearn, an open-source toolkit for the automatic inference of rules for shallow-transfer machine translation from scarce parallel corpora and morphological dictionaries. ruLearn will make rule-based machine translation a very appealing alternative for under-resourced language pairs because it avoids the need for human experts to handcraft transfer rules and requires, in contrast to statistical machine translation, a small amount of parallel corpora (a few hundred parallel sentences proved to be sufficient). The inference algorithm implemented by ruLearn has been recently published by the same authors in Computer Speech & Language (volume 32). It is able to produce rules whose translation quality is similar to that obtained by using hand-crafted rules. ruLearn generates rules that are ready for their use in the Apertium platform, although they can be easily adapted to other platforms. When the rules produced by ruLearn are used together with a hybridisation strategy for integrating linguistic resources from shallow-transfer rule-based machine translation into phrase-based statistical machine translation (published by the same authors in Journal of Artificial Intelligence Research, volume 55), they help to mitigate data sparseness. This paper also shows how to use ruLearn and describes its implementation.Research funded by the Spanish Ministry of Economy and Competitiveness through projects TIN2009-14009-C02-01 and TIN2012-32615, by Generalitat Valenciana through grant ACIF/2010/174, and by the European Union Seventh Framework Programme FP7/2007-2013 under grant agreement PIAP-GA-2012-324414 (Abu-MaTran)

Why Catalan-Spanish Neural Machine Translation? Analysis, comparison and combination with standard Rule and Phrase-based technologies

Catalan and Spanish are two related languages given that both derive from Latin. They share similarities in several linguistic levels including morphology, syntax and semantics. This makes them particularly interesting for the MT task. Given the recent appearance and popularity of neural MT, this paper analyzes the performance of this new approach compared to the well-established rule-based and phrase-based MT systems. Experiments are reported on a large database of 180 million words. Results, in terms of standard automatic measures, show that neural MT clearly outperforms the rule-based and phrase-based MT system on in-domain test set, but it is worst in the out-of-domain test set. A naive system combination specially works for the latter. In-domain manual analysis shows that neural MT tends to improve both adequacy and fluency, for example, by being able to generate more natural translations instead of literal ones, choosing to the adequate target word when the source word has several translations and improving gender agreement. However, out-of-domain manual analysis shows how neural MT is more affected by unknown words or contexts.Postprint (published version

ANNOTATED DISJUNCT FOR MACHINE TRANSLATION

Most information found in the Internet is available in English version. However, most people in the world are non-English speaker. Hence, it will be of great advantage to have reliable Machine Translation tool for those people. There are many approaches for developing Machine Translation (MT) systems, some of them are direct, rule-based/transfer, interlingua, and statistical approaches. This thesis focuses on developing an MT for less resourced languages i.e. languages that do not have available grammar formalism, parser, and corpus, such as some languages in South East Asia. The nonexistence of bilingual corpora motivates us to use direct or transfer approaches. Moreover, the unavailability of grammar formalism and parser in the target languages motivates us to develop a hybrid between direct and transfer approaches. This hybrid approach is referred as a hybrid transfer approach. This approach uses the Annotated Disjunct (ADJ) method. This method, based on Link Grammar (LG) formalism, can theoretically handle one-to-one, many-to-one, and many-to-many word(s) translations. This method consists of transfer rules module which maps source words in a source sentence (SS) into target words in correct position in a target sentence (TS). The developed transfer rules are demonstrated on English → Indonesian translation tasks. An experimental evaluation is conducted to measure the performance of the developed system over available English-Indonesian MT systems. The developed ADJ-based MT system translated simple, compound, and complex English sentences in present, present continuous, present perfect, past, past perfect, and future tenses with better precision than other systems, with the accuracy of 71.17% in Subjective Sentence Error Rate metric

Automatic medical term generation for a low-resource language: translation of SNOMED CT into Basque

211 p. (eusk.) 148 p. (eng.)Tesi-lan honetan, terminoak automatikoki euskaratzeko sistemak garatu eta ebaluatu ditugu. Horretarako,SNOMED CT, terminologia kliniko zabala barnebiltzen duen ontologia hartu dugu abiapuntutzat, etaEuSnomed deritzon sistema garatu dugu horren euskaratzea kudeatzeko. EuSnomedek lau urratsekoalgoritmoa inplementatzen du terminoen euskarazko ordainak lortzeko: Lehenengo urratsak baliabidelexikalak erabiltzen ditu SNOMED CTren terminoei euskarazko ordainak zuzenean esleitzeko. Besteakbeste, Euskalterm banku terminologikoa, Zientzia eta Teknologiaren Hiztegi Entziklopedikoa, eta GizaAnatomiako Atlasa erabili ditugu. Bigarren urratserako, ingelesezko termino neoklasikoak euskaratzekoNeoTerm sistema garatu dugu. Sistema horrek, afixu neoklasikoen baliokidetzak eta transliterazio erregelakerabiltzen ditu euskarazko ordainak sortzeko. Hirugarrenerako, ingelesezko termino konplexuak euskaratzendituen KabiTerm sistema garatu dugu. KabiTermek termino konplexuetan agertzen diren habiaratutakoterminoen egiturak erabiltzen ditu euskarazko egiturak sortzeko, eta horrela termino konplexuakosatzeko. Azken urratsean, erregeletan oinarritzen den Matxin itzultzaile automatikoa osasun-zientziendomeinura egokitu dugu, MatxinMed sortuz. Horretarako Matxin domeinura egokitzeko prestatu dugu,eta besteak beste, hiztegia zabaldu diogu osasun-zientzietako testuak itzuli ahal izateko. Garatutako lauurratsak ebaluatuak izan dira metodo ezberdinak erabiliz. Alde batetik, aditu talde txiki batekin egin dugulehenengo bi urratsen ebaluazioa, eta bestetik, osasun-zientzietako euskal komunitateari esker egin dugunMedbaluatoia kanpainaren baitan azkeneko bi urratsetako sistemen ebaluazioa egin da