Handling terminology is an important matter in a translation workflow . However , current Machine Translation ( MT ) systems do not yet propose anything proactive upon tools which assist in managing terminological databases . In this work , we investigate several enhancements to analogical learning and test our implementation on translating medical terms . We show that the analogical engine works equally well when translating from and into a morphologically rich language , or when dealing with language pairs written in different scripts . Combining it with a phrasebased statistical engine leads to significant improvements . If machine translation is to meet commercial needs , it must offer a sensible approach to translating terms . Currently , MT systems offer at best database management tools which allow a human ( typically a translator , a terminologist or even the vendor of the system ) to specify bilingual terminological entries . More advanced tools are meant to identify inconsistencies in terminological translations and might prove useful in controlledlanguage situations ( Itagaki et al . , 2007 ) . One approach to translate terms consists in using a domain-specific parallel corpus with standard alignment techniques ( Brown et al . , 1993 ) to mine new translations . Massive amounts of parallel data are certainly available in several pairs of languages for domains such as parliament debates or the like . However , having at our disposal a domain-specific ( e.g. computer science ) bitext with an adequate coverage is another issue . One might argue that domain-specific comparable ( or perhaps unrelated ) corpora are easier to acquire , in which case context-vector techniques ( Rapp , 1995 ; Fung and McKeown , 1997 ) can be used to identify the translation of terms . We certainly agree with that point of view to a certain extent , but as discussed by Morin et al . ( 2007 ) , for many specific domains and pairs of languages , such resources simply do not exist . Furthermore , the task of translation identification is more difficult and error-prone . Analogical learning has recently regained some interest in the NLP community . Lepage and Denoual ( 2005 ) proposed a machine translation system entirely based on the concept of formal analogy , that is , analogy on forms . Stroppa and Yvon ( 2005 ) applied analogical learning to several morphological tasks also involving analogies on words . Langlais and Patry ( 2007 ) applied it to the task of translating unknown words in several European languages , an idea investigated as well by Denoual ( 2007 ) for a Japanese to English translation task . In this study , we improve the state-of-the-art of analogical learning by ( i ) proposing a simple yet effective implementation of an analogical solver ; ( ii ) proposing an efficient solution to the search issue embedded in analogical learning , ( iii ) investigating whether a classifier can be trained to recognize bad candidates produced by analogical learning . We evaluate our analogical engine on the task of translating terms of the medical domain ; a domain well-known for its tendency to create new words , many of which being complex lexical constructions . Our experiments involve five language pairs , including languages with very different morphological systems . In the remainder of this paper , we first present in Section 2 the principle of analogical learning . Practical issues in analogical learning are discussed in Section 3 along with our solutions . In Section 4 , we report on experiments we conducted with our analogical device . We conclude this study and discuss future work in Section 5 . In this study , we proposed solutions to practical issues involved in analogical learning . A simple yet effective implementation of a solver is described . A search strategy is proposed which outperforms the one described in ( Langlais and Patry , 2007 ) . Also , we showed that a classifier trained to select good candidate translations outperforms the most-frequently-generated heuristic used in several works on analogical learning . Our analogical device was used to translate medical terms in different language pairs . The approach rates comparably across the 10 translation directions we considered . In particular , we do not see a drop in performance when translating into a morphology rich language ( such as Finnish ) , or when translating into languages with different scripts . Averaged over all translation directions , the best variant could translate in first position 21 % of the terms with a precision of 57 % , while at best , one could translate 30 % of the terms with a perfect precision . We show that the analogical translations are of better quality than those produced by a phrase-based engine trained at the character level , albeit with much lower recall . A straightforward combination of both approaches led an improvement of 5.3 BLEU points over the SMT alone . Better SMT performance could be obtained with a system based on morphemes , see for instance ( Toutanova et al . , 2008 ) . However , since lists of morphemes specific to the medical domain do not exist for all the languages pairs we considered here , unsupervised methods for acquiring morphemes would be necessary , which is left as a future work . In any case , this comparison is meaningful , since both the SMT and the analogical device work at the character level . This work opens up several avenues . First , we will test our approach on terminologies from different domains , varying the size of the training material . Second , analyzing the segmentation induced by analogical learning would be interesting . Third , we need to address the problem of combining the translations produced by analogy into a front-end statistical translation engine . Last , there is no reason to constrain ourselves to translating terminology only . We targeted this task in the first place , because terminology typically plugs translation systems , but we think that analogical learning could be useful for translating infrequent entities .

Challenge: Existing machine translation systems do not manage domain-specific terms mainly due to the expensiveness for certain language pairs and domains.
Approach: They propose simple methods to improve existing analogy solvers and apply them to the translation of medial terms.
Outcome: The proposed analogy solvers significantly improve machine translation of the medical domain for morphologically rich languages and language pairs in different scripts.