Domain adaptation strategies in statistical machine translation: a brief overview

© Cambridge University Press, 2015.Statistical machine translation (SMT) is gaining interest given that it can easily be adapted to any pair of languages. One of the main challenges in SMT is domain adaptation because the performance in translation drops when testing conditions deviate from training conditions. Many research works are arising to face this challenge. Research is focused on trying to exploit all kinds of material, if available. This paper provides an overview of research, which copes with the domain adaptation challenge in SMT.Peer ReviewedPostprint (author's final draft

Basque-to-Spanish and Spanish-to-Basque machine translation for the health domain

[EU]Master Amaierako Lan honek medikuntza domeinuko euskara eta gaztelera arteko itzulpen automatiko sistema bat garatzeko helburuarekin emandako lehenengo urratsak aurkezten ditu. Corpus elebidun nahikoaren faltan, hainbat esperimentu burutu dira Itzulpen Automatiko Neuronalean erabiltzen diren parametroak domeinuz kanpoko corpusean aztertzeko; medikuntza domeinuan izandako jokaera ebaluatzeko ordea, eskuz itzulitako corpusa erabili da medikuntza domeinuko corpusen presentzia handituz entrenatutako sistema desberdinak probatzeko. Lortutako emaitzek deskribatutako helbururako bidean lehenengo aurrerapausoa suposatzen dute.[EN]This project presents the initial steps towards the objective of developing a Machine Translation system for the health domain between Basque and Spanish. In the absence of a big enough bilingual corpus, several experiments have been carried out to test different Neural Machine Translation parameters on an out-of-domain corpus; while performance on the health domain has been evaluated with a manually translated corpus in different systems trained with increasing presence of health domain corpora. The results obtained represent a first step forward to the described objective

Experiments on domain adaptation for English-Hindi SMT

Statistical Machine Translation (SMT) systems are usually trained on large amounts of bilingual text and monolingual target language text. If a significant amount of out-of-domain data is added to the training data, the quality of translation can drop. On the other hand, training an SMT system on a small amount of training material for given indomain data leads to narrow lexical coverage which again results in a low translation quality. In this paper, (i) we explore domain-adaptation techniques to combine large out-of-domain training data with small-scale in-domain training data for English—Hindi statistical machine translation and (ii) we cluster large out-of-domain training data to extract sentences similar to in-domain sentences and apply adaptation techniques to combine clustered sub-corpora with in-domain training data into a unified framework, achieving a 0.44 absolute corresponding to a 4.03% relative improvement in terms of BLEU over the baseline

Adaptation of machine translation for multilingual information retrieval in the medical domain

Objective. We investigate machine translation (MT) of user search queries in the context of cross-lingual information retrieval (IR) in the medical domain. The main focus is on techniques to adapt MT to increase translation quality; however, we also explore MT adaptation to improve eectiveness of cross-lingual IR. Methods and Data. Our MT system is Moses, a state-of-the-art phrase-based statistical machine translation system. The IR system is based on the BM25 retrieval model implemented in the Lucene search engine. The MT techniques employed in this work include in-domain training and tuning, intelligent training data selection, optimization of phrase table configuration, compound splitting, and exploiting synonyms as translation variants. The IR methods include morphological normalization and using multiple translation variants for query expansion. The experiments are performed and thoroughly evaluated on three language pairs: Czech–English, German–English, and French–English. MT quality is evaluated on data sets created within the Khresmoi project and IR eectiveness is tested on the CLEF eHealth 2013 data sets. Results. The search query translation results achieved in our experiments are outstanding – our systems outperform not only our strong baselines, but also Google Translate and Microsoft Bing Translator in direct comparison carried out on all the language pairs. The baseline BLEU scores increased from 26.59 to 41.45 for Czech–English, from 23.03 to 40.82 for German–English, and from 32.67 to 40.82 for French–English. This is a 55% improvement on average. In terms of the IR performance on this particular test collection, a significant improvement over the baseline is achieved only for French–English. For Czech–English and German–English, the increased MT quality does not lead to better IR results. Conclusions. Most of the MT techniques employed in our experiments improve MT of medical search queries. Especially the intelligent training data selection proves to be very successful for domain adaptation of MT. Certain improvements are also obtained from German compound splitting on the source language side. Translation quality, however, does not appear to correlate with the IR performance – better translation does not necessarily yield better retrieval. We discuss in detail the contribution of the individual techniques and state-of-the-art features and provide future research directions

Cross-Lingual Adaptation using Structural Correspondence Learning

Cross-lingual adaptation, a special case of domain adaptation, refers to the transfer of classification knowledge between two languages. In this article we describe an extension of Structural Correspondence Learning (SCL), a recently proposed algorithm for domain adaptation, for cross-lingual adaptation. The proposed method uses unlabeled documents from both languages, along with a word translation oracle, to induce cross-lingual feature correspondences. From these correspondences a cross-lingual representation is created that enables the transfer of classification knowledge from the source to the target language. The main advantages of this approach over other approaches are its resource efficiency and task specificity. We conduct experiments in the area of cross-language topic and sentiment classification involving English as source language and German, French, and Japanese as target languages. The results show a significant improvement of the proposed method over a machine translation baseline, reducing the relative error due to cross-lingual adaptation by an average of 30% (topic classification) and 59% (sentiment classification). We further report on empirical analyses that reveal insights into the use of unlabeled data, the sensitivity with respect to important hyperparameters, and the nature of the induced cross-lingual correspondences

In no uncertain terms : a dataset for monolingual and multilingual automatic term extraction from comparable corpora

Automatic term extraction is a productive field of research within natural language processing, but it still faces significant obstacles regarding datasets and evaluation, which require manual term annotation. This is an arduous task, made even more difficult by the lack of a clear distinction between terms and general language, which results in low inter-annotator agreement. There is a large need for well-documented, manually validated datasets, especially in the rising field of multilingual term extraction from comparable corpora, which presents a unique new set of challenges. In this paper, a new approach is presented for both monolingual and multilingual term annotation in comparable corpora. The detailed guidelines with different term labels, the domain- and language-independent methodology and the large volumes annotated in three different languages and four different domains make this a rich resource. The resulting datasets are not just suited for evaluation purposes but can also serve as a general source of information about terms and even as training data for supervised methods. Moreover, the gold standard for multilingual term extraction from comparable corpora contains information about term variants and translation equivalents, which allows an in-depth, nuanced evaluation

Basque-to-Spanish and Spanish-to-Basque machine translation for the health domain

[EU]Master Amaierako Lan honek medikuntza domeinuko euskara eta gaztelera arteko itzulpen automatiko sistema bat garatzeko helburuarekin emandako lehenengo urratsak aurkezten ditu. Corpus elebidun nahikoaren faltan, hainbat esperimentu burutu dira Itzulpen Automatiko Neuronalean erabiltzen diren parametroak domeinuz kanpoko corpusean aztertzeko; medikuntza domeinuan izandako jokaera ebaluatzeko ordea, eskuz itzulitako corpusa erabili da medikuntza domeinuko corpusen presentzia handituz entrenatutako sistema desberdinak probatzeko. Lortutako emaitzek deskribatutako helbururako bidean lehenengo aurrerapausoa suposatzen dute.[EN]This project presents the initial steps towards the objective of developing a Machine Translation system for the health domain between Basque and Spanish. In the absence of a big enough bilingual corpus, several experiments have been carried out to test different Neural Machine Translation parameters on an out-of-domain corpus; while performance on the health domain has been evaluated with a manually translated corpus in different systems trained with increasing presence of health domain corpora. The results obtained represent a first step forward to the described objective