Multi-domain Adaptation for Statistical Machine Translation Based on Feature Augmentation (2016)

Abstract: Domain adaptation is a major challenge when applying machine translation to practical tasks. In this paper, we present domain adaptation methods for machine translation that assume multiple domains. The proposed methods combine two model types: a corpus-concatenated model covering multiple domains and single-domain models that are accurate but sparse in specific domains. We combine the advantages of both models using feature augmentation for domain adaptation in machine learning. Our experimental results show that the BLEU scores of the proposed method clearly surpass those of single-domain models for low-resource domains. For high-resource domains, the scores of the proposed method were superior to those of both single-domain and corpusconcatenated models. Even in domains having a million bilingual sentences, the translation quality was at least preserved and even improved in some domains. These results demonstrate that state-of-the-art domain adaptation can be realized with appropriate settings, even when using standard log-linear models.

## introduction machine translation is used for translating a variety of text types, including speech. however, it remains challenging to appropriately translate texts across all domains and only a limited number of domains have been targeted. the most promising approach to improve translation quality is to train the translator on massive bilingual corpora. however, collecting such corpora is challenging and expensive in several domains. domain adaptation, which improves target domain quality by using data from another domain, has been proposed as a solution @xcite @xcite @xcite . this technique is important when applying machine translation to practical tasks. this paper presents methods of domain adaptation for statistical machine translation (smt) that assume multiple domains. the proposed methods combine multiple models using log-linear interpolation. these are simple yet effective approaches to take advantage of multiple domains based on feature augmentation @xcite , a domain adaptation technique used in machine learning. we propose the following two methods. both methods use a corpus-concatenated model, which covers multiple domains and contains few unknown words, and single-domain models, which are accurate in their specific domains. in addition, we tune the hyper-parameter of the multiple-model combination. with appropriate settings, state-of-the-art domain adaptation can be realized even when using standard log-linear models. in this study, we use phrase-based statistical machine translation (pbsmt) @xcite with preordering. the remainder of this paper is organized as follows. section 2 briefly reviews domain adaptation in machine translation. section 3 explains our proposed methods in detail. section 4 discusses the characteristics of our methods through experiments, and section 5 concludes the paper. ## domain adaptation for statistical machine translation domain adaptation is applied when the target domain (in-domain) data are insufficient but data from another domain (out-domain) are available in sufficient quantities. domain adaptation in machine translation aims to improve the translation quality of in-domain texts using both in-domain and out-domain data. there are two types of domains: those that are predefined, such as "news" and "web," and those that are artificially created via automatic clustering. even when using automatic clustering, the translation quality can be improved in some cases @xcite . however, in this study, we have used predefined domains. ## conclusions in this paper, we presented multi-domain adaptation methods for statistical machine translation. the proposed methods combined the corpus-concatenated model, which has high coverage and few unknown words, with single-domain models, which ensure precision of the feature functions. to take advantage of the benefits of both models, we applied feature augmentation to machine translation. in addition, we tuned the empty value to balance the two models. in both joint and independent optimization, the translation quality was improved or at the same level compared with the single-domain models in our experiments. the resulting bleu scores of the proposed method clearly surpassed those of the single-domain models in lowresource domains. even in domains with a million training sentences, the translation quality remained at least equal, and in some domains, it was improved. these results show that stateof-the-art domain adaptation can be realized with appropriate settings, even when using standard log-linear models. the proposed methods can be easily applied to other translation strategies, such as treeto-tree translation. we plan to apply our methods to tree-to-tree translation and evaluate the effects.