Mixture-Model Adaptation for SMT

نویسندگان

  • George Foster
  • Roland Kuhn
چکیده

We describe a mixture-model approach to adapting a Statistical Machine Translation System for new domains, using weights that depend on text distances to mixture components. We investigate a number of variants on this approach, including cross-domain versus dynamic adaptation; linear versus loglinear mixtures; language and translation model adaptation; different methods of assigning weights; and granularity of the source unit being adapted to. The best methods achieve gains of approximately one BLEU percentage point over a state-of-the art non-adapted baseline system.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Adaptation of Reordering Models for Statistical Machine Translation

Previous research on domain adaptation (DA) for statistical machine translation (SMT) has mainly focused on the translation model (TM) and the language model (LM). To the best of our knowledge, there is no previous work on reordering model (RM) adaptation for phrasebased SMT. In this paper, we demonstrate that mixture model adaptation of a lexicalized RM can significantly improve SMT performanc...

متن کامل

Domain Adaptation in Statistical Machine Translation with Mixture Modelling

Mixture modelling is a standard technique for density estimation, but its use in statistical machine translation (SMT) has just started to be explored. One of the main advantages of this technique is its capability to learn specific probability distributions that better fit subsets of the training dataset. This feature is even more important in SMT given the difficulties to translate polysemic ...

متن کامل

A Comparison of Mixture and Vector Space Techniques for Translation Model Adaptation

In this paper, we propose two extensions to the vector space model (VSM) adaptation technique (Chen et al., 2013b) for statistical machine translation (SMT), both of which result in significant improvements. We also systematically compare the VSM techniques to three mixture model adaptation techniques: linear mixture, log-linear mixture (Foster and Kuhn, 2007), and provenance features (Chiang e...

متن کامل

Domain Adaptation in Statistical Machine Translation of User-Forum Data using Component-Level Mixture Modelling

This paper reports experiments on adapting components of a Statistical Machine Translation (SMT) system for the task of translating online user-generated forum data from Symantec. Such data is monolingual, and differs from available bitext MT training resources in a number of important respects. For this reason, adaptation techniques are important to achieve optimal results. We investigate the ...

متن کامل

Domain Adaptation Techniques for Machine Translation and Their Evaluation in a Real-World Setting

Statistical Machine Translation (SMT) is currently used in real-time and commercial settings to quickly produce initial translations for a document which can later be edited by a human. The SMT models specialized for one domain often perform poorly when applied to other domains. The typical assumption that both training and testing data are drawn from the same distribution no longer applies. Th...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2007