Online Learning for Statistical Machine Translation
نویسندگان
چکیده
منابع مشابه
Online Learning for Interactive Statistical Machine Translation
State-of-the-art Machine Translation (MT) systems are still far from being perfect. An alternative is the so-called Interactive Machine Translation (IMT) framework. In this framework, the knowledge of a human translator is combined with a MT system. The vast majority of the existing work on IMT makes use of the well-known batch learning paradigm. In the batch learning paradigm, the training of ...
متن کاملOnline Learning for Statistical Machine Translation
We present online learning techniques for statistical machine translation (SMT). The availability of large training data sets that grow constantly over time is becoming more and more frequent in the field of SMT—for example, in the context of translation agencies or the daily translation of government proceedings. When new knowledge is to be incorporated in the SMT models, the use of batch lear...
متن کاملActive Learning for Multilingual Statistical Machine Translation
Statistical machine translation (SMT) models require bilingual corpora for training, and these corpora are often multilingual with parallel text in multiple languages simultaneously. We introduce an active learning task of adding a new language to an existing multilingual set of parallel text and constructing high quality MT systems, from each language in the collection into this new target lan...
متن کاملNon-linear Learning for Statistical Machine Translation
Modern statistical machine translation (SMT) systems usually use a linear combination of features to model the quality of each translation hypothesis. The linear combination assumes that all the features are in a linear relationship and constrains that each feature interacts with the rest features in an linear manner, which might limit the expressive power of the model and lead to a under-fit m...
متن کاملTransductive learning for statistical machine translation
Statistical machine translation systems are usually trained on large amounts of bilingual text and monolingual text in the target language. In this paper we explore the use of transductive semi-supervised methods for the effective use of monolingual data from the source language in order to improve translation quality. We propose several algorithms with this aim, and present the strengths and w...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Computational Linguistics
سال: 2016
ISSN: 0891-2017,1530-9312
DOI: 10.1162/coli_a_00244