We present morphogen, a tool for improving translation into morphologically rich languages with synthetic phrases. We approach the problem of translating into morphologically rich languages in two phases. First, an inflection model is learned to predict target word inflections from source side context. Then this model is used to create additional sentence specific translation phrases. These “synthetic phrases” augment the standard translation grammars and decoding proceeds normally with a standard translation model. We present an open source Python implementation of our method, as well as a method of obtaining an unsupervised morphological analysis of the target language when no supervised analyzer is available.</p
This paper presents a language to specify the rules of a morphological transfer module for a machine...
In this paper, a novel algorithm for incorporating morpho-logical knowledge into statistical machine...
The field of statistical natural language processing has been turning toward morpholog-ically rich l...
<p>We present morphogen, a tool for improving translation into morphologically rich languages with s...
Translation into morphologically rich lan-guages is an important but recalcitrant prob-lem in MT. We...
Translation into morphologically rich languages is an important but recalcitrant problem in MT. We p...
Abstract We propose a language-independent approach for improving statistical machine translation fo...
We propose a novel pipeline for translation into morphologically rich languages which consists of tw...
We improve the quality of statistical machine translation (SMT) by applying models that predict word...
Abstract. This paper describes the integration of morpho-syntactic information in phrase-based and s...
We address the problem of translating from morphologically poor to morphologically rich languages by...
Translating into morphologically rich languages is difficult. Although the coverage of lemmas may...
We present a novel morphological analysis technique which induces a morphological and syntactic sym...
Translation into morphologically-rich languages challenges neural machine translation (NMT) models w...
When translating between two languages that differ in their degree of morpholog-ical synthesis, synt...
This paper presents a language to specify the rules of a morphological transfer module for a machine...
In this paper, a novel algorithm for incorporating morpho-logical knowledge into statistical machine...
The field of statistical natural language processing has been turning toward morpholog-ically rich l...
<p>We present morphogen, a tool for improving translation into morphologically rich languages with s...
Translation into morphologically rich lan-guages is an important but recalcitrant prob-lem in MT. We...
Translation into morphologically rich languages is an important but recalcitrant problem in MT. We p...
Abstract We propose a language-independent approach for improving statistical machine translation fo...
We propose a novel pipeline for translation into morphologically rich languages which consists of tw...
We improve the quality of statistical machine translation (SMT) by applying models that predict word...
Abstract. This paper describes the integration of morpho-syntactic information in phrase-based and s...
We address the problem of translating from morphologically poor to morphologically rich languages by...
Translating into morphologically rich languages is difficult. Although the coverage of lemmas may...
We present a novel morphological analysis technique which induces a morphological and syntactic sym...
Translation into morphologically-rich languages challenges neural machine translation (NMT) models w...
When translating between two languages that differ in their degree of morpholog-ical synthesis, synt...
This paper presents a language to specify the rules of a morphological transfer module for a machine...
In this paper, a novel algorithm for incorporating morpho-logical knowledge into statistical machine...
The field of statistical natural language processing has been turning toward morpholog-ically rich l...