Feature-Rich Statistical Translation of Noun Phrases

Feature-Rich Statistical Translation of Noun Phrases Philipp Koehn and Kevin Knight Information Sciences Institute Department of Computer Science University of Southern California koehn@isi.edu, knight@isi.edu Abstract We define noun phrase translation as a subtask of machine translation. This en- ables us to build a dedicated noun phrase translation subsystem that improves over the currently best general statistical ma- chine translation methods by incorporat- ing special modeling and special features. We achieved 65.5% translation accuracy in a German-English translation task vs. 53.2% with IBM Model 4. 1 Introduction Recent research in machine translation challenges us with the exciting problem of combining statisti- cal methods with prior linguistic knowledge. The power of statistical methods lies in the quick acquisi- tion of knowledge from vast amounts of data, while linguistic analysis both provides a fitting framework for these methods and contributes additional knowl- edge sources useful for finding correct translations. We present work that successfully defines a sub- task of machine translation: the translation of noun phrases. We demonstrate through analysis and ex- periments that it is feasible and beneficial to treat noun phrase translation as a subtask. This opens the path to dedicated modeling of other types of syn- tactic constructs, e.g., verb clauses, where issues of subcategorization of the verb play a big role. Focusing on a narrower problem allows not only more dedicated modeling, but also the use of com- putationally more expensive methods. We go on to tackle the task of noun phrase trans- lation in a maximum entropy reranking framework. Treating translation as a reranking problem instead of as a search problem enables us to use features over the full translation pair. We integrate both em- pirical and symbolic knowledge sources as features into our system which outperforms the best known methods in statistical machine translation. Previous work on defining subtasks within sta- tistical machine translation has been performed on, e.g., noun-noun pair (Cao and Li, 2002) and named entity translation (Al-Onaizan and Knight, 2002). 2 Noun Phrase Translation as a Subtask In this work, we consider both noun phrases and prepositional phrases, which we will refer to as NP/PPs. We include prepositional phrases for a number of reasons. Both are attached at the clause level. Also, the translation of the preposition of- ten depends heavily on the noun phrase (in the morning). Moreover, the distinction between noun phrases and prepositional phrases is not always clear (note the Japanese bunsetsu) or hard to separate (German joining of preposition and determiner into one lexical unit, e.g., ins � in das � in the). 2.1 Definition We define the NP/PPs in a sentence as follows: Given a sentence � and its syntactic parse tree � , the NP/PPs of the sentence � are the subtrees �� that contain at least one noun and no verb, and are not part of a larger subtree that contains no verb. NP/PP NP/PP S NP VP DT NNP NN the Bush administration VBZ VP has VBN VP decided TO VP to VB NP renounce NP …

Content truncated to 500 words