Computing multiple weighted reordering hypotheses for a statistical machine translation phrase-based systems
نویسندگان
چکیده
Reordering is one source of error in statistical machine translation (SMT). This paper extends the study of the statistical machine reordering (SMR) approach, which uses the powerful techniques of the SMT systems to solve reordering problems. Here, the novelties yield in: (1) using the SMR approach in a SMT phrase-based system, (2) adding a feature function in the SMR step, and (3) analyzing the reordering hypotheses at several stages. Coherent improvements are reported in the TC-STAR task (Es/En) at a relatively low computational cost.
منابع مشابه
Discriminative Phrase-based Lexicalized Reordering Models using Weighted Reordering Graphs
Lexicalized reordering models play a central role in phrase-based statistical machine translation systems. Starting from the distance-based reordering model, improvements have been made by considering adjacent words in word-based models, adjacent phrases pairs in phrasebased models, and finally, all phrases pairs in a sentence pair in the reordering graphs. However, reordering graphs treat all ...
متن کاملReordering Modeling using Weighted Alignment Matrices
In most statistical machine translation systems, the phrase/rule extraction algorithm uses alignments in the 1-best form, which might contain spurious alignment points. The usage of weighted alignment matrices that encode all possible alignments has been shown to generate better phrase tables for phrase-based systems. We propose two algorithms to generate the well known MSD reordering model usi...
متن کاملMultiple Reorderings in Phrase-Based Machine Translation
This paper presents a method to integrate multiple reordering strategies in phrase-based statistical machine translation. Recently there has been much research effort in reordering problems in machine translation. State-of-the-art decoders incorporate sophisticated local reordering strategies, but there is little research on a unified approach to incorporate various kinds of reordering methods....
متن کاملImproving Reordering for Statistical Machine Translation with Smoothed Priors and Syntactic Features
In this paper we propose several novel approaches to improve phrase reordering for statistical machine translation in the framework of maximum-entropy-based modeling. A smoothed prior probability is introduced to take into account the distortion effect in the priors. In addition to that we propose multiple novel distortion features based on syntactic parsing. A new metric is also introduced to ...
متن کاملA Lexicalized Reordering Model for Hierarchical Phrase-based Translation
Lexicalized reordering model plays a central role in phrase-based statistical machine translation systems. The reordering model specifies the orientation for each phrase and calculates its probability conditioned on the phrase. In this paper, we describe the necessity and the challenge of introducing such a reordering model for hierarchical phrase-based translation. To deal with the challenge, ...
متن کاملذخیره 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 2008