Difference between revisions of "Chiang 2005"
Line 28: | Line 28: | ||
=== The synchronous CFG model === | === The synchronous CFG model === | ||
− | Based on the definition of synchronous CFGs, the basic elements of the model are weighted rewrite rules with aligned pairs of right-handed sides, of the form: | + | Based on the definition of synchronous CFGs, the basic elements of the model are weighted rewrite rules with aligned pairs of right-handed sides, of the form: <math> X \rightarrow \left \langle \gamma , \alpha \right \rangle </math>, where X is a non-terminal, and <math>\gamma</math> and <math>\alpha</math> are strings of terminals and non-terminals. The weight of each rule is determined by a log-linear model: |
− | + | [[File:f2.png]] | |
== Experimental results == | == Experimental results == | ||
== Related papers == | == Related papers == |
Revision as of 20:00, 1 November 2011
Contents
Citation
Chiang, D. 2005. A Hierarchical Phrase-Based Model for Statistical Machine Translation. In Proceedings of the 43rd Annual Meeting of the ACL, pp. 263–270, Ann Arbor. Association for Computational Linguistics.
Online version
Information Sciences Institute, University of Southern California
Summary
This paper presents a statistical phrase-based machine translation model that uses hierarchical phrases (phrases that contain subphrases). The model is formally syntax-based because it uses Synchronous Context-Free Grammars (synchronous CFG) but not linguistically syntax-based because the grammar is learned from a parallel text without using any linguistic annotations or assumptions. Using BLEU as a metric, it is shown to outperform previous state-of-the-art phrase-based systems.
Motivation
The hierarchical model is motivated by the inability of conventional phrase-based models to learn reorderings of phrases (they only learn local reorderings of words). For example, considering the following Mandarin sentence:
Aozhou shi yu Bei Han you bangjiao de shaoshu guojia zhiyi Australia is with North Korea have diplomatic relations that few countries one of (Australia is one of the few countries that have diplomatic relations with North Korea)
the typical output of a conventional phrase-based system would be:
Australia is diplomatic relations with North Korea is one of the few countries
because it is able to do the local reorderings of "diplomatic ... Korea" and "one ... countries" but fails to perform the inversion of the two groups.
To solve this problem, the proposal is to have pairs of hierarchical phrases that consist of both words and subphrases. These pairs are formally defined as productions of a synchronous CFG. Then, in the previous example, the following productions are sufficient to translate the previous sentence correctly:
The synchronous CFG model
Based on the definition of synchronous CFGs, the basic elements of the model are weighted rewrite rules with aligned pairs of right-handed sides, of the form: , where X is a non-terminal, and and are strings of terminals and non-terminals. The weight of each rule is determined by a log-linear model: