Syntax-based Machine Translation System Research Articles

統語ベース翻訳のための構文解析器の自己学習

構文情報を考慮する機械翻訳手法である統語ベース翻訳では，構文解析器の精度が翻訳精度に大きな影響を与えることが知られている．また，構文解析の精度向上を図る手法の一つとして，構文解析器の出力を学習データとして用いる構文解析器の自己学習が提案されている．しかし，構文解析器が生成する構文木には誤りが存在することから，自動生成された構文木が常に精度向上に寄与するわけではない．そこで本論文では，機械翻訳における自動評価尺度を用いて，このような誤った構文木を学習データから取り除き，自己学習の効果を向上させる手法を提案する．具体的には，解析された n-best 構文木それぞれを用いて統語ベース翻訳を行い，それぞれの翻訳結果に対し，自動評価尺度でリスコアリングする．この中で，良いスコアを持つ構文木を自己学習に使用することで，構文構造はアノテーションされていないが，対訳が存在するデータを用いて，構文解析・機械翻訳の精度を向上させることができる．実験により，本手法で自己学習したモデルを用いることで，統語ベース翻訳システムの翻訳精度が2つの言語対で有意に向上し，また構文解析自体の精度も有意に向上することが確認できた．

Language Modeling for Syntax-Based Machine Translation Using Tree Substitution Grammars

The poor grammatical output of Machine Translation (MT) systems appeals syntax-based approaches within language modeling. However, previous studies showed that syntax-based language modeling using (Context-Free) Treebank Grammars was not very helpful in improving BLEU scores for Chinese-English machine translation. In this article we further study this issue in the context of Chinese-English syntax-based Statistical Machine Translation (SMT) where Synchronous Tree Substitution Grammars (STSGs) are utilized to model the translation process. In particular, we develop a Tree Substitution Grammar-based language model for syntax-based MT, and present three methods to efficiently integrate the proposed language model into MT decoding. In addition, we design a simple and effective method to adapt syntax-based language models for MT tasks. We demonstrate that the proposed methods are able to benefit a state-of-the-art syntax-based MT system. On the NIST Chinese-English MT evaluation corpora, we finally achieve an improvement of 0.6 BLEU points over the baseline.

Binarization of Synchronous Context-Free Grammars

Systems based on synchronous grammars and tree transducers promise to improve the quality of statistical machine translation output, but are often very computationally intensive. The complexity is exponential in the size of individual grammar rules due to arbitrary re-orderings between the two languages. We develop a theory of binarization for synchronous context-free grammars and present a linear-time algorithm for binarizing synchronous rules when possible. In our large-scale experiments, we found that almost all rules are binarizable and the resulting binarized rule set significantly improves the speed and accuracy of a state-of-the-art syntax-based machine translation system. We also discuss the more general, and computationally more difficult, problem of finding good parsing strategies for non-binarizable rules, and present an approximate polynomial-time algorithm for this problem.

Fujitsu machine translation system: ATLAS

Due to the rapid advancement of both computer technology and linguistic theory, machine translation systems are now coming into practical use. Fujitsu has two machine translation systems, ATLAS-I is a syntax-based machine translation system which translates English into Japanese. ATLAS II is a semantic-based system which aims at high quality multilingual translation. In this paper, both the ATLAS-I and ATLAS II translation mechanisms are explained.