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首页> 外文会议>Proceedings of the Ninth International Conference on Machine Learning and Cybernetics >Does joint decoding really outperform cascade processing in English-to-Chinese transliteration generation? The role of syllabification
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Does joint decoding really outperform cascade processing in English-to-Chinese transliteration generation? The role of syllabification

机译：在英译汉音译中，联合解码真的能胜过级联处理吗？音节化的作用

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摘要

Transliteration is a challengeable task aimed at converting a proper name into another language with phonetic equivalence. Since the conversion relates to the phonetic aspect of a text, syllabification is considered a major factor affecting the performance of a transliteration system. In grapheme-based approaches, there are two routines to transliterate, one is to perform in a pipeline of separate syllabification and other components in generation process step by step, the other is to synchronously segment syllables and generating transliteration options. Usually, joint decoding outperforms the cascade processing in many natural language processing missions, however, syllabification is a special component in transliteration task. Thus in this paper, we investigate the two routines with a systematic analysis and compare their results to illustrate the strength of syllabification. A phrase-based statistical machine translation framework for joint decoding and a conditional random field syllabification system are used in this work for our investigation, which shows a different scenario on the issue of joint decoding versus cascade processing in transliteration.
机译：音译是一项具有挑战性的任务，旨在将专有名称转换为具有语音等效性的另一种语言。由于转换涉及文本的语音方面，因此音节化被认为是影响音译系统性能的主要因素。在基于音素的方法中，有两种音译程序，一种是在单独的音节化和其他组成部分的流水线中逐步执行，另一种是同步分割音节并生成音译选项。通常，联合解码在许多自然语言处理任务中要胜过级联处理，但是音节化是音译任务中的一个特殊组成部分。因此，在本文中，我们通过系统分析来研究这两个例程，并比较它们的结果以说明音节化的强度。在这项工作中，我们使用了用于联合解码的基于短语的统计机器翻译框架和条件随机字段音节化系统，以研究联合译与音译中的级联处理之间的不同情况。

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