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Who, What, When, Where, Why? Comparing Multiple Approaches to the Cross-Lingual 5W Task

机译:谁,什么,何时,何地,为什么?比较跨语言的5W任务的多种方法

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摘要

Cross-lingual tasks are especially difficult due to the compounding effect of errors in language processing and errors in machine translation (MT). In this paper, we present an error analysis of a new cross-lingual task: the 5W task, a sentence-level understanding task which seeks to return the English 5W's (Who, What, When, Where and Why) corresponding to a Chinese sentence. We analyze systems that we developed, identifying specific problems in language processing and MT that cause errors. The best cross-lingual 5W system was still 19% worse than the best monolingual 5W system, which shows that MT significantly degrades sentence-level understanding. Neither source-language nor target-language analysis was able to circumvent problems in MT, although each approach had advantages relative to the other. A detailed error analysis across multiple systems suggests directions for future research on the problem.
机译:由于语言处理中的错误和机器翻译(MT)中的错误的复合作用,跨语言任务特别困难。在本文中,我们提出了一项新的跨语言任务的错误分析:5W任务,这是一个句子级的理解任务,旨在返回与中文句子相对应的英语5W(“谁,什么,什么时候,什么地方,为什么”) 。我们分析开发的系统,确定在语言处理和MT中导致错误的特定问题。最佳的跨语言5W系统仍比最佳单语言5W系统差19%,这表明MT大大降低了句子层次的理解。源语言分析和目标语言分析都无法规避MT中的问题,尽管每种方法相对于其他方法都具有优势。跨多个系统的详细错误分析为该问题的未来研究提供了方向。

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