Cost Optimization in Crowdsourcing Translation: Low cost translations made even cheaper
Cost Optimization in Crowdsourcing Translation: Low cost translations made even cheaper
复制标题
众包翻译的成本优化：低成本翻译变得更便宜
DOI：
10.3115/v1/n15-1072
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Chris Callison
中科院分区：
文献类型：
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作者：
Mingkun Gao;W. Xu;Chris Callison
众包使得以比雇佣专业翻译人员低得多的成本进行翻译成为可能。然而，获得训练统计机器翻译系统所需的数百万个翻译仍然是昂贵的。我们提出了两种机制来降低众包的成本，同时保持高翻译质量。首先，我们开发了一种减少冗余翻译的方法。我们训练了一个线性模型来逐句评估翻译质量，并在可接受和不可接受的翻译之间拟合一个阈值。与过去的工作不同，我们总是为每个源句子支付固定数量的翻译，然后从中选择最好的，当我们收到足够好的翻译时，我们可以更早地停止并支付更少的费用。其次，我们介绍了一种方法，通过快速识别只翻译了几个句子的不良译者来减少翻译人员的数量。这也允许我们对翻译人员进行排名，以便我们只重新雇用优秀的翻译人员以降低成本。
Crowdsourcing makes it possible to create translations at much lower cost than hiring professional translators. However, it is still expensive to obtain the millions of translations that are needed to train statistical machine translation systems. We propose two mechanisms to reduce the cost of crowdsourcing while maintaining high translation quality. First, we develop a method to reduce redundant translations. We train a linear model to evaluate the translation quality on a sentenceby-sentence basis, and fit a threshold between acceptable and unacceptable translations. Unlike past work, which always paid for a fixed number of translations for each source sentence and then chose the best from them, we can stop earlier and pay less when we receive a translation that is good enough. Second, we introduce a method to reduce the pool of translators by quickly identifying bad translators after they have translated only a few sentences. This also allows us to rank translators, so that we re-hire only good translators to reduce cost.