Bridging the domain gap in cross-lingual document classification
Table 7: Effect of the temperature of the translation sampling decoder.
['[ITALIC] μ', 'amazon-fr', 'amazon-de', 'mldoc-fr', 'mldoc-de']
Given a translation system, we use the sample decoding strategy to translate the sample. The sample space is the entire vocabulary space. We tune the temperature of μ of the softmax distribution. As discussed in Xie et al. When μ=0, the sampling reduces to the greedy search and produce the best quality samples. When μ=1, the sampling produces diverse outputs but loses some semantic information. The results show that temperature has a significant influence on the final performance. However, because the quality of translation systems for different language pairs are not the same, their best temperature also varies.