{'fraction_non_alphanumeric': 0.039984319874559, 'fraction_numerical': 0.018816150529204233, 'mean_word_length': 4.880184331797235, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A joint inference of deep case analysis and zero subject generation for Japanese-to-English statistical machine translation

Japanese to English translation is known to be one of the most difficult language pair for statistical machine translation (SMT). It has been widely believed for years that the difference of word orders, i.e., Japanese is an SOV language, while English is an SVO language, makes the English-to-Japanese and Japanese-to-English translation difficult. However, simple, yet powerful pre-ordering techniques have made this argument a thing of the past (Isozaki et al., 2010b; Komachi et al., 2006; Fei and Michael, 2004; Lerner and Petrov, 2013; Wu et al., 2011; Katz-Brown and Collins, 2008; Neubig et al., 2012; Hoshino et al., 2013). While many successes of English-to-Japanese translation have been reported recently, the quality improvement of Japanese-to-English translation is still small even with the help of pre-ordering (Goto et al., 2013). We found that there are two major issues that make Japanese-to-English translation difficult. One is that Japanese subject and object cannot easily be identified compared to English, while their detections are the key process to generate correct English word orders. Japanese surface syntactic structures are not always corresponding to their deep structures, i.e., semantic roles. The other is that Japanese is a pro-drop language in which certain classes of pronouns may be omitted when they are pragmatically inferable. In Japanese-to-English translation, these omitted pronouns have to be generated properly. There are several researches that focused on the pre-ordering with Japanese deep syntactic analysis (Komachi et al., 2006; Hoshino et al., 2013) and zero pronoun generation (Taira et al., 2012) for Japanese-to-English translation. However, these two issues have been considered independently, while they heavily rely on one another. In this paper, we propose a simple joint inference which handles both Japanese deep structure analysis and zero pronoun generation. To the best of our knowledge, this is the first study that addresses these two issues at the same time. This paper is organized as follows. First, we describe why Japanese-to-English translation is difficult. Second, we show the basic idea of this work and its implementation based on pointwise probabilistic models and a global inference with an integer linear programming (ILP). Several experiments are employed to confirm that our new model can improve the Japanese to English translation quality.