Translation of New Named Entities from E nglish to C hinese (2020)

Abstract: New things are being created and new words are constantly being added to languages worldwide. However, it is not practical to translate them all manually into a new foreign language. When translating from an alphabetic language such as English to Chinese, appropriate Chinese characters must be assigned, which is particularly costly compared to other language pairs. Therefore, we propose a task of generating and evaluating new translations from English to Chinese focusing on named entities. We defined three criteria for human evaluation—fluency, adequacy of pronunciation, and adequacy of meaning—and constructed evaluation data based on these definitions. In addition, we built a baseline system and analyzed the output of the system.

## introduction a machine translation (mt) system is expected to generate the correct translation results for each input. however, new named entities (nes), such as company names, character names, and product names, are constantly being created worldwide. therefore, such words must be assigned new translations without referring to any translations in other languages. in particular, the translation of nes between different alphabets, for example, from english (en) to chinese (zh) characters, is more difficult than that between other language pairs. it is necessary to select appropriate chinese characters (hanzi) in consideration of appropriate fluency, adequacy of pronunciation (hereinafter referred to as "pronunciation"), and adequacy of meaning (hereinafter referred to as "meaning"). these three dimensions should also be considered in ne translation evaluation. for example, the ne pair of (curtiss-wright, 柯蒂斯-莱特) is evaluated high in terms of pronunciation. however, its fluency in chinese is not good because it is not an original chinese word. although several studies have been conducted on en-zh mt @xcite , ne translation @xcite and transliteration @xcite , no research has been conducted so far on generating and evaluating the translations of brand new nes in terms of fluency, pronunciation, and meaning. the difficulty in considering these three dimensions makes translating a new ne a challenging task. in chinese, there can be several hanzi with similar pronunciations or meanings, and they all can be selected for appropriate ne translation. for instance, the ne pairs in en-zh of (blackstone group, 黑石集团) and (blackstone group, 百仕 通), where the former represents the literal translation and the latter represents transliteration, are both correct, and it is difficult to judge which is preferable. thus, it is first necessary to define the criteria of fluency, pronunciation, and meaning. thus, in this paper, we propose a novel task of generating and evaluating brand new ne translations for en-zh. the main contributions are: ## related work in terms of ne translation @xcite @xcite phonemes, the english graphemes, phonetically associated english letters, must be converted into hanzi, which represent ideas and meanings. in terms of literal translation, because hanzi usually express certain connotations, choosing the appropriate hanzi should also be considered. in addition, owing to the lack of apparent semantic content on location names and people's names, these words cannot be expressed in chinese through words equivalent in meaning. further, it is very likely that the standard translation of these words cannot be found in existing lexical resources, which increases the complexity of the task. a semantic transliteration method @xcite is proposed for the translation of personal names from english to chinese, which considers the language of origin, gender, and the given or surname information of the source names. the approach aims at maintaining the phonetic equivalence as well as optimizing the semantic transfer. however, as highlighted in the paper, the research is a case study, and the proposed mathematical framework does not extend to the machine transliteration of nes. ## dataset we construct our evaluation data based on the company list of the new york stock exchange foot_1 , in which we select the companies that have both english and chinese wikipedia pages as the source and target nes, respectively; the titles of chinese pages can be requested for by using the langlinks api and the english titles. we chose company names because they reflect the corporations' characteristics, providing more information for evaluating fluency, pronunciation, and meaning. because we focus on the english to chinese ne translation, companies from greater china are ignored. in all, 338 en-zh ne pairs were evaluated by two annotators in terms of the three dimensions mentioned above. ## baseline model in the following section, we describe a baseline model for character-based ne translation. furthermore, we propose filters to remove noisy samples from the wikititles dataset and demonstrate that the sanitized data could improve the performance of the model. ## conclusion this paper describes a new method for ne translation from english to chinese. for this purpose, we presented human evaluation criteria for business names and build a test set. further, we found that the correlation between the bleu score and human evaluation scores is weak. the reason is that while the human evaluation scores represent the quality of the ne translation, bleu represents the similarity between the reference nes and the outputs from the model. thus, we conclude that, for evaluating ne translations, reference-less methods should be more effective than reference-based methods.