Building an English-Chinese Parallel Corpus Annotated with Sub-sentential Translation Techniques

Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), pages 4024–4033 Marseille, 11–16 May 2020 c⃝ European Language Resources Association (ELRA), licensed under CC-BY-NC 4024 Building an English-Chinese Parallel Corpus Annotated with Sub-sentential Translation Techniques Yuming Zhai1, Lufei Liu2, Xinyi Zhong3, Gabriel Illouz1, Anne Vilnat1 1 Université Paris-Saclay, CNRS, LIMSI, 91400, Orsay, France 2Université Paris Diderot, 75013, Paris, France 3Université Sorbonne Nouvelle - Paris 3, 75005, Paris, France yuming.zhai@limsi.fr, lufei.liu@etu.univ-paris-diderot.fr, xinyi.zhong@sorbonne-nouvelle.fr gabriel.illouz@u-psud.fr, anne.vilnat@limsi.fr Abstract Human translators often resort to different non-literal translation techniques besides the literal translation, such as idiom equivalence, generalization, particularization, semantic modulation, etc., especially when the source and target languages have different and distant origins. Translation techniques constitute an important subject in translation studies, which help researchers to understand and analyse translated texts. However, they receive less attention in developing Natural Language Processing (NLP) applications. To fill this gap, one of our long term objectives is to have a better semantic control of extracting paraphrases from bilingual parallel corpora. Based on this goal, we suggest this hypothesis: it is possible to automatically recognize different sub-sentential translation techniques. For this original task, since there is no dedicated data set for English-Chinese, we manually annotated a parallel corpus of eleven genres. Fifty sentence pairs for each genre have been annotated in order to consolidate our annotation guidelines. Based on this data set, we conducted an experiment to classify between literal and non-literal translations. The preliminary results confirm our hypothesis. The corpus and code are available. We hope that this annotated corpus will be useful for linguistic contrastive studies and for fine-grained evaluation of NLP tasks, such as automatic word alignment and machine translation. Keywords: corpus annotation, translation technique, automatic classification 1. Introduction Translation theorists and linguists have conducted studies on translation techniques for a few decades (Vinay and Dar- belnet, 1958; Newmark, 1981; Larson, 1984; Chuquet and Paillard, 1989; Molina and Hurtado Albir, 2002; Fadaee, 2011; Ðor ¯devi´c, 2017). Translation techniques refer to the specific steps for the sake of accomplishing an acceptable and appropriate translation, which can be divided coarsely into literal translation and non-literal translation at sub- sentential level. Consider two human non-literal translation examples in ta- ble 1: the translation of the first sentence conveys the mean- ing in a more direct way to help readers’ understanding; the translation of the second sentence divides one sentence into two clauses to paraphrase the expression « unfold out of », thus the translation is more natural and compact. EN: Don’t make me go through all of this and not make it. ZH: 别让我的辛苦白费了。 ("Don’t let my hard work be wasted.") EN: In the east the dawn was unfolding out of the darkness. ZH: 东方晨曦初现，黑暗渐去。 ("In the east the dawn was beginning to appear, and the darkness was fading.") Table 1: English-Chinese non-literal translations Non-literal translations between different languages can cause difficulties for automatic word alignment (Dorr et al., 2002; Deng and Xue, 2017), or cause meaning changes in certain cases. However, non-literal translation tech- niques …

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