Placeholder translation systems enable the users to specify how a specific
phrase is translated in the output sentence. The system is trained to output
special placeholder tokens, and the user-specified term is injected into the
output through the context-free replacement of the placeholder token. However,
this approach could result in ungrammatical sentences because it is often the
case that the specified term needs to be inflected according to the context of
the output, which is unknown before the translation. To address this problem,
we propose a novel method of placeholder translation that can inflect specified
terms according to the grammatical construction of the output sentence. We
extend the sequence-to-sequence architecture with a character-level decoder
that takes the lemma of a user-specified term and the words generated from the
word-level decoder to output the correct inflected form of the lemma. We
evaluate our approach with a Japanese-to-English translation task in the
scientific writing domain, and show that our model can incorporate specified
terms in the correct form more successfully than other comparable models.

本文提出了一种能够通过序列到序列的架构，将用户指定的术语以正确的形式融入到最终的翻译结果中，从而解决了现有 Placeholder 翻译系统无法灵活处理语法和语境的问题。

占位符翻译中目标端屈折变形建模

Modeling Target-side Inflection in Placeholder Translation

For Japanese-to-English translation, zero pronouns in Japanese pose a
challenge, since the model needs to infer and produce the corresponding pronoun
in the target side of the English sentence. However, although fully resolving
zero pronouns often needs discourse context, in some cases, the local context
within a sentence gives clues to the inference of the zero pronoun. In this
study, we propose a data augmentation method that provides additional training
signals for the translation model to learn correlations between local context
and zero pronouns. We show that the proposed method significantly improves the
accuracy of zero pronoun translation with machine translation experiments in
the conversational domain.

本研究针对日英翻译中的零代词问题，提出了一种数据扩充方法来增强模型对局部文本和零代词之间的关联，实验证明该方法显著提高了在对话领域中零代词翻译的准确性。