Open Vocabulary Learning for Neural Chinese Pinyin IME
This addresses input inconvenience for Chinese language users by improving conversion accuracy, though it is incremental as it builds on existing neural methods.
The paper tackled the problem of pinyin-to-character conversion in Chinese input methods, which suffers from character ambiguities and fixed vocabulararies, by proposing a neural model with an online updated vocabulary and sampling mechanism; the result showed it outperformed commercial IMEs and state-of-the-art models on standard and real datasets across multiple metrics.
Pinyin-to-character (P2C) conversion is the core component of pinyin-based Chinese input method engine (IME). However, the conversion is seriously compromised by the ambiguities of Chinese characters corresponding to pinyin as well as the predefined fixed vocabularies. To alleviate such inconveniences, we propose a neural P2C conversion model augmented by an online updated vocabulary with a sampling mechanism to support open vocabulary learning during IME working. Our experiments show that the proposed method outperforms commercial IMEs and state-of-the-art traditional models on standard corpus and true inputting history dataset in terms of multiple metrics and thus the online updated vocabulary indeed helps our IME effectively follows user inputting behavior.