针对长尾问题的二重加权多音字消歧算法

数据的长尾分布问题是NLP实践领域中的常见问题。以语音合成前端的多音字消歧任务为例，多音字数据的极度不均衡、尾部数据的缺乏，影响着语音合成系统的工业实用效果。该文观察到，汉语多音字的分布在“字符”与“字音”两个维度上都呈长尾特性，因此该文针对性地提出一种二重加权算法(Double Weighted, DW)。DW算法可分别与两种长尾算法：MARC,Decouple-cRT结合，进一步提升模型性能。在开源数据和工业数据上，DW算法较基线模型和两种原始算法取得了不同程度的准确率提升，为多维长尾问题提供解决方案与借鉴思路。

Double-Weighted Disambiguation Algorithm for Long-tail Polyphone Problem

The problem of long-tail distributed data is common in NLP practice. Taking the polyphone disambiguation task in text-to-speech (TTS) as an example, the extreme data imbalance and the lack of tail data affect industrial online TTS applications. Observging that the Chinese polyphone is long-tail distributed on both “character” and “pronunciation” dimensions, this paper proposes a double-weighted (DW) algorithm, which can be combined with the other two long-tail algorithms: MARC and Decouple-cRT. Given the perspectives of both open-source data and industrial data, DW demonstrates improvement in accuracy compared to the baseline model and the two original algorithms.