Grapheme-to-phoneme conversion (G2P) is a critical component of the text-to-speech system (TTS), where polyphone disambiguation is the most crucial task. However, polyphone disambiguation datasets often suffer from the long-tail problem, and context learning for polyphonic characters commonly stems from a single dimension. In this paper, we propose a novel model DLM: a Decoupled Learning Model for long-tailed polyphone disambiguation in Mandarin. Firstly, DLM decouples representation and classification learnings. It can apply different data samplers for each stage to obtain an optimal training data distribution. This can mitigate the long-tail problem. Secondly, two improved attention mechanisms and a gradual conversion strategy are integrated into the DLM, which achieve transition learning of context from local to global. Finally, to evaluate the effectiveness of DLM, we construct a balanced polyphone disambiguation corpus via in-context learning. Experiments on the benchmark CPP dataset demonstrate that DLM achieves a boosted accuracy of 99.07%. Moreover, DLM improves the disambiguation performance of long-tailed polyphonic characters. For many long-tailed characters, DLM even achieves an accuracy of 100%.

“针对当前大多数中文语法纠错模型存在错误边界识别不准确以及过度纠正的问题,我们提出了一种基于序列标注与指针生成网络的中文语法纠错模型。首先,在数据方面,我们使用了官方提供的lang8数据集和历年的CGED数据集,并对该数据集进行了繁体转简体、数据清洗等操作。其次,在模型方面,我们采用了ERNIE+Global Pointer的序列标注模型、基于ERNIE+CRF的序列标注模型、基于BART+指针生成网络的纠错模型以及基于CECToR的纠错模型。最后,在模型集成方面,我们使用了投票和基于ERNIE模型计算困惑度的方法,来生成最终预测结果。根据测试集的结果,我们的乃乏乍指标达到了48.68,位居第二名。”