# PERT: A New Solution to Pinyin to Character Conversion Task

arXiv: https://arxiv.org/abs/2205.11737 · HTML: https://mlsanity.com/paper/2205.11737/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 37 references that are in the map.
- First posted on arXiv 24 May 2022.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baseline](https://mlsanity.com/paper/1709.05522/) (2017)
- [Fully Non-autoregressive Neural Machine Translation: Tricks of the Trade](https://mlsanity.com/paper/2012.15833/) (2020)
- [Portuguese Named Entity Recognition using BERT-CRF](https://mlsanity.com/paper/1909.10649/) (2019)
- [ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information](https://mlsanity.com/paper/2106.16038/) (2021)
- [THCHS-30 : A Free Chinese Speech Corpus](https://mlsanity.com/paper/1512.01882/) (2015)
- [CLUECorpus2020: A Large-scale Chinese Corpus for Pre-training Language Model](https://mlsanity.com/paper/2003.01355/) (2020)
- [How much pretraining data do language models need to learn syntax?](https://mlsanity.com/paper/2109.03160/) (2021)
- [Massively Multilingual Neural Grapheme-to-Phoneme Conversion](https://mlsanity.com/paper/1708.01464/) (2017)
- [Improving Fluency of Non-Autoregressive Machine Translation](https://mlsanity.com/paper/2004.03227/) (2020)
- [A Smart Sliding Chinese Pinyin Input Method Editor on Touchscreen](https://mlsanity.com/paper/1909.01063/) (2019)

## Built on by (most cited first, 2 of 2)

- [Generative Input: Towards Next-Generation Input Methods Paradigm](https://mlsanity.com/paper/2311.01166/) (2023)
- [Exploring the Usage of Chinese Pinyin in Pretraining](https://mlsanity.com/paper/2310.04960/) (2023)

## Abstract (arXiv)

Pinyin to Character conversion (P2C) task is the key task of Input Method Engine (IME) in commercial input software for Asian languages, such as Chinese, Japanese, Thai language and so on. It's usually treated as sequence labelling task and resolved by language model, i.e. n-gram or RNN. However, the low capacity of the n-gram or RNN limits its performance. This paper introduces a new solution named PERT which stands for bidirectional Pinyin Encoder Representations from Transformers. It achieves significant improvement of performance over baselines. Furthermore, we combine PERT with n-gram under a Markov framework, and improve performance further. Lastly, the external lexicon is incorporated into PERT so as to resolve the OOD issue of IME.