论文标题
Matra：印度脚本的多语言专注音译系统
MATra: A Multilingual Attentive Transliteration System for Indian Scripts
论文作者
论文摘要
音译是NLP域中的一项任务，其中输出单词是使用任何外语字母编写的类似单词。如今，该系统已针对多种语言对开发，涉及英语作为源或目标单词，并在Google Translate和聊天机器人等多个地方部署。但是，在译为其他指示语言的指示语言领域的研究很少。本文展示了一个基于变压器（具有一些修改）的多语言模型，该模型比该领域中的所有现有模型都可以显着更高的性能和准确性，并且比最先进的模型获得了更好的结果。本文显示了一个模型，该模型可以在以下五种语言之间进行任何一对的音译 - 英语，印地语，孟加拉语，卡纳达语和泰米尔语。它适用于语言在任何书面任务中的通信障碍的情况。该模型击败了最先进的（对于上述五种语言中的所有对 - 英语，印地语，孟加拉语，卡纳达语和泰米尔语），并获得了80.7％的前1位准确性得分，比最佳当前结果高约29.5％。此外，该模型在语音准确性方面达到了93.5％（音译主要是基于语音/声音的任务）。
Transliteration is a task in the domain of NLP where the output word is a similar-sounding word written using the letters of any foreign language. Today this system has been developed for several language pairs that involve English as either the source or target word and deployed in several places like Google Translate and chatbots. However, there is very little research done in the field of Indic languages transliterated to other Indic languages. This paper demonstrates a multilingual model based on transformers (with some modifications) that can give noticeably higher performance and accuracy than all existing models in this domain and get much better results than state-of-the-art models. This paper shows a model that can perform transliteration between any pair among the following five languages - English, Hindi, Bengali, Kannada and Tamil. It is applicable in scenarios where language is a barrier to communication in any written task. The model beats the state-of-the-art (for all pairs among the five mentioned languages - English, Hindi, Bengali, Kannada, and Tamil) and achieves a top-1 accuracy score of 80.7%, about 29.5% higher than the best current results. Furthermore, the model achieves 93.5% in terms of Phonetic Accuracy (transliteration is primarily a phonetic/sound-based task).
