Frustratingly Easy Multilingual Grapheme-to-Phoneme Conversion (2020)

Abstract: In this paper, we describe two CU-Boulder submissions to the SIGMORPHON 2020 Task 1 on multilingual grapheme-to-phoneme conversion (G2P). Inspired by the high performance of a standard transformer model (Vaswani et al., 2017) on the task, we improve over this approach by adding two modifications: (i) Instead of training exclusively on G2P, we additionally create examples for the opposite direction, phoneme-to-grapheme conversion (P2G). We then perform multi-task training on both tasks. (ii) We produce ensembles of our models via majority voting. Our approaches, though being conceptually simple, result in systems that place 6th and 8th amongst 23 submitted systems, and obtain the best results out of all systems on Lithuanian and Modern Greek, respectively.

## introduction this paper describes the cu boulder submissions to the sigmorphon 2020 shared task on multilingual grapheme-to-phoneme conversion (g2p). g2p is an important task, due to its applications in text-to-speech and automatic speech recognition systems. it is explained by @xcite as: the process of converting a sequence of letters into a sequence of phones is called grapheme-to-phoneme conversion, sometimes shortened g2p. the job of a grapheme-to-phoneme algorithm is thus to convert a letter string like cake into a phone string like @xcite . while the earliest g2p algorithms have used handwritten parser-based rules in the format of chomsky-halle rewrite rules, often called letter-tosound, or lts, rules @xcite , later techniques have moved on to generating semiautomatic alignment tables such as in @xcite . today, a lot of work aims at using machine learning -in particular deep learning techniquesto solve sequence-to-sequence problems like this. we explore using a transformer model @xcite for this problem, since it has shown great promise in several areas of natural language processing (nlp), outperforming the previous state of the art on a large variety of tasks, including machine translation @xcite , summarization @xcite , question-answering @xcite , and sentiment-analysis @xcite . while previous work has used transformers for g2p, experiments have only been performed on english, specifically on the cmudict @xcite and nettalk foot_0 datasets @xcite . our approach builds upon the standard architecture by adding two straightforward modifications: multi-task training @xcite and ensembling. we find that these simple additions lead to performance improvements over the standard model, and our models place 6th and 8th among 23 submissions to the sigmorphon 2020 shared task on multilingual grapheme-to-phoneme conversion. our two models further perform the best on the languages lithuanian and modern greek, respectively. ## proposed approach we submit two different systems to the shared task, which are based on the transformer architecture, multi-task learning, and ensembling. we describe all components individually in this section. ## conclusion we described two cu boulder submissions to sig-morphon 2020 task 1. our systems consisted of transformer models, some of which were trained in a multi-task fashion on g2p and p2g. we further created ensembles consisting of multiple individual models via majority voting. our internal experiments and the official results showed that these two straightforward extensions of the transformer model enabled our systems to improve over the official shared task baselines and a standard transformer model for g2p. our final models, cu-1 and cu-2, placed 6th and 8th out of 23 submissions, and obtained the best results of all systems for lithuanian and modern greek, respectively.