@inproceedings{garrette-2023-fine,
    title = "Fine-tuning m{SLAM} for the {SIGMORPHON} 2022 Shared Task on Grapheme-to-Phoneme Conversion",
    author = "Garrette, Dan",
    editor = {Nicolai, Garrett  and
      Chodroff, Eleanor  and
      Mailhot, Frederic  and
      {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}},
    booktitle = "Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.sigmorphon-1.31",
    doi = "10.18653/v1/2023.sigmorphon-1.31",
    pages = "250--250",
    abstract = "Grapheme-to-phoneme (G2P) conversion is a task that is inherently related to both written and spoken language. Therefore, our submission to the G2P shared task builds off of mSLAM (Bapna et al., 2022), a 600M parameter encoder model pretrained simultaneously on text from 101 languages and speech from 51 languages. For fine-tuning a G2P model, we combined mSLAM{'}s text encoder, which uses characters as its input tokens, with an uninitialized single-layer RNN-T decoder (Graves, 2012) whose vocabulary is the set of all 381 phonemes appearing in the shared task data. We took an explicitly multilingual approach to modeling the G2P tasks, fine-tuning and evaluating a single model that covered all the languages in each task, and adding language codes as prefixes to the input strings as a means of specifying the language of each example. Our models perform well in the shared task{'}s {``}high{''} setting (in which they were trained on 1,000 words from each language), though they do poorly in the {``}low{''} task setting (training on only 100 words from each language). Our models also perform reasonably in the {``}mixed{''} setting (training on 100 words in the target language and 1000 words in a related language), hinting that mSLAM{'}s multilingual pretraining may be enabling useful cross-lingual sharing.",
}

Grapheme-to-phoneme (G2P) conversion is a task that is inherently related to both written and spoken language. Therefore, our submission to the G2P shared task builds off of mSLAM (Bapna et al., 2022), a 600M parameter encoder model pretrained simultaneously on text from 101 languages and speech from 51 languages. For fine-tuning a G2P model, we combined mSLAM{'}s text encoder, which uses characters as its input tokens, with an uninitialized single-layer RNN-T decoder (Graves, 2012) whose vocabulary is the set of all 381 phonemes appearing in the shared task data. We took an explicitly multilingual approach to modeling the G2P tasks, fine-tuning and evaluating a single model that covered all the languages in each task, and adding language codes as prefixes to the input strings as a means of specifying the language of each example. Our models perform well in the shared task{'}s {``}high{''} setting (in which they were trained on 1,000 words from each language), though they do poorly in the {``}low{''} task setting (training on only 100 words from each language). Our models also perform reasonably in the {``}mixed{''} setting (training on 100 words in the target language and 1000 words in a related language), hinting that mSLAM{'}s multilingual pretraining may be enabling useful cross-lingual sharing.
Garrette, Dan
Fine-tuning mSLAM for the SIGMORPHON 2022 Shared Task on Grapheme-to-Phoneme Conversion