In this task, participants will create computational models that map native orthography lemmas ("graphemes") to valid IPA transcriptions of phonemic pronunciation. This task is crucial for speech processing, namely text-to-speech synthesis.
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Final results will be reported here by August 15, 2024. System papers and a summary of the task will appear in the SIGMORPHON 2024 proceedings.
The data is scraped from Wiktionary using WikiPron (Lee et al. 2020). Data is augmented with IPA transcriptions produced by GPT. All data is validated by organizers.
Training and development data found in the data/tsv folder, separated by orthographic system. For each orthographic system, we include subdirectories for individual language sets and a subdirectory for the concatenation of languages. Datasets are UTF-8-encoded tab-separated values files. Each example occupies a single line and consists of a grapheme sequence—a sequence of NFC Unicode codepoints—a tab character, and the corresponding phone sequence, a roughly-phonemic IPA, tokenized using the segments library.
There are three subtasks, which will be scored separately. Participant teams may submit as many systems as they want to as many subtasks as they want.
In all three subtasks, the data is randomly split into training (80%), development (10%), and testing (10%) data.
Participants will be provided three multilingual training sets. Each dataset will be composed of languages utilizing the same orthography (Roman, Cyrillic, Arabic). For evaluation, each training set will be paired with a test set, with each test set composed of samples from the training languages along with an additional unseen language but utilizing of the same orthography. Models in this track will be tasked to evaluate per training-test dataset pairing (i.e. models are trained on one orthography at a time).
Participants will be provided three multilingual training sets using the same script constraints as seen in Task 1. Provided these datasets, participants will be allowed to train over the concatenation of all data (or subset of) for single evaluation on the concatenation of all test datasets in Task 1. That is, the subtask will evaluate model ability to utilize mixed-orthography scripts for downstream evaluation.
This task will function similarly to Task 2 but all unseen languages in the test set will be replaced with languages with orthographies distinct from all other scripts present in the preceding two tasks. For fairness, script systems will be chosen such that they are functionally similar to the training scripts (e.g. the unseen Arabic languages will be replaced with other Abjad).
(N.B. Due to quality concerns, the originally planned Devanagari-based subset was removed from the task. We will release this dataset later for community use.)
The metric used to rank systems is word error rate (WER), the percentage of words for which the hypothesized transcription sequence does not match the gold transcription. This value, in accordance with common practice, is a decimal value multiplied by 100 (e.g.: 13.53). In the medium- and low-frequency tasks, WER is macro-averaged across all ten languages. We provide two Python scripts for evaluation:
evaluate.py computes the WER for one language. evaluate_all.py computes per-language and average WER across multiple languages.
Please submit your results in the two-column (grapheme sequence, tab-character, tokenized phone sequence) TSV format, the same one used for the training and development data. If you use an internal representation other than NFC, you must convert back before submitting.
Please use this email form to submit your results.
- January 15, 2024: Data collection is complete, and data is released to participants RELEASED
- February 15, 2024: Baseline systems released to participants
- May 15, 2024: Test data is available for participants
- May 31, 2024: Final Submissions are due
- June 3, 2024: Results announced to participants
- June 22, 2024: System papers due for review
- July 31, 2024: Reviews back to participants
- August 15, 2024: CR deadline; task paper due from organizers.
For baseline architectures, we are hosting a fork of the City University of New York CompLing lab's Yoyodyne project. Inspired by fairseq, Yoyodyne is a Pytorch-Lightning wrapper specialized for string transduction tasks. It hosts both general NLP architectures (LSTM, Transformer) along with models specialized for word level transduction (EditAction Transducer, Feature-invariant Transformers). Baseline architectures and the scripts for training are:
- Edit-Action Transducer [4,5]:
yoyodyne/examples/baselines/transducer.sh - Feature-invariant Transformer [6]:
yoyodyne/examples/baselines/transformer.sh - LSTM with Attention [1]:
yoyodyne/examples/baselines/attentive_lstm.sh
Model architectures are largely equivalent and are intended to be run on a conventional consumer-level GPU or CPU. Interested participants are welcomed to build off the architectures or utilize other models available in the codebase (see yoyodyne/README.md for further details).
All models may be evaluated using the yoyodyne/examples/baselines/predict.sh script, requiring only changes to the arch flag.
The task is organized by members of the Computational Linguistics Lab at the Graduate Center, City University of New York and the University of British Columbia.
The code is released under the Apache License 2.0. The data is released under the Creative Commons Attribution-ShareAlike 3.0 Unported License inherited from Wiktionary itself.
[1] Bahdanau, D. Cho, K. Bengio, Y. Neural Machine Translation by Jointly Learning to Align and Translate. In Proceedings of International Conference on Learning Representations 2015.
[2] Girrbach, L. 2023. SIGMORPHON 2022 Shared Task on Grapheme-to-Phoneme Conversion Submission Description: Sequence Labelling for G2P. Proceedings of the 20th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology, pages 239–244.
[3] Lee, J. L, Ashby, L. F.E., Garza, M. E., Lee-Sikka, Y., Miller, S., Wong, A., McCarthy, A. D., and Gorman, K. 2020. Massively multilingual pronunciation mining with WikiPron. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 4223-4228.
[4] Makarov, P., and Clematide, S. 2018. Imitation learning for neural morphological string transduction. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2877-2882.
[5] Makarov, P., and Clematide, S. 2020. CLUZH at SIGMORPHON 2020 shared task on multilingual grapheme-to-phoneme conversion. In Proceedings of the 17th SIGMORPHON Workshopon Computational Research in Phonetics, Phonology, and Morphology, pages 171-176.
[6] Wu, S. Cotterell, R. Hulden, M. Applying the Transformer to Character-level Transduction](https://aclanthology.org/2021.eacl-main.163) In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 1901–1907.