Papers by Isak Czeresnia Etinger
Multimodal, Multilingual Grapheme-to-Phoneme Conversion for Low-Resource Languages (D19-61)
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| Challenge: | Grapheme-to-phoneme conversion (g2p) is a task of predicting the pronunciation of words from their orthographic representation. |
| Approach: | They propose to leverage audio data as an auxiliary modality in a multi-task training process to learn a more optimal grapheme representation. |
| Outcome: | The proposed model reduces phoneme error rate to 2.46% on in-domain test set compared to unimodal spelling- pronunciation model. |
Formality Style Transfer for Noisy, User-generated Conversations: Extracting Labeled, Parallel Data from Unlabeled Corpora (D19-55)
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| Challenge: | Typical datasets used for style transfer in NLP contain aligned pairs of two opposite extremes of a style. |
| Approach: | They propose a technique to derive a dataset of aligned pairs from an unlabeled corpus by using an auxiliary dataset, allowing for in-domain training. |
| Outcome: | The proposed method significantly outperforms OpenNMT’s Seq2Seq model trained on the Yahoo Formality Dataset and 6 novel datasets. |