Papers by Gayatri Bhat
Language Modeling for Code-Mixing: The Role of Linguistic Theory based Synthetic Data (P18-1)
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| Challenge: | Code-mixed (CM) language training is a difficult problem because of lack of data and the increased confusability due to the presence of more than one language. |
| Approach: | They propose a computational technique for creating grammatically valid artificial CM data based on the Equivalence Constraint Theory. |
| Outcome: | The proposed method reduces the perplexity of the model and does not reduce the perceptibility of the models. |
A Margin-based Loss with Synthetic Negative Samples for Continuous-output Machine Translation (D19-56)
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| Challenge: | Existing methods for word embeddings generate faster training with fewer learnable parameters. |
| Approach: | They propose a novel margin-based loss that uses only predicted and target embeddings . they argue that the loss is more consistent and interpretable than other margin--based losses . |
| Outcome: | The proposed model is more consistent and interpretable than other margin-based losses. |