Gia H. Ngo

Also published as: Gia H Ngo


Isolating the Effects of Modeling Recursive Structures: A Case Study in Pronunciation Prediction of Chinese Characters
Minh Nguyen | Gia H Ngo | Nancy Chen
Proceedings of the 2019 Workshop on Widening NLP

Finding that explicitly modeling structures leads to better generalization, we consider the task of predicting Cantonese pronunciations of logographs (Chinese characters) using logographs’ recursive structures. This task is a suitable case study for two reasons. First, logographs’ pronunciations depend on structures (i.e. the hierarchies of sub-units in logographs) Second, the quality of logographic structures is consistent since the structures are constructed automatically using a set of rules. Thus, this task is less affected by confounds such as varying quality between annotators. Empirical results show that modeling structures explicitly using treeLSTM outperforms LSTM baseline, reducing prediction error by 6.0% relative.


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Statistical Machine Transliteration Baselines for NEWS 2018
Snigdha Singhania | Minh Nguyen | Gia H. Ngo | Nancy Chen
Proceedings of the Seventh Named Entities Workshop

This paper reports the results of our trans-literation experiments conducted on NEWS 2018 Shared Task dataset. We focus on creating the baseline systems trained using two open-source, statistical transliteration tools, namely Sequitur and Moses. We discuss the pre-processing steps performed on this dataset for both the systems. We also provide a re-ranking system which uses top hypotheses from Sequitur and Moses to create a consolidated list of transliterations. The results obtained from each of these models can be used to present a good starting point for the participating teams.

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Multimodal neural pronunciation modeling for spoken languages with logographic origin
Minh Nguyen | Gia H. Ngo | Nancy Chen
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Graphemes of most languages encode pronunciation, though some are more explicit than others. Languages like Spanish have a straightforward mapping between its graphemes and phonemes, while this mapping is more convoluted for languages like English. Spoken languages such as Cantonese present even more challenges in pronunciation modeling: (1) they do not have a standard written form, (2) the closest graphemic origins are logographic Han characters, of which only a subset of these logographic characters implicitly encodes pronunciation. In this work, we propose a multimodal approach to predict the pronunciation of Cantonese logographic characters, using neural networks with a geometric representation of logographs and pronunciation of cognates in historically related languages. The proposed framework improves performance by 18.1% and 25.0% respective to unimodal and multimodal baselines.