Using same-language machine translation to create alternative target sequences for text-to-speech synthesis

Cahill, Peter; Du, Jinhua; Way, Andy; Carson-Berndsen, Julie

doi:10.21437/interspeech.2009-408

Cited by 4 publications

References 13 publications

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Investigating Query Expansion and Coreference Resolution in Question Answering on BERT

Bhattacharjee

Haque

Wenniger

et al. 2020

Lecture Notes in Computer Science

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The Bidirectional Encoder Representations from Transformers (BERT) model produces state-of-the-art results in many question answering (QA) datasets, including the Stanford Question Answering Dataset (SQuAD). This paper presents a query expansion (QE) method that identifies good terms from input questions, extracts synonyms for the good terms using a widely-used language resource, WordNet, and selects the most relevant synonyms from the list of extracted synonyms. The paper also introduces a novel QE method that produces many alternative sequences for a given input question using same-language machine translation (MT). Furthermore, we use a coreference resolution (CR) technique to identify anaphors or cataphors in paragraphs and substitute them with the original referents. We found that the QA system with this simple CR technique significantly outperforms the BERT baseline in a QA task. We also found that our best-performing QA system is the one that applies these three preprocessing methods (two QE and CR methods) together to BERT, which produces an excellent F1 score (89.8 F1 points) in a QA task. Further, we present a comparative analysis on the performances of the BERT QA models taking a variety of criteria into account, and demonstrate our findings in the answer span prediction task.

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Investigating Query Expansion and Coreference Resolution in Question Answering on BERT

Bhattacharjee

Haque

Wenniger

et al. 2020

Lecture Notes in Computer Science

Self Cite

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The ADAPT System Description for the STAPLE 2020 English-to-Portuguese Translation Task

Haque¹,

Yasmin²,

Way³

2020

Proceedings of the Fourth Workshop on Neural Generation and Translation

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This paper describes the ADAPT Centre's submission to STAPLE (Simultaneous Translation and Paraphrase for Language Education) 2020, a shared task of the 4th Workshop on Neural Generation and Translation (WNGT), for the English-to-Portuguese translation task. In this shared task, the participants were asked to produce high-coverage sets of plausible translations given English prompts (input source sentences). We present our English-to-Portuguese machine translation (MT) models that were built applying various strategies, e.g. data and sentence selection, monolingual MT for generating alternative translations, and combining multiple nbest translations. Our experiments show that adding the aforementioned techniques to the baseline yields an excellent performance in the English-to-Portuguese translation task.

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Improving speech synthesis of machine translation output

2010

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Using same-language machine translation to create alternative target sequences for text-to-speech synthesis

Cited by 4 publications

References 13 publications

Investigating Query Expansion and Coreference Resolution in Question Answering on BERT

Investigating Query Expansion and Coreference Resolution in Question Answering on BERT

The ADAPT System Description for the STAPLE 2020 English-to-Portuguese Translation Task

Improving speech synthesis of machine translation output

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