James Hadley


2020

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Multiple Segmentations of Thai Sentences for Neural Machine Translation
Alberto Poncelas | Wichaya Pidchamook | Chao-Hong Liu | James Hadley | Andy Way
Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)

Thai is a low-resource language, so it is often the case that data is not available in sufficient quantities to train an Neural Machine Translation (NMT) model which perform to a high level of quality. In addition, the Thai script does not use white spaces to delimit the boundaries between words, which adds more complexity when building sequence to sequence models. In this work, we explore how to augment a set of English–Thai parallel data by replicating sentence-pairs with different word segmentation methods on Thai, as training data for NMT model training. Using different merge operations of Byte Pair Encoding, different segmentations of Thai sentences can be obtained. The experiments show that combining these datasets, performance is improved for NMT models trained with a dataset that has been split using a supervised splitting tool.

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The Impact of Indirect Machine Translation on Sentiment Classification
Alberto Poncelas | Pintu Lohar | James Hadley | Andy Way
Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)

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A Tool for Facilitating OCR Postediting in Historical Documents
Alberto Poncelas | Mohammad Aboomar | Jan Buts | James Hadley | Andy Way
Proceedings of LT4HALA 2020 - 1st Workshop on Language Technologies for Historical and Ancient Languages

Optical character recognition (OCR) for historical documents is a complex procedure subject to a unique set of material issues, including inconsistencies in typefaces and low quality scanning. Consequently, even the most sophisticated OCR engines produce errors. This paper reports on a tool built for postediting the output of Tesseract, more specifically for correcting common errors in digitized historical documents. The proposed tool suggests alternatives for word forms not found in a specified vocabulary. The assumed error is replaced by a presumably correct alternative in the post-edition based on the scores of a Language Model (LM). The tool is tested on a chapter of the book An Essay Towards Regulating the Trade and Employing the Poor of this Kingdom (Cary, 1719). As demonstrated below, the tool is successful in correcting a number of common errors. If sometimes unreliable, it is also transparent and subject to human intervention.

2019

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Proceedings of the Qualities of Literary Machine Translation
James Hadley | Maja Popović | Haithem Afli | Andy Way
Proceedings of the Qualities of Literary Machine Translation