Proceedings of the First Workshop on Curation and Applications of Parallel and Comparable Corpora

Haithem Afli, Chao-Hong Liu (Editors)


Anthology ID:
W17-56
Month:
November
Year:
2017
Address:
Taipei, Taiwan
Venue:
WS
SIG:
Publisher:
Asian Federation of Natural Language Processing
URL:
https://www.aclweb.org/anthology/W17-56
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-56.pdf

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Proceedings of the First Workshop on Curation and Applications of Parallel and Comparable Corpora
Haithem Afli | Chao-Hong Liu

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Building a Better Bitext for Structurally Different Languages through Self-training
Jungyeul Park | Loïc Dugast | Jeen-Pyo Hong | Chang-Uk Shin | Jeong-Won Cha

We propose a novel method to bootstrap the construction of parallel corpora for new pairs of structurally different languages. We do so by combining the use of a pivot language and self-training. A pivot language enables the use of existing translation models to bootstrap the alignment and a self-training procedure enables to achieve better alignment, both at the document and sentence level. We also propose several evaluation methods for the resulting alignment.

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MultiNews: A Web collection of an Aligned Multimodal and Multilingual Corpus
Haithem Afli | Pintu Lohar | Andy Way

Integrating Natural Language Processing (NLP) and computer vision is a promising effort. However, the applicability of these methods directly depends on the availability of a specific multimodal data that includes images and texts. In this paper, we present a collection of a Multimodal corpus of comparable texts and their images in 9 languages from the web news articles of Euronews website. This corpus has found widespread use in the NLP community in Multilingual and multimodal tasks. Here, we focus on its acquisition of the images and text data and their multilingual alignment.

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Learning Phrase Embeddings from Paraphrases with GRUs
Zhihao Zhou | Lifu Huang | Heng Ji

Learning phrase representations has been widely explored in many Natural Language Processing tasks (e.g., Sentiment Analysis, Machine Translation) and has shown promising improvements. Previous studies either learn non-compositional phrase representations with general word embedding learning techniques or learn compositional phrase representations based on syntactic structures, which either require huge amounts of human annotations or cannot be easily generalized to all phrases. In this work, we propose to take advantage of large-scaled paraphrase database and present a pairwise-GRU framework to generate compositional phrase representations. Our framework can be re-used to generate representations for any phrases. Experimental results show that our framework achieves state-of-the-art results on several phrase similarity tasks.