@inproceedings{lohar-etal-2022-building,
    title = "Building Machine Translation System for Software Product Descriptions Using Domain-specific Sub-corpora Extraction",
    author = "Lohar, Pintu  and
      Madden, Sinead  and
      O{'}Connor, Edmond  and
      Popovic, Maja  and
      Habruseva, Tanya",
    editor = "Duh, Kevin  and
      Guzm{\'a}n, Francisco",
    booktitle = "Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)",
    month = sep,
    year = "2022",
    address = "Orlando, USA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://aclanthology.org/2022.amta-research.1",
    pages = "1--13",
    abstract = "Building Machine Translation systems for a specific domain requires a sufficiently large and good quality parallel corpus in that domain. However, this is a bit challenging task due to the lack of parallel data in many domains such as economics, science and technology, sports etc. In this work, we build English-to-French translation systems for software product descriptions scraped from LinkedIn website. Moreover, we developed a first-ever test parallel data set of product descriptions. We conduct experiments by building a baseline translation system trained on general domain and then domain-adapted systems using sentence-embedding based corpus filtering and domain-specific sub-corpora extraction. All the systems are tested on our newly developed data set mentioned earlier. Our experimental evaluation reveals that the domain-adapted model based on our proposed approaches outperforms the baseline.",
}