@inproceedings{he-etal-2017-hccl,
    title = "{HCCL} at {S}em{E}val-2017 Task 2: Combining Multilingual Word Embeddings and Transliteration Model for Semantic Similarity",
    author = "He, Junqing  and
      Wu, Long  and
      Zhao, Xuemin  and
      Yan, Yonghong",
    editor = "Bethard, Steven  and
      Carpuat, Marine  and
      Apidianaki, Marianna  and
      Mohammad, Saif M.  and
      Cer, Daniel  and
      Jurgens, David",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/S17-2033/",
    doi = "10.18653/v1/S17-2033",
    pages = "220--225",
    abstract = "In this paper, we introduce an approach to combining word embeddings and machine translation for multilingual semantic word similarity, the task2 of SemEval-2017. Thanks to the unsupervised transliteration model, our cross-lingual word embeddings encounter decreased sums of OOVs. Our results are produced using only monolingual Wikipedia corpora and a limited amount of sentence-aligned data. Although relatively little resources are utilized, our system ranked 3rd in the monolingual subtask and can be the 6th in the cross-lingual subtask."
}