@inproceedings{gashaw-shashirekha-2019-language,
    title = "Language Modelling with {NMT} Query Translation for {A}mharic-{A}rabic Cross-Language Information Retrieval",
    author = "Gashaw, Ibrahim  and
      Shashirekha, H.l",
    editor = "Sharma, Dipti Misra  and
      Bhattacharya, Pushpak",
    booktitle = "Proceedings of the 16th International Conference on Natural Language Processing",
    month = dec,
    year = "2019",
    address = "International Institute of Information Technology, Hyderabad, India",
    publisher = "NLP Association of India",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2019.icon-1.7/",
    pages = "56--64",
    abstract = "This paper describes our first experiment on Neural Machine Translation (NMT) based query translation for Amharic-Arabic Cross-Language Information Retrieval (CLIR) task to retrieve relevant documents from Amharic and Arabic text collections in response to a query expressed in the Amharic language. We used a pre-trained NMT model to map a query in the source language into an equivalent query in the target language. The relevant documents are then retrieved using a Language Modeling (LM) based retrieval algorithm. Experiments are conducted on four conventional IR models, namely Uni-gram and Bi-gram LM, Probabilistic model, and Vector Space Model (VSM). The results obtained illustrate that the proposed Uni-gram LM outperforms all other models for both Amharic and Arabic language document collections."
}