Special Issue on Machine Translation for Low-Resource Languages

Submission Deadline: May 31, 2020

Please click the link to know more about Manuscript Preparation: http://www.ajoai.org/submission

Please download to know all details of the Special Issue

Special Issue Flyer (PDF)
  • Lead Guest Editor
    • Benyamin Ahmadnia
      Department of Computer Science, Tulane University, New Orleans, USA
  • Guest Editor
    Guest Editors play a significant role in a special issue. They maintain the quality of published research and enhance the special issue’s impact. If you would like to be a Guest Editor or recommend a colleague as a Guest Editor of this special issue, please Click here to complete the Guest Editor application.
    • Bonnie J Dorr
      Institute for Human and Machine Cognition (IHMC), Ocala, USA
    • Hossein Sarrafzadeh
      Department of Cybersecurity, St. Bonaventure University, St. Bonaventure, USA
    • Javier Serrano
      Department of Telecommunications and Systems Engineering, Universitat Autonoma de Barcelona, Cerdanyola del Valles, Spain
    • Mahsa Mohaghegh
      School of Engineering, Computer, and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand
    • Mojtaba Sabbagh-Jafari
      Department of Computer Engineering, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran
    • Pariya Razmdideh
      Department of Linguistics and Translation Studies, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran
  • Introduction

    The biggest issue with low-resource languages is the extreme difficulty of obtaining enough resources. Machine Translation (MT) has proven successful for several language pairs. However, each language comes with its own challenges. Low-resource languages have largely been left out of the MT revolution. In low-resource languages there are often very few written texts and of those that exist, they do not have a parallel text in another language. MT has made significant progress in recent years with a shift to statistical and neural models and rapid development of new architectures such as the transformer. However, current models trained on little parallel data tend to produce poor quality translations and without the parallel texts, statistical or neural MT will give subpar results. This challenge is exacerbated in the context of social media, where we need to enable communication for languages with no corresponding parallel corpora or unofficial languages. We are pleased to invite the academic community to respond to this issue on low-resource MT.
    Research topic should be relevant to low-resource MT, including, but not limited to: Unsupervised statistical or neural MT for low-resource language pairs. Semi-supervised statistical or neural MT for low-resource language pairs. Pretraining methods leveraging monolingual data. Multilingual statistical or neural MT for low-resource languages.
    Aims and Scope:
    1. Low-resource Languages
    2. Statistical Machine Translation
    3. Neural Machine Translation
    4. Active Learning
    5. Unsupervised Learning
    6. Semi-supervised Learning
    7. Dual Learning
    8. Round-tripping
    9. Bridge Language
    10. Bootstrapping

  • Guidelines for Submission

    Manuscripts can be submitted until the expiry of the deadline. Submissions must be previously unpublished and may not be under consideration elsewhere.

    Papers should be formatted according to the guidelines for authors (see: http://www.ajoai.org/submission). By submitting your manuscripts to the special issue, you are acknowledging that you accept the rules established for publication of manuscripts, including agreement to pay the Article Processing Charges for the manuscripts. Manuscripts should be submitted electronically through the online manuscript submission system at http://www.sciencepublishinggroup.com/login. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal and will be listed together on the special issue website.

  • Published Papers

    The special issue currently is open for paper submission. Potential authors are humbly requested to submit an electronic copy of their complete manuscript by clicking here.