• Combining quality estimation and automatic post-editing to enhance machine translation output

      Chatterjee, Rajen; Negri, Matteo; Turchi, Marco; Blain, Frédéric; Specia, Lucia (Association for Machine Translation in the America, 2018-03)
    • Guiding neural machine translation decoding with external knowledge

      Chatterjee, Rajen; Negri, Matteo; Turchi, Marco; Federico, Marcello; Specia, Lucia; Blain, Frédéric (Association for Computational Linguistics, 2017-09)
      Chatterjee, R., Negri, M., Turchi, M., Federico, M. et al. (2017) Guiding neural machine translation decoding with external knowledge. In, Proceedings of the Second Conference on Machine Translation, Volume 1: Research Papers, Bojar, O., Buck, C., Chatterjee, R., Federmann, C. et al. (eds.) Stroudsburg, PA: Association for Computational Linguistics, pp. 157-168.
    • The Matecat Tool

      Federico, Marcello; Bertoldi, Nicola; Cettolo, Mauro; Negri, Matteo; Turchi, Marco; Trombetti, Marco; Cattelan, Alessandro; Farina, Antonio; Lupinetti, Domenico; Marines, Andrea; et al. (Dublin City University and Association for Computational Linguistics, 2014-08-31)
      We present a new web-based CAT tool providing translators with a professional work environment, integrating translation memories, terminology bases, concordancers, and machine translation. The tool is completely developed as open source software and has been already successfully deployed for business, research and education. The MateCat Tool represents today probably the best available open source platform for investigating, integrating, and evaluating under realistic conditions the impact of new machine translation technology on human post-editing.
    • The first Automatic Translation Memory Cleaning Shared Task

      Barbu, Eduard; Parra Escartín, Carla; Bentivogli, Luisa; Negri, Matteo; Turchi, Marco; Orasan, Constantin; Federico, Marcello (Springer, 2017-01-21)
      This paper reports on the organization and results of the rst Automatic Translation Memory Cleaning Shared Task. This shared task is aimed at nding automatic ways of cleaning translation memories (TMs) that have not been properly curated and thus include incorrect translations. As a follow up of the shared task, we also conducted two surveys, one targeting the teams participating in the shared task, and the other one targeting professional translators. While the researchers-oriented survey aimed at gathering information about the opinion of participants on the shared task, the translators-oriented survey aimed to better understand what constitutes a good TM unit and inform decisions that will be taken in future editions of the task. In this paper, we report on the process of data preparation and the evaluation of the automatic systems submitted, as well as on the results of the collected surveys.
    • Translation quality and productivity: a study on rich morphology languages

      Specia, Lucia; Harris, Kim; Burchardt, Aljoscha; Turchi, Marco; Negri, Matteo; Skadina, Inguna (Asia-Pacific Association for Machine Translation, 2017)
      Specia, L., Blain, F., Harris, K., Burchardt, A. et al. (2017) Translation quality and productivity: a study on rich morphology languages. In, Machine Translation Summit XVI, Vol 1. MT Research Track, Kurohashi, S., and Fung, P., Nagoya, Aichi, Japan: Asia-Pacific Association for Machine Translation, pp. 55-71.