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Description
  • We describe the %22Shared Task on Applying Machine Learning Techniques to Optimise the Division of Labour in Hybrid Machine Translation%22 (ML4HMT) which aims to foster research on improved system combination approaches for machine translation (MT). Participants of the challenge are requested to build hybrid translations by combining the output of several MT systems of different types. We first describe the ML4HMT corpus used in the shared task, then explain the XLIFF-based annotation format we have designed for it, and briefly summarize the participating systems. Using both automated metrics scores and extensive manual evaluation, we discuss the individual performance of the various systems. An interesting result from the shared task is the fact that we were able to observe different systems winning according to the automated metrics scores when compared to the results from the manual evaluation. We conclude by summarising the first edition of the challenge and by giving an outlook to future work.
  • We describe the %22Shared Task on Applying Machine Learning Techniques to Optimise the Division of Labour in Hybrid Machine Translation%22 (ML4HMT) which aims to foster research on improved system combination approaches for machine translation (MT). Participants of the challenge are requested to build hybrid translations by combining the output of several MT systems of different types. We first describe the ML4HMT corpus used in the shared task, then explain the XLIFF-based annotation format we have designed for it, and briefly summarize the participating systems. Using both automated metrics scores and extensive manual evaluation, we discuss the individual performance of the various systems. An interesting result from the shared task is the fact that we were able to observe different systems winning according to the automated metrics scores when compared to the results from the manual evaluation. We conclude by summarising the first edition of the challenge and by giving an outlook to future work. (en)
Title
  • The ML4HMT Workshop on Optimising the Division of Labour in Hybrid Machine Translation
  • The ML4HMT Workshop on Optimising the Division of Labour in Hybrid Machine Translation (en)
skos:prefLabel
  • The ML4HMT Workshop on Optimising the Division of Labour in Hybrid Machine Translation
  • The ML4HMT Workshop on Optimising the Division of Labour in Hybrid Machine Translation (en)
skos:notation
  • RIV/00216208:11320/12:10130040!RIV13-GA0-11320___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GBP103/12/G084)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 150814
http://linked.open...ai/riv/idVysledku
  • RIV/00216208:11320/12:10130040
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • translation; machine; hybrid; labour; division; optimising; workshop; ml4hmt (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [F3A0BA2988D2]
http://linked.open...v/mistoKonaniAkce
  • İstanbul, Turkey
http://linked.open...i/riv/mistoVydani
  • İstanbul, Turkey
http://linked.open...i/riv/nazevZdroje
  • Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012)
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Pecina, Pavel
  • Avramidis, Eleftherios
  • Costa-Jussa, Marta R.
  • Federmann, Christian
  • van Genabith, Josef
  • Melero, Maite
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • European Language Resources Association
https://schema.org/isbn
  • 978-2-9517408-7-7
http://localhost/t...ganizacniJednotka
  • 11320
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