Publications
Authors:
  • Marc Dymetman , Eric Gaussier , Cyril Goutte , Nicola Cancedda
Citation:
Journée ATALA Traduction Automatique, Paris, France, December 1, 2007.
Abstract:
We present some on-going research on
phrase-based Statistical Machine Translation
using flexible phrases that may
contain gaps of variable lengths. This
allows us to naturally handle various
linguistic phenomena such as negations
or separable particles. We integrate this
within the standard Maximum Entropy
model using some dedicated feature
functions, and describe a beam-search
stack decoder that handles these noncontiguous,
elastic phrases. Preliminary
experimental results show that the
translation performance compares
favourably with phrase-based MT using
fixed gap size. We expect that future
results may allow us to leverage the
added flexibility of elastic chunks to
further increase translation performance
Year:
2007
Report number:
2007/062
Attachments: