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This is the second workshop on “Deep Machine Translation“, the first
being held in Prague in 2015
(https://ufal.mff.cuni.cz/events/deep-machine-translation-workshop). Its
aim is to bring together researchers and students working on machine
translation approaches and technology using “deep understanding” (not
necessarily using Deep Neural Networks, as the name might suggest, but
certainly not excluding them either). Adding “more linguistics” has
long been considered as a possible way to boost quality of current,
mainly (PB)SMT-based systems. However, there are many ways to do so,
and it was felt a forum is needed where experience can be shared among
people working on such systems.
Papers on original and unpublished research are welcome on any of the
topics listed above in general, and specifically on any of the
following:
* General approaches to the use of linguistic knowledge for Machine Translation
* Contrast and comparison of Deep linguistic methods vs. Deep neural
networks for MT
* Semantics for Machine Translation
* Combination of statistical and “manual” approaches to Machine
Translation, hybrid systems
* Innovative use of manually built lexical resources in Machine
Translation (monolingual, bilingual)
* Deep linguistic representation of meaning / semantics, including
semantic graphs, logical representation, temporal and spatial
representation and grounding
* Deep linguistic analysis and generation
* Joint linguistic and distributional modeling (analysis, generation, transfer)
* Analysis, generation and transfer using graph-based meaning representation
* Incorporating coreference, named entity recognition, words sense
disambiguation, or any other linguistically motivated features into
the MT chain
* Multilingual question-answering and CLIR approaches, including
specific methods for query translation and query matching in a
multilingual setting
* Evaluation methods for standard text translation, query translation,
and CLIR Schedule