DeepMT 2016 - Deep Machine Translation Workshop 2016

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-trans...). 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Papers on original and unpublished research are welcome on any of the topics listed above in general, and specifically on any of the following: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