1,246 research outputs found
Marrying Universal Dependencies and Universal Morphology
The Universal Dependencies (UD) and Universal Morphology (UniMorph) projects
each present schemata for annotating the morphosyntactic details of language.
Each project also provides corpora of annotated text in many languages - UD at
the token level and UniMorph at the type level. As each corpus is built by
different annotators, language-specific decisions hinder the goal of universal
schemata. With compatibility of tags, each project's annotations could be used
to validate the other's. Additionally, the availability of both type- and
token-level resources would be a boon to tasks such as parsing and homograph
disambiguation. To ease this interoperability, we present a deterministic
mapping from Universal Dependencies v2 features into the UniMorph schema. We
validate our approach by lookup in the UniMorph corpora and find a
macro-average of 64.13% recall. We also note incompatibilities due to paucity
of data on either side. Finally, we present a critical evaluation of the
foundations, strengths, and weaknesses of the two annotation projects.Comment: UDW1
Cross-lingual Argumentation Mining: Machine Translation (and a bit of Projection) is All You Need!
Argumentation mining (AM) requires the identification of complex discourse
structures and has lately been applied with success monolingually. In this
work, we show that the existing resources are, however, not adequate for
assessing cross-lingual AM, due to their heterogeneity or lack of complexity.
We therefore create suitable parallel corpora by (human and machine)
translating a popular AM dataset consisting of persuasive student essays into
German, French, Spanish, and Chinese. We then compare (i) annotation projection
and (ii) bilingual word embeddings based direct transfer strategies for
cross-lingual AM, finding that the former performs considerably better and
almost eliminates the loss from cross-lingual transfer. Moreover, we find that
annotation projection works equally well when using either costly human or
cheap machine translations. Our code and data are available at
\url{http://github.com/UKPLab/coling2018-xling_argument_mining}.Comment: Accepted at Coling 201
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