  Semantic role labelling (SRL) is a task in natural language processing which
detects and classifies the semantic arguments associated with the predicates of
a sentence. It is an important step towards understanding the meaning of a
natural language. There exists SRL systems for well-studied languages like
English, Chinese or Japanese but there is not any such system for the
Vietnamese language. In this paper, we present the first SRL system for
Vietnamese with encouraging accuracy. We first demonstrate that a simple
application of SRL techniques developed for English could not give a good
accuracy for Vietnamese. We then introduce a new algorithm for extracting
candidate syntactic constituents, which is much more accurate than the common
node-mapping algorithm usually used in the identification step. Finally, in the
classification step, in addition to the common linguistic features, we propose
novel and useful features for use in SRL. Our SRL system achieves an $F_1$
score of 73.53\% on the Vietnamese PropBank corpus. This system, including
software and corpus, is available as an open source project and we believe that
it is a good baseline for the development of future Vietnamese SRL systems.
