4 research outputs found
On Multilingual Training of Neural Dependency Parsers
We show that a recently proposed neural dependency parser can be improved by
joint training on multiple languages from the same family. The parser is
implemented as a deep neural network whose only input is orthographic
representations of words. In order to successfully parse, the network has to
discover how linguistically relevant concepts can be inferred from word
spellings. We analyze the representations of characters and words that are
learned by the network to establish which properties of languages were
accounted for. In particular we show that the parser has approximately learned
to associate Latin characters with their Cyrillic counterparts and that it can
group Polish and Russian words that have a similar grammatical function.
Finally, we evaluate the parser on selected languages from the Universal
Dependencies dataset and show that it is competitive with other recently
proposed state-of-the art methods, while having a simple structure.Comment: preprint accepted into the TSD201
Left-to-Right Dependency Parsing with Pointer Networks
We propose a novel transition-based algorithm that straightforwardly parses
sentences from left to right by building attachments, with being the
length of the input sentence. Similarly to the recent stack-pointer parser by
Ma et al. (2018), we use the pointer network framework that, given a word, can
directly point to a position from the sentence. However, our left-to-right
approach is simpler than the original top-down stack-pointer parser (not
requiring a stack) and reduces transition sequence length in half, from 2-1
actions to . This results in a quadratic non-projective parser that runs
twice as fast as the original while achieving the best accuracy to date on the
English PTB dataset (96.04% UAS, 94.43% LAS) among fully-supervised
single-model dependency parsers, and improves over the former top-down
transition system in the majority of languages tested.Comment: Proceedings of NAACL 2019. 7 page