3,110 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
Memetic cooperative coevolution of Elman recurrent neural networks
Cooperative coevolution decomposes an optimi-
sation problem into subcomponents and collectively solves
them using evolutionary algorithms. Memetic algorithms
provides enhancement to evolutionary algorithms with local
search. Recently, the incorporation of local search into a
memetic cooperative coevolution method has shown to be
efficient for training feedforward networks on pattern classification problems. This paper applies the memetic cooperative coevolution method for training recurrent neural networks on grammatical inference problems. The results show
that the proposed method achieves better performance in
terms of optimisation time and robustness
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