Abstract
In this paper, we present a set of improvements introduced to MUMULS, a tagger for the automatic detection of verbal multiword expressions. Our tagger participated in the PARSEME shared task and it was the only one based on neural networks.
We show that character-level embeddings can improve the performance, mainly by reducing the out-of-vocabulary rate. Furthermore, replacing the softmax layer in the decoder by a conditional random field classifier brings additional improvements.
Finally, we compare different context-aware feature representations of input tokens using various encoder architectures. The experiments on Czech show that the combination of character-level embeddings using a convolutional network, self-attentive encoding layer over the word representations and an output conditional random field classifier yields the best empirical results.