Improving a Neural-based Tagger for Multiword Expressions Identification
Dušan Variš, Natalia Klyueva · 2018
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.