Improving Recurrent Neural Networks For Sequence Labelling
This work addresses sequence labeling problems in natural language processing, but it appears incremental as it builds on existing RNN methods without introducing a new paradigm.
The paper tackled the problem of improving Recurrent Neural Networks (RNNs) for sequence labeling tasks by proposing two new RNN variants, which were shown to be more effective than traditional Elman and Jordan RNNs across four tasks including Spoken Language Understanding and POS tagging, though no concrete numbers were provided.
In this paper we study different types of Recurrent Neural Networks (RNN) for sequence labeling tasks. We propose two new variants of RNNs integrating improvements for sequence labeling, and we compare them to the more traditional Elman and Jordan RNNs. We compare all models, either traditional or new, on four distinct tasks of sequence labeling: two on Spoken Language Understanding (ATIS and MEDIA); and two of POS tagging for the French Treebank (FTB) and the Penn Treebank (PTB) corpora. The results show that our new variants of RNNs are always more effective than the others.