Label-Dependencies Aware Recurrent Neural Networks
This work addresses a specific bottleneck in sequence labeling for NLP, offering a simpler and more effective alternative to complex models like CRFs, though it is incremental in nature.
The authors tackled the problem of improving sequence labeling in NLP by proposing a modified Jordan RNN that re-injects labels as embeddings, achieving better performance than other RNNs and CRF models on Spoken Language Understanding tasks.
In the last few years, Recurrent Neural Networks (RNNs) have proved effective on several NLP tasks. Despite such great success, their ability to model \emph{sequence labeling} is still limited. This lead research toward solutions where RNNs are combined with models which already proved effective in this domain, such as CRFs. In this work we propose a solution far simpler but very effective: an evolution of the simple Jordan RNN, where labels are re-injected as input into the network, and converted into embeddings, in the same way as words. We compare this RNN variant to all the other RNN models, Elman and Jordan RNN, LSTM and GRU, on two well-known tasks of Spoken Language Understanding (SLU). Thanks to label embeddings and their combination at the hidden layer, the proposed variant, which uses more parameters than Elman and Jordan RNNs, but far fewer than LSTM and GRU, is more effective than other RNNs, but also outperforms sophisticated CRF models.