  One of the key challenges in natural language processing (NLP) is to yield
good performance across application domains and languages. In this work, we
investigate the robustness of the mention detection systems, one of the
fundamental tasks in information extraction, via recurrent neural networks
(RNNs). The advantage of RNNs over the traditional approaches is their capacity
to capture long ranges of context and implicitly adapt the word embeddings,
trained on a large corpus, into a task-specific word representation, but still
preserve the original semantic generalization to be helpful across domains. Our
systematic evaluation for RNN architectures demonstrates that RNNs not only
outperform the best reported systems (up to 9\% relative error reduction) in
the general setting but also achieve the state-of-the-art performance in the
cross-domain setting for English. Regarding other languages, RNNs are
significantly better than the traditional methods on the similar task of named
entity recognition for Dutch (up to 22\% relative error reduction).
