  Recurrent Neural Networks (RNNs) have been widely used in processing natural
language tasks and achieve huge success. Traditional RNNs usually treat each
token in a sentence uniformly and equally. However, this may miss the rich
semantic structure information of a sentence, which is useful for understanding
natural languages. Since semantic structures such as word dependence patterns
are not parameterized, it is a challenge to capture and leverage structure
information. In this paper, we propose an improved variant of RNN,
Multi-Channel RNN (MC-RNN), to dynamically capture and leverage local semantic
structure information. Concretely, MC-RNN contains multiple channels, each of
which represents a local dependence pattern at a time. An attention mechanism
is introduced to combine these patterns at each step, according to the semantic
information. Then we parameterize structure information by adaptively selecting
the most appropriate connection structures among channels. In this way, diverse
local structures and dependence patterns in sentences can be well captured by
MC-RNN. To verify the effectiveness of MC-RNN, we conduct extensive experiments
on typical natural language processing tasks, including neural machine
translation, abstractive summarization, and language modeling. Experimental
results on these tasks all show significant improvements of MC-RNN over current
top systems.
