Neural Associative Memory for Dual Sequence Modeling
Many important NLP problems can be posed as dual sequence or sequence to sequence modeling tasks. Recent advances in building end to end neural architectures have been highly successful in solving such tasks. In this work we propose a new architecture for dual sequence modeling that is based on associative memory. We derive AM RNNs, a recurrent associative memory (AM) which augments generic recurrent neural networks (RNN) This architecture is extended to the Dual AM RNN which operates on two AMs at once. Our models achieve very competitive results on textual entailment. A qualitative analysis demonstrates that long range dependencies between source and target sequence can be bridged effectively using Dual AM RNNs. However, an initial experiment on auto encoding reveals that these benefits are not exploited by the system when learning to solve sequence to sequence tasks which indicates that additional supervision or regularization is needed.
Rep4NLP@ACL