Neural Associative Memory For Dualsequence Modeling

# Neural Associative Memory for Dual-Sequence Modeling

**arXiv ID:** 1606.03864
**Authors:** Dirk Weissenborn
**Published:** 2016-06-13T09:08:04Z
**Abstract:**
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.

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## References

- [arXiv:1606.03864](http://arxiv.org/abs/1606.03864v2)