Sequence Learning with Side Dependencies

2020 
Numerous sequence learning models have been proposed to capture the sequential dependencies and have achieved remarkable success in a wide range of applications. In addition to sequential dependencies, real sequential data also exhibits side dependencies. For example, in session-based recommender systems, items are naturally related because of their intrinsic attributes such as brand, category, and function; and in document modeling, words are inherently related since they can share similar syntactic functions. Intuitively, such side dependencies provide rich information beyond the sequence (or sequential dependencies); thus they have great potential to advance traditional sequence modeling. However, research on exploring side dependencies for sequence learning is rather limited. In this paper, we study the problem of sequence learning with side dependencies. In particular, we propose a novel sequence learning framework SEE, which can simultaneously capture both sequential and side dependencies. Extensive experiments on real sequential data demonstrate the effectiveness of the proposed framework SEE and the advantages of integrating side dependencies.
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