Amortized Context Vector Inference for Sequence-to-Sequence Networks
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Neural attention (NA) is an effective mechanism for inferring complex structural data dependencies that span long temporal horizons. As a consequence, it has become a key component of sequence-to-sequence models that yield state-of-the-art performance in as hard tasks as abstractive document summarization (ADS), machine translation (MT), and video captioning (VC). NA mechanisms perform inference of context vectors; these constitute weighted sums of deterministic input sequence encodings, adaptively sourced over long temporal horizons. However, recent work in the field of amortized variational inference (AVI) has shown that it is often useful to treat the representations generated by deep networks as latent random variables. This allows for the models to better explore the space of possible representations. Based on this motivation, in this work we introduce a novel regard towards a popular NA mechanism, namely soft-attention (SA). Our approach treats the context vectors generated by SA models as latent variables, the posteriors of which are inferred by employing AVI. Both the means and the covariance matrices of the inferred posteriors are parameterized via deep network mechanisms similar to those employed in the context of standard SA. To illustrate our method, we implement it in the context of popular sequence-to-sequence model variants with SA. We conduct an extensive experimental evaluation using challenging ADS, VC, and MT benchmarks, and show how our approach compares to the baselines.
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