Input Combination Strategies for Multi-Source Transformer Decoder

In multi-source sequence-to-sequence tasks, the attention mechanism can be modeled in several ways. This topic has been thoroughly studied on recurrent architectures. In this paper, we extend the previous work to the encoder-decoder attention in the Transformer architecture. We propose four differen...

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Bibliographic Details
Published in:arXiv.org 2018-11
Main Authors: Libovický, Jindřich, Helcl, Jindřich, Mareček, David
Format: Article
Language:English
Subjects:
Encoders-Decoders
Online Access:Get full text
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Summary:In multi-source sequence-to-sequence tasks, the attention mechanism can be modeled in several ways. This topic has been thoroughly studied on recurrent architectures. In this paper, we extend the previous work to the encoder-decoder attention in the Transformer architecture. We propose four different input combination strategies for the encoder-decoder attention: serial, parallel, flat, and hierarchical. We evaluate our methods on tasks of multimodal translation and translation with multiple source languages. The experiments show that the models are able to use multiple sources and improve over single source baselines.
ISSN:2331-8422