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Domestic service robots (DSRs) are a promising solution to the shortage ofhome care workers. However, one of the main limitations of DSRs is theirinability to interact naturally through language. Recently, data-drivenapproaches have been shown to be effective for tackling this limitation;however, they often require large-scale datasets, which is costly. Based onthis background, we aim to perform automatic sentence generation of fetchinginstructions: for example, "Bring me a green tea bottle on the table." This isparticularly challenging because appropriate expressions depend on the targetobject, as well as its surroundings. In this paper, we propose the attentionbranch encoder--decoder network (ABEN), to generate sentences from visualinputs. Unlike other approaches, the ABEN has multimodal attention branchesthat use subword-level attention and generate sentences based on subwordembeddings. In experiments, we compared the ABEN with a baseline method usingfour standard metrics in image captioning. Results show that the ABENoutperformed the baseline in terms of these metrics.

Alleviating the Burden of Labeling: Sentence Generation by Attention Branch Encoder-Decoder Network