Alleviating the Burden of Labeling: Sentence Generation by Attention Branch Encoder-Decoder Network
This addresses the challenge of enabling natural language interaction for domestic service robots, though it appears incremental as it builds on existing captioning methods.
The paper tackles the problem of generating natural language instructions for domestic service robots from visual inputs, proposing an attention branch encoder-decoder network (ABEN) that outperforms a baseline method on four standard image captioning metrics.
Domestic service robots (DSRs) are a promising solution to the shortage of home care workers. However, one of the main limitations of DSRs is their inability to interact naturally through language. Recently, data-driven approaches have been shown to be effective for tackling this limitation; however, they often require large-scale datasets, which is costly. Based on this background, we aim to perform automatic sentence generation of fetching instructions: for example, "Bring me a green tea bottle on the table." This is particularly challenging because appropriate expressions depend on the target object, as well as its surroundings. In this paper, we propose the attention branch encoder--decoder network (ABEN), to generate sentences from visual inputs. Unlike other approaches, the ABEN has multimodal attention branches that use subword-level attention and generate sentences based on subword embeddings. In experiments, we compared the ABEN with a baseline method using four standard metrics in image captioning. Results show that the ABEN outperformed the baseline in terms of these metrics.