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Recent studies in distributed vector representations for words have variety of ways to represent words. We propose a various ways using input embedding and output embedding to better represent words than single model. We compared the performance in terms of word analogy and word similarity with each input and output embeddings and various dual embeddings which are the combination of those two embeddings. Performance evaluation results show that the proposed dual embeddings outperform each single embedding, especially with the way of simply adding input and output embeddings. We figured out two things in this paper, i) not only input embedding but also output embedding has such meaning to represent the words and ii) combining input embedding and output embedding as dual embedding outperforms the single embedding when we use input embedding and output embedding individually.

In this demo, we demonstrate an in-device conversational photo sharing service, termed meChat, which helps users share in-device photos easily in messaging applications by searching conversation-related photos automatically. In particular, meChat understands the semantics of on-going conversation and in-device photos by projecting both of them into a single semantic space. Subsequently, it retrieves in-device photos related to the conversation context. Through this process, meChat makes it easy for the users to share the photos while communicating in messaging applications. In addition, it is worth noting that meChat works in a stand-alone, privacy-protecting manner without sending out any in-device photos and conversations to the external servers. This is different from existing photo services (e.g., Google Photos) which resort on the cloud server.