Po-Wei Lin

Po-Wei Lin, Shang-Yu Su, Yun-Nung Chen

The goal of dialogue relation extraction (DRE) is to identify the relation between two entities in a given dialogue. During conversations, speakers may expose their relations to certain entities by explicit or implicit clues, such evidences called "triggers". However, trigger annotations may not be always available for the target data, so it is challenging to leverage such information for enhancing the performance. Therefore, this paper proposes to learn how to identify triggers from the data with trigger annotations and then transfers the trigger-finding capability to other datasets for better performance. The experiments show that the proposed approach is capable of improving relation extraction performance of unseen relations and also demonstrate the transferability of our proposed trigger-finding model across different domains and datasets.

Shang-Yu Su, Po-Wei Lin, Yun-Nung Chen

Spoken dialogue systems that assist users to solve complex tasks such as movie ticket booking have become an emerging research topic in artificial intelligence and natural language processing areas. With a well-designed dialogue system as an intelligent personal assistant, people can accomplish certain tasks more easily via natural language interactions. Today there are several virtual intelligent assistants in the market; however, most systems only focus on textual or vocal interaction. In this paper, we present HUMBO, a system aiming at generating dialogue responses and simultaneously synthesize corresponding visual expressions on faces for better multimodal interaction. HUMBO can (1) let users determine the appearances of virtual assistants by a single image, and (2) generate coherent emotional utterances and facial expressions on the user-provided image. This is not only a brand new research direction but more importantly, an ultimate step toward more human-like virtual assistants.