Spoken Conversational Search for General Knowledge

It studies the integration of question answering (QA) systems in a dialogue system (DS). Not long ago, each of these research subjects were studied separately; only very recently has studying the intersection between them gained increasing interest (Reddy et al., 2018; Choi et al., 2018). We present a spoken conversational question answering system that is able to answer questions about general knowledge in French by calling two distinct QA systems. It solves coreference and ellipsis by modelling context. Furthermore, it is extensible, thus other components such as neural approaches for question-answering can be easily integrated. It is also possible to collect a dialogue corpus from its iterations. In contrast to most conversational systems which support only speech, two input and output modalities are supported speech and text. Thus it is possible to let the user check the answers by either asking relevant Wikipedia excerpts or by navigating through the retrieved name entities or by exploring the answer details of the QA components: the confidence score as well as the set of explored triplets. Therefore, the user has the final word to consider the answer as correct or incorrect and to1 https://www.wikidata.org