  The rise of intelligent assistant systems like Siri and Alexa have led to the
emergence of Conversational Search, a research track of Information Retrieval
(IR) that involves interactive and iterative information-seeking user-system
dialog. Recently released OR-QuAC and TCAsT19 datasets narrow their research
focus on the retrieval aspect of conversational search i.e. fetching the
relevant documents (passages) from a large collection using the conversational
search history. Currently proposed models for these datasets incorporate
history in retrieval by appending the last N turns to the current question
before encoding. We propose to use another history selection approach that
dynamically selects and weighs history turns using the attention mechanism for
question embedding. The novelty of our approach lies in experimenting with soft
attention-based history selection approach in an open-retrieval setting.
