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Asking clarifying questions in response to ambiguous or faceted queries has been recognized as a useful technique for various information retrieval systems, in particular, conversational search systems with limited bandwidth interfaces. Analyzing and generating clarifying question have been recently studied in the literature. However, accurate utilization of user responses to clarifying questions has been relatively less explored. In this paper, we propose a neural network model based on a novel attention mechanism, called multi source attention network. Our model learns a representation for a user-system conversation that includes clarifying questions. In more detail, with the help of multiple information sources, our model weights each term in the conversation. In our experiments, we use two separate external sources, including the top retrieved documents and a set of different possible clarifying questions for the query. We implement the proposed representation learning model for two downstream tasks in conversational search; document retrieval and next clarifying question selection. We evaluate our models using a public dataset for search clarification. Our experiments demonstrate significant improvements compared to competitive baselines. 

Estimating the quality of a result list, often referred to as query performance prediction (QPP), is a challenging and important task in information retrieval. It can be used as feedback to users, search engines, and system administrators. Although predicting the performance of retrieval models has been extensively studied for the ad-hoc retrieval task, the effectiveness of performance prediction methods for question answering (QA) systems is relatively unstudied. The short length of answers, the dominance of neural models in QA, and the re-ranking nature of most QA systems make performance prediction for QA a unique, important, and technically interesting task. In this paper, we introduce and motivate the task of performance prediction for non-factoid question answering and propose a neural performance predictor for this task. Our experiments on two recent datasets demonstrate that the proposed model outperforms competitive baselines in all settings.