Automating App Review Response Generation. Previous studies showed that replying to a user review usually has a positive effect on the rating that is given by the user to the app. For example, Hassan et al. found that responding to a review increases the chances of a user updating their given rating by up to six times compared to not responding. To alleviate the labor burden in replying to the bulk of user reviews, developers usually adopt a templatebased strategy where the templates can express appreciation for using the app or mention the company email address for users to follow up. However, reading a large number of user reviews every day is not an easy task for developers. Thus, there is a need for more automation to help developers respond to user reviews.  the :128,aforementioned need, in this work we propose a novel approach RRGen that automatically generates review responses by learning knowledge relations between reviews and their responses. RRGen explicitly incorporates review attributes, such as user rating and review length, and learns the relations between reviews and corresponding responses in a supervised way from the available training data. Experiments on 58 apps and 309,246 reviewresponse pairs highlight that RRGen outperforms the baselines by at least 67.4% in terms of BLEU4 (an accuracy measure that is widely used to evaluate dialogue response generation systems). Qualitative analysis also confirms the effectiveness of RRGen in generating relevant and accurate responses.

Discovering Relevant Reviews for Answering ProductRelated Queries With the increasing popularity of ecommerce, the number of productrelated queries generated by customers is growing. Answering these queries manually in real time is infeasible, and so automatic questionanswering systems can be immensely helpful. Product queries are, however, very different from opendomain questions: they tend to be productspecific and the answers they demand can be very subjective. Previous research suggests that reviews are a valuable resource for answering product queries, but a key challenge is the language mismatch between user queries and reviews. To address this, we propose two neural models that discover relevant reviews for answering product queries. We demonstrate that our best model produces strong performance, outperforming stateoftheart systems by consistently finding the most relevant reviews for product queries.