Learning multiple intent representations for queries has potential applications in facet generation, document ranking, search result diversification, and search explanation. The state-of-the-art model for this task assumes that there is a sequence of intent representations. In this paper, we argue that the model should not be penalized as long as it generates an accurate and complete set of intent representations. Based on this intuition, we propose a stochastic permutation invariant approach for optimizing such networks. We extrinsically evaluate the proposed approach on a facet generation task and demonstrate significant improvements compared to competitive baselines. Our analysis shows that the proposed permutation invariant approach has the highest impact on queries with more potential intents.

Representation learning has always played an important role in information retrieval (IR) systems. Most retrieval models, including recent neural approaches, use representations to calculate similarities between queries and documents to find relevant information from a corpus. Recent models use large-scale pre-trained language models for query representation. The typical use of these models, however, has a major limitation in that they generate only a single representation for a query, which may have multiple intents or facets. The focus of this paper is to address this limitation by considering neural models that support multiple intent representations for each query. Specifically, we propose the NMIR (Neural Multiple Intent Representations) model that can generate semantically different query intents and their appropriate representations. We evaluate our model on query facet generation using a large-scale dataset of real user queries sampled from the Bing search logs. We also provide an extrinsic evaluation of the proposed model using a clarifying question selection task. The results show that NMIR significantly outperforms competitive baselines.

Over recent years, podcasts have emerged as a novel medium for sharing and broadcasting information over the Internet. Audio streaming platforms originally designed for music content, such as Amazon Music, Pandora, and Spotify, have reported a rapid growth, with millions of users consuming podcasts every day. With podcasts emerging as a new medium for consuming information, the need to develop information access systems that enable efficient and effective discovery from a heterogeneous collection of music and podcasts is more important than ever. However, information access in such domains still remains understudied. In this work, we conduct a large-scale log analysis to study and compare podcast and music search behavior on Spotify, a major audio streaming platform. Our findings suggest that there exist fundamental differences in user behavior while searching for podcasts compared to music. Specifically, we identify the need to improve podcast search performance. We propose a simple yet effective transformer-based neural instant search model that retrieves items from a heterogeneous collection of music and podcast content. Our model takes advantage of multi-task learning to optimize for a ranking objective in addition to a query intent type identification objective. Our experiments on large-scale search logs show that the proposed model significantly outperforms strong baselines for both podcast and music queries.

Podcasts are spoken documents across a wide-range of genres and styles, with growing listenership across the world, and a rapidly lowering barrier to entry for both listeners and creators. The great strides in search and recommendation in research and industry have yet to see impact in the podcast space, where recommendations are still largely driven by word of mouth. In this perspective paper, we highlight the many differences between podcasts and other media, and discuss our perspective on challenges and future research directions in the domain of podcast information access.

Asking clarifying questions in response to ambiguous or faceted queries has been recognized as a useful technique for various information retrieval systems, especially conversational search systems with limited bandwidth interfaces. Analyzing and generating clarifying questions have been studied recently but the accurate utilization of user responses to clarifying questions has been relatively less explored. In this paper, we enrich the representations learned by Transformer networks using a novel attention mechanism from external information sources that weights each term in the conversation. We evaluate this Guided Transformer model in a conversational search scenario that includes clarifying questions. 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. Our experiments use a public dataset for search clarification and demonstrate significant improvements compared to competitive baselines.