To fulfill their information needs, users submit sets of related queries to available search engines. Query logs record users' activities along with timestamps and additional search-related information. The analysis of those chronological query logs enables the modeling of search tasks from user interactions. Previous research works rely on clicked URLs and surrounding queries to determine if adjacent queries are part of the same search tasks to segment the query logs properly. However, waiting for clicked URLs or future adjacent queries could render the use of these methods unfeasible in user supporting applications that require model results on the fly. Therefore, we propose a model for sequential search log segmentation. The proposed model uses only query pairs and their time span, generating results suited for on the fly user supporting applications, with improved accuracy over existing search segmentation approaches. We also show the advantages of fine-tuning the proposed model for adjusting the architecture to a small annotated collection.

Users convert their information needs to search queries, which are then run on available search engines. Query logs registered by search engines enable the automatic identification of the search tasks that users perform to fulfill their information needs. Search engine logs contain queries in multiple languages, but most existing methods for search task identification are not multilingual. Some methods rely on search context training of custom embeddings or external indexed collections that support a single language, making it challenging to support the multiple languages of queries run in search engines. Other methods depend on supervised components and user identifiers to model search tasks. The supervised components require labeled collections, which are difficult and costly to get in multiple languages. Also, the need for user identifiers renders these methods unfeasible in user agnostic scenarios. Hence, we propose an unsupervised multilingual approach for search task identification. The proposed approach is user agnostic, enabling its use in both user-independent and personalized scenarios. Furthermore, the multilingual query representation enables us to address the existing trade-off when mapping new queries to the identified search tasks.