Survey and evaluation of query intent detection methods

Second ACM International Conference on Web Search and Data Mining, Barcelona (Spain)User interactions with search engines reveal three main underlying intents, namely navigational, informational, and transactional. By providing more accurate results depending on such query intents the performance of search engines can be greatly improved. Therefore, query classification has been an active research topic for the last years. However, while query topic classification has deserved a specific bakeoff, no evaluation campaign has been devoted to the study of automatic query intent detection. In this paper some of the available query intent detection techniques are reviewed, an evaluation framework is proposed, and it is used to compare those methods in order to shed light on their relative performance and drawbacks. As it will be shown, manually prepared gold-standard files are much needed, and traditional pooling is not the most feasible evaluation method. In addition to this, future lines of work in both query intent detection and its evaluation are propose

A Multi-Granularity Matching Attention Network for Query Intent Classification in E-commerce Retrieval

Query intent classification, which aims at assisting customers to find desired products, has become an essential component of the e-commerce search. Existing query intent classification models either design more exquisite models to enhance the representation learning of queries or explore label-graph and multi-task to facilitate models to learn external information. However, these models cannot capture multi-granularity matching features from queries and categories, which makes them hard to mitigate the gap in the expression between informal queries and categories. This paper proposes a Multi-granularity Matching Attention Network (MMAN), which contains three modules: a self-matching module, a char-level matching module, and a semantic-level matching module to comprehensively extract features from the query and a query-category interaction matrix. In this way, the model can eliminate the difference in expression between queries and categories for query intent classification. We conduct extensive offline and online A/B experiments, and the results show that the MMAN significantly outperforms the strong baselines, which shows the superiority and effectiveness of MMAN. MMAN has been deployed in production and brings great commercial value for our company.Comment: Accepted by WWW 202

Conversational Financial Information Retrieval Model (ConFIRM)

With the exponential growth in large language models (LLMs), leveraging their emergent properties for specialized domains like finance merits exploration. However, regulated fields such as finance pose unique constraints, requiring domain-optimized frameworks. We present ConFIRM, an LLM-based conversational financial information retrieval model tailored for query intent classification and knowledge base labeling. ConFIRM comprises two modules: 1) a method to synthesize finance domain-specific question-answer pairs, and 2) evaluation of parameter efficient fine-tuning approaches for the query classification task. We generate a dataset of over 4000 samples, assessing accuracy on a separate test set. ConFIRM achieved over 90% accuracy, essential for regulatory compliance. ConFIRM provides a data-efficient solution to extract precise query intent for financial dialog systems.Comment: 10 pages, 2 figures, 2 tables, 2 appendice