Towards Understanding User Requests in AI Bots

Abstract

This paper presents the task of deeply analyzing user requests: the situation in ordering bots where users input an utterance, the bots would hopefully extract its full product descriptions and then parse them to recognize each product information (PI). This information is useful to help bots better understand user requests, and act upon a much wider range of actions. We model it as a two-layer sequence labeling problem and apply CRFs to solve the task. We investigate two different feature settings, which are manually designed and automatically learnt from neural models of LSTM and CNN, to build good CRF models. In designing features, we propose additional ones based on Brown clustering to enhance the performance of CRF models. To verify the effectiveness, we build a corpus in the retail domain to conduct extensive experiments. The results show that automatically learnt features are very effective and commonly yield better performance than manually designed features. In both settings, adding the information of tags in one layer can also boost the performance of the other layer. Overall, we achieve the best performance with the F-measure of 93.08% in recognizing full product descriptions, and the F-measure of 92.97% in recognizing PI. To our knowledge, this is the first attempt towards understanding user utterances in the context of building Vietnamese ordering bots.