Mining Implicit Intention Using Attention-Based RNN Encoder-Decoder Model

Nowadays, people are increasingly inclined to use social tools to express their intentions explicitly and implicitly. Most of the work is dedicated to solving the explicit intention detection, ignoring the implicit intention detection, as the former is relatively easy to solve with the classification method. In this work, we use the Attention-Based Encoder-Decoder model which is specified for the sequence-to-sequence task for user implicit intention detection. Our key idea is to leverage the model to "translate" the implicit intention into the corresponding explicit intent by using the parallel corpora built on the social data. Specifically, our model has domain adaptability since the way people express implicit intentions for different domain is variable, while the way to express explicit intentions is mostly in the same form, such as "I want to do sth". In order to demonstrate the effectiveness of our method, we conduct experiments in four domains. The results show that our method offers a powerful "translation" for the implicit intentions and consequently identifies them.