Detecting Context Dependent Messages in a Conversational Environment

Understand

While automatic response generation for building chatbot systems has drawn a lot of attention recently, there is limited understanding on when we need to consider the linguistic context of an input text in the generation process.

• The task is challenging, as messages in a conversational environment are short and informal, and evidence that can indicate a message is context dependent is scarce.
• After a study of social conversation data crawled from the web, we observed that some characteristics estimated from the responses of messages are discriminative for identifying context dependent messages.
• With the characteristics as weak supervision, we propose using a Long Short Term Memory (LSTM) network to learn a classifier.