  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. Our method carries out text
representation and classifier learning in a unified framework. Experimental
results show that the proposed method can significantly outperform baseline
methods on accuracy of classification.
