Who Is Answering to Whom? Finding “Reply-To” Relations in Group Chats with Long Short-Term Memory Networks

Social networks enjoy great popularity among Internet users while generating large volumes of online short-text conversations every day. It leads to a huge number of free-style asynchronous conversations where multiple users are involved and multiple topics are discussed at the same time in the same place, e.g., an instant group chat in WeChat. Here emerges an interesting problem: As a result of a large number of users and topics, the conversation structure may get into a mess, which interferes with our access to the messages we are interested in. For example, when we open the chat records, we do not want to read all the historical messages. We just want to get the messages that are the most relevant with the messages we care about. Therefore, it is an essential task to understand the logical correlations among messages, which benefits the text mining, the natural language processing and the web intelligence techniques. In this paper, we present the concept of "reply-to" relations to capture most kinds of logical correlations between messages, such as Q&A or complement. Also, we propose a model called LSTM-RT to predict the "reply-to" relations between messages, which is based on the high-quality vector representations of words and LSTM networks. In addition, we give two versions of LSTM-RT based on word level and sentence level, respectively. Experiments conducted on two real-world group chat datasets demonstrate the effectiveness of our proposed models.