Which domain are the conversations in?
open-domain Twitter dialogues
Building dialog agents that can converse naturally with humans is a challenging yet intriguing problem of artificial intelligence. In open-domain human-computer conversation, where the conversational agent is expected to respond to human responses in an interesting and engaging way, commonsense knowledge has to be integrated into the model effectively. In this paper, we investigate the impact of providing commonsense knowledge about the concepts covered in the dialog. Our model represents the first attempt to integrating a large commonsense knowledge base into end-to-end conversational models. In the retrieval-based scenario, we propose the Tri-LSTM model to jointly take into account message and commonsense for selecting an appropriate response. Our experiments suggest that the knowledge-augmented models are superior to their knowledge-free counterparts in automatic evaluation.
In recent years, data-driven approaches to building conversation models have been made possible by the proliferation of social media conversation data and the increase of computing power. By relying on a large number of message-response pairs, the Seq2Seq framework BIBREF0 attempts to produce an appropriate response based solely on the message itself, without any memory module. In human-to-human conversations, however, people respond to each other's utterances in a meaningful way not only by paying attention to the latest utterance of the conversational partner itself, but also by recalling relevant information about the concepts covered in the dialogue and integrating it into their responses. Such information may contain personal experience, recent events, commonsense knowledge and more (Figure 1 ). As a result, it is speculated that a conversational model with a “memory look-up” module can mimic human conversations more closely BIBREF1 , BIBREF2 . In open-domain human-computer conversation, where the model is expected to respond to human utterances in an interesting and engaging way, commonsense knowledge has to be integrated into the model effectively. In the context of artificial intelligence (AI), commonsense knowledge is the set of background information that an individual is intended to know or assume and the ability to use it when appropriate BIBREF3 , BIBREF4 , BIBREF5 . Due to the vastness of such kind of knowledge, we speculate that this goal is better suited by employing an external memory module containing commonsense knowledge rather than forcing the system to encode it in model parameters as in traditional methods. In this paper, we investigate how to improve end-to-end dialogue systems by augmenting them with commonsense knowledge, integrated in the form of external memory. The remainder of this paper is as follows: next section proposes related work in the context of conversational models and commonsense knowledge; following, a section describes the proposed model in detail; later, a section illustrates experimental results; finally, the last section proposes concluding remarks and future work.