Investigating Proactivity in Task-Oriented Dialogues
Authors: Vevake Balaraman, Bernardo Magnini (Fondazione Bruno Kessler, Università di Trento, 2020)
Topic: Chatbots and Dialogue Systems

Abstract
Proactivity (i.e., the capacity to provide useful information even when not explicitly required) is a fundamental characteristic of human dialogues. Although current task-oriented dialogue systems are good at providing information explicitly requested by the user, they are poor in exhibiting proactivity, which is typical in human-human interactions. In this study, we investigate the presence of proactive behaviours in several available dialogue collections, both human-human and human-machine and show how the data acquisition decision affects the proactive behaviour present in the dataset. We adopt a two-step approach to semi-automatically detect proactive situations in the datasets, where proactivity is not annotated, and show that the dialogues collected with approaches that provide more freedom to the agent/user, exhibit high proactivity.

Introduction
Proactivity is the collaborative attitude of humans to offer information in a dialogue even when such information was not explicitly requested. As an example, a travel operator may suggest points of interest and attractions in a certain area, even if the customer did not explicitly requested for them. The following portion of dialogue, extracted from the Nespole dataset AUTHOR, shows proactive contributions of the travel agent (displayed in italics). Client: good morning; could you suggest any village in the Val di Fiemme to me; where it's possible to skate for example; that is does any skating rink exist in the Val di Fiemme; Agent: yes; in the whole of Val di Fiemme there are some outdoor skating rinks; where you can skate usually in the afternoon; in some rinks even in the morning; and then right in Cavalese there's a skating rink an ice rink; where even some courses are organized; where they also hold hockey or skating shows; and it's indoors. In this dialogue situation the travel agent provides indications both about the opening time of skating rinks and about skating courses, which were not requested by the customer. We may think proactivity as a guess of the agent with respect to the customer needs, with the purpose of anticipating expected requests, this way facilitating the achievements of the dialogue goals. Proactivity is a crucial characteristics of human-human dialogues. It is related to the so called principles of cooperative dialogue, which have been summarized in the popular Grice's maxims AUTHOR. In particular, proactivity follows the maxim of quantity, where one tries to be as informative as one possibly can, and gives as much information as it is needed, and no more. Under this maxim, proactivity has to find a trade-off between providing useful not requested information and limiting excessive not needed information. For instance, in the context of our dialogue about skating in Val di Fiemme, an agent suggesting a good pizzeria would probably be perceived as a violation of the quantity maxim, as this information seems not enough needed in that context. Despite the large use of proactivity that we note in everyday human-human dialogues, proactive behaviours are poorly represented in most of the models at the core of the last generation of task-oriented dialogue systems. Overall, we notice a general lack of cooperative phenomena (e.g., clarification questions, explanatory dialogues, proactivity, etc.), that characterize, and somehow make efficient, task-oriented human-human dialogues. A notable exception are recommendation systems AUTHOR, where, however, the focus is on influencing the user towards a specific goal (e.g., buy a certain product). Instead, we intend proactivity to be a general collaborative strategy aiming at improving the quality and effectiveness of the conversation. As an example, proactivity can be used to anticipate future requests of the user (e.g., providing the telephone number of a certain restaurant), or to recover from failure situations (e.g., offering possible alternatives when there are no restaurants satisfying the user desires). The main purpose of the paper is to conduct an empirical analysis over several existing task-oriented dialogue datasets, used to train dialogue models, in order to verify the presence of proactive behaviours. More specifically, we consider a human-human dialogue corpus collected with a role-taking methodology, i.e., Nespole, and compare it with other task-oriented dialogues collected either with Wizard of Oz or with bootstrapping methods. To conduct such a comparison, the major obstacle is that in both cases, proactivity is not annotated in any way, and we had to figure out methods (addressed in Section methodology) to semi-automatically detect proactive situations. Results confirm that dialogues collected through role-taking methodology show a much richer presence of proactivity, which is a challenge for future dialogue systems.

Conclusion
Task-oriented dialogue systems have shown to be effective in providing services to users with a high success rate. However, the interaction still lacks an effective proactive approach, which is typical in human-human conversations. In this study, we compare proactive behaviours in several available dialogue datasets, and show that the dialogues collected through Wizard of Oz contain a small proportion of system proactive responses, while dialogues collected through simulation-based and role-taking methodologies contain higher degree of proactivity. To sum up, we suggest that data collection strategies should be better aware that their designing principles have strong influence on the quality of the dialogues. Particularly, we recommend higher attention to proactive behaviours, and, in general, to collaborative phenomena.