relevant to the DEliBot project
What makes you change your mind? An empirical investigation in online group decision-making conversations
People leverage group discussions to collaborate in order to solve complex tasks, e.g. in project meetings or hiring panels. By doing so, they engage in a variety of conversational strategies where they try to convince each other of the best approach and ultimately reach a decision. In this work, we investigate methods for detecting what makes someone change their mind. To this end, we leverage a recently introduced dataset containing group discussions of people collaborating to solve a task. To find out what makes someone change their mind, we incorporate various techniques such as neural text classification and language-agnostic change point detection. Evaluation of these methods shows that while the task is not trivial, the best way to approach it is using a language-aware model with learning-to-rank training. Finally, we examine the cues that the models develop as indicative of the cause of a change of mind.
DeliData: A dataset for deliberation in multi-party problem solving
Dialogue systems research is traditionally focused on dialogues between two interlocutors, largely ignoring group conversations. Moreover, most previous research is focused either on task-oriented dialogue (e.g. restaurant bookings) or user engagement (chatbots), while research on systems for collaborative dialogues is an under-explored area. To this end, we introduce the first publicly available dataset containing collaborative conversations on solving a cognitive task, consisting of 500 group dialogues and 14k utterances. Furthermore, we propose a novel annotation schema that captures deliberation cues and release 50 dialogues annotated with it. Finally, we demonstrate the usefulness of the annotated data in training classifiers to predict the constructiveness of a conversation.
The data collection platform, dataset and annotated corpus
are publicly available at https://delibot.xyz