Publications
relevant to the DEliBot project
The effect of diversity on group decision-making
Abstract:
We explore different aspects of cognitive diversity and its effect
on the success of group deliberation. To evaluate this, we
use 500 dialogues from small, online groups discussing the
Wason Card Selection task – the DeliData corpus. Leveraging
the corpus, we perform quantitative analysis evaluating three
different measures of cognitive diversity. First, we analyse the
effect of group size as a proxy measure for diversity. Second, we
evaluate the effect of the size of the initial idea pool. Finally, we
look into the content of the discussion by analysing discussed
solutions, discussion patterns, and how conversational probing
can improve those characteristics.
Despite the reputation of groups for compounding bias, we
show that small groups can, through dialogue, overcome intuitive biases and improve individual decision-making. Across a
large sample and different operationalisations, we consistently
find that greater cognitive diversity is associated with more
successful group deliberation.
What makes you change your mind? An empirical investigation in online group decision-making conversations
Abstract:
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
Poster of an early version of the dataset
Abstract:
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
