The SEEMPAD Dataset for Emphatic and Persuasive Argumentation
Authors: Elena Cabrio, Serena Villata (Université Cote d'Azur, 2018)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
Emotions play an important role English. Emotions play an important role in argumentation as humans mix rational and emotional attitudes when they argue with each other to take decisions. The SEEMPAD project aims at investigating the role of emotions in human argumentation. In this paper, we present a resource resulting from two ﬁeld experiments involving humans in debates, where arguments put forward during such debates are annotated with the emotions felt by the participants. In addition, in the second experiment, one of the debaters plays the role of the persuader, to convince the other participants about the goodness of her viewpoint applying different persuasion strategies. To the best of our knowledge, this is the ﬁrst dataset of arguments annotated with the emotions collected from the participants of a debate, using facial emotion recognition tools.

Introduction
Argumentation in Artiﬁcial Intelligence (AI) is deﬁned as a formal framework to support decision making (Rahwan and Simari, 2009; Atkinson et al., 2017). In this context, argumentation is used to achieve the so called critical thinking. However, humans are proved to behave differently as they mix rational and emotional attitudes. In order to study the role emotions play in argumentation, we proposed an empirical evaluation of the connection between argumentation and emotions in online debate interactions (Villata et al., 2017; Villata et al., 2018). In particular, in the context of the SEEMPAD project,1 we designed a ﬁeld experiment (Villata et al., 2017) with human participants which investigates the correspondences between the arguments and their relations (i.e., support and attack) put forward during a debate, and the emotions detected by facial emotion recognition systems in the debaters. In addition, given the importance of persuasion in argumentation, we also designed a second ﬁeld experiment (Villata et al., 2018) to study the correlation between the arguments, the relations between them, the emotions detected on the participants, and one of the classical persuasion strategies proposed by Aristotle in rethoric (i.e., logos, ethos, and pathos), played by some participants in the debate to convince the others. In our studies, we selected a behavioral method to extract the emotional manifestations. We used a set of webcams (one for each participant in the discussion) whose recordings have been analyzed with the FaceReader software2 to detect a set of discrete emotions from facial expressions (i.e., happiness, anger, fear, sadness, disgust, and surprise). Participants were placed far from each other, and they were debating through a purely text-based online debate chat (IRC). As a post-processing phase, we aligned the textual arguments the debaters proposed in the chat with the emotions the debaters were feeling while these arguments have been proposed in the debate. In this paper, we describe the two annotated resources resulting from this post-processing of the data we collected in our two ﬁeld experiments. Our resource, called the SEEMPAD resource, is composed of two different annotated datasets, one for each of these experiments3. The datasets collect all the arguments put forward during the debates. These arguments have been paired by attack and support relations, as in standard Argument Mining annotations (Cabrio and Villata, 2018; Lippi and Torroni, 2016). Moreover, arguments are annotated with the source of the argument, and the emotional status captured from all the participants, when the arguments are put forward in the debate. To the best of our knowledge, this is the ﬁrst argumentation dataset annotated with the emotions captured from the output of facial emotion recognition tools. In addition, this resource can be used both for argument mining tasks (i.e., relation prediction), and for emotion classiﬁcation in text, where instances of text annotated with the emotions detected on the participants are usually not available. Finally, text-based emotion classiﬁcation would beneﬁt from the different annotation layers that are present in our dataset. In the reminder of the paper, Sections 2 and 3 describe the dataset resulting from the two ﬁeld experiments. Conclusions end the paper.

Conclusion
This paper presented the SEEMPAD resource for empathic and persuasive argumentation. These datasets have been built on the data resulting from two ﬁeld experiments on humans to assess the impact of emotions during the argumentation in online debates. Several Natural Language Processing tasks can be can be thought on this dataset. First of all, given that the dataset resulting from the Experiment 1 is a gold standard of arguments annotated with emotions, systems for emotion classiﬁcation can use it as a benchmark for evaluation. In addition, a comparison of systems’ performances on this data compared with the standard dataset for emotion classiﬁcation would be interesting, given that in SEEMPAD emotions have not been manually annotated but they have been captured from the participants’ facial emotion expressions. Second, the dataset from Experiment 2 can be used to address a new task in argument mining, namely persuasive strategy detection, in line with the work of (Duthie and Budzynska, 2018) and (Habernal and Gurevych, 2016).