Jillian Aurisano


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Reference Resolution and New Entities in Exploratory Data Visualization: From Controlled to Unconstrained Interactions with a Conversational Assistant
Abari Bhattacharya | Abhinav Kumar | Barbara Di Eugenio | Roderick Tabalba | Jillian Aurisano | Veronica Grosso | Andrew Johnson | Jason Leigh | Moira Zellner
Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue

In the context of data visualization, as in other grounded settings, referents are created by the task the agents engage in and are salient because they belong to the shared physical setting. Our focus is on resolving references to visualizations on large displays; crucially, reference resolution is directly involved in the process of creating new entities, namely new visualizations. First, we developed a reference resolution model for a conversational assistant. We trained the assistant on controlled dialogues for data visualizations involving a single user. Second, we ported the conversational assistant including its reference resolution model to a different domain, supporting two users collaborating on a data exploration task. We explore how the new setting affects reference detection and resolution; we compare the performance in the controlled vs unconstrained setting, and discuss the general lessons that we draw from this adaptation.


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Augmenting Small Data to Classify Contextualized Dialogue Acts for Exploratory Visualization
Abhinav Kumar | Barbara Di Eugenio | Jillian Aurisano | Andrew Johnson
Proceedings of the Twelfth Language Resources and Evaluation Conference

Our goal is to develop an intelligent assistant to support users explore data via visualizations. We have collected a new corpus of conversations, CHICAGO-CRIME-VIS, geared towards supporting data visualization exploration, and we have annotated it for a variety of features, including contextualized dialogue acts. In this paper, we describe our strategies and their evaluation for dialogue act classification. We highlight how thinking aloud affects interpretation of dialogue acts in our setting and how to best capture that information. A key component of our strategy is data augmentation as applied to the training data, since our corpus is inherently small. We ran experiments with the Balanced Bagging Classifier (BAGC), Condiontal Random Field (CRF), and several Long Short Term Memory (LSTM) networks, and found that all of them improved compared to the baseline (e.g., without the data augmentation pipeline). CRF outperformed the other classification algorithms, with the LSTM networks showing modest improvement, even after obtaining a performance boost from domain-trained word embeddings. This result is of note because training a CRF is far less resource-intensive than training deep learning models, hence given a similar if not better performance, traditional methods may still be preferable in order to lower resource consumption.


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Towards a dialogue system that supports rich visualizations of data
Abhinav Kumar | Jillian Aurisano | Barbara Di Eugenio | Andrew Johnson | Alberto Gonzalez | Jason Leigh
Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue