Andrew Johnson


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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 12th 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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Cross-lingual Transfer Learning for Japanese Named Entity Recognition
Andrew Johnson | Penny Karanasou | Judith Gaspers | Dietrich Klakow
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)

This work explores cross-lingual transfer learning (TL) for named entity recognition, focusing on bootstrapping Japanese from English. A deep neural network model is adopted and the best combination of weights to transfer is extensively investigated. Moreover, a novel approach is presented that overcomes linguistic differences between this language pair by romanizing a portion of the Japanese input. Experiments are conducted on external datasets, as well as internal large-scale real-world ones. Gains with TL are achieved for all evaluated cases. Finally, the influence on TL of the target dataset size and of the target tagset distribution is further investigated.


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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