ARTA: Collection and Classification of Ambiguous Requests and Thoughtful Actions
Shohei Tanaka, Koichiro Yoshino, Katsuhito Sudoh, Satoshi Nakamura

TL;DR
This paper presents ARTA, a dataset and model for classifying ambiguous user requests into appropriate system actions, using positive/unlabeled learning to handle incomplete annotations and improve classification accuracy.
Contribution
The paper introduces a novel corpus and applies PU learning for classifying ambiguous requests, addressing annotation challenges in dialogue systems.
Findings
PU learning outperforms PN learning in classification accuracy.
The corpus enables better understanding of ambiguous user requests.
The model effectively suggests thoughtful system actions.
Abstract
Human-assisting systems such as dialogue systems must take thoughtful, appropriate actions not only for clear and unambiguous user requests, but also for ambiguous user requests, even if the users themselves are not aware of their potential requirements. To construct such a dialogue agent, we collected a corpus and developed a model that classifies ambiguous user requests into corresponding system actions. In order to collect a high-quality corpus, we asked workers to input antecedent user requests whose pre-defined actions could be regarded as thoughtful. Although multiple actions could be identified as thoughtful for a single user request, annotating all combinations of user requests and system actions is impractical. For this reason, we fully annotated only the test data and left the annotation of the training data incomplete. In order to train the classification model on such…
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Taxonomy
TopicsSpeech and dialogue systems · AI in Service Interactions · Natural Language Processing Techniques
MethodsAttentive Walk-Aggregating Graph Neural Network
