Abstract: Volitionality and subject animacy are fundamental and closely related properties of events. Their classification, however, is challenging because it requires contextual text understanding and a huge amount of labeled data. This paper proposes a novel method that jointly learns volitionality and subject animacy at a low cost, heuristically labeling events in a raw corpus. Volitionality labels are assigned using a small lexicon of volitional and non-volitional adverbs such as deliberately and accidentally; subject animacy labels are assigned using a list of animate and inanimate nouns obtained from ontological knowledge. Since our labeling method assigns labels only to a biased set of events, a classifier is trained with regularization to take into account the property. This paper explores the following two approaches: bias reduction and adversarial representation learning. In bias reduction, the words used for labeling are regarded as bias that should not be over-exploited to make predictions, and their estimated contribution towards predictions is penalized.

In adversarial representation learning, the classifier is given unlabeled events as well and makes their latent representations closer to labeled events' ones in an adversarial manner while learning classification on labeled events. We conduct experiments with crowdsourced gold data in Japanese and English and show that our method effectively learns volitionality and subject animacy without manually labeled data.