Living Machines: A study of atypical animacy

TY - CHAP

T1 - Living Machines: A study of atypical animacy

AU - Coll Ardanuy, Mariona

AU - Nanni, Federico

AU - Beelen, Kaspar

AU - Hosseini, Kasra

AU - Ahnert, Ruth

AU - Lawrence, Jon

AU - McDonough, Katherine

AU - Tolfo, Giorgia

AU - Wilson, Daniel CS

AU - McGillivray, Barbara

PY - 2020/12/1

Y1 - 2020/12/1

N2 - This paper proposes a new approach to animacy detection, the task of determining whether an entity is represented as animate in a text. In particular, this work is focused on atypical animacy and examines the scenario in which typically inanimate objects, specifically machines, are given animate attributes. To address it, we have created the first dataset for atypical animacy detection, based on nineteenth-century sentences in English, with machines represented as either animate or inanimate. Our method builds on recent innovations in language modeling, specifically BERT contextualized word embeddings, to better capture fine-grained contextual properties of words. We present a fully unsupervised pipeline, which can be easily adapted to different contexts, and report its performance on an established animacy dataset and our newly introduced resource. We show that our method provides a substantially more accurate characterization of atypical animacy, especially when applied to highly complex forms of language use.

AB - This paper proposes a new approach to animacy detection, the task of determining whether an entity is represented as animate in a text. In particular, this work is focused on atypical animacy and examines the scenario in which typically inanimate objects, specifically machines, are given animate attributes. To address it, we have created the first dataset for atypical animacy detection, based on nineteenth-century sentences in English, with machines represented as either animate or inanimate. Our method builds on recent innovations in language modeling, specifically BERT contextualized word embeddings, to better capture fine-grained contextual properties of words. We present a fully unsupervised pipeline, which can be easily adapted to different contexts, and report its performance on an established animacy dataset and our newly introduced resource. We show that our method provides a substantially more accurate characterization of atypical animacy, especially when applied to highly complex forms of language use.

M3 - Conference paper

SP - 4534

EP - 4545

BT - Proceedings of the 28th International Conference on Computational Linguistics

PB - International Committee on Computational Linguistics

CY - Barcelona, Spain (Online)

ER -