Classification of telicity using cross-linguistic annotation projection

Abstract

This paper addresses the automatic recognition of telicity, an aspectual notion. A telic event includes a natural endpoint (she walked home), while an atelic event does not (she walked around). Recognizing this difference is a prerequisite for temporal natural language understanding. In English, this classification task is difficult, as telicity is a covert linguistic category. In contrast, in Slavic languages, aspect is part of a verb's meaning and even available in machine-readable dictionaries. Our contributions are as follows. We successfully leverage additional silver standard training data in the form of projected annotations from parallel English-Czech data as well as context information, improving automatic telicity classification for English significantly compared to previous work. We also create a new data set of English texts manually annotated with telicity.

A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios

“…Instead of using parallel corpora, existing high-resource labeled datasets can also be machine-translated into the low-resource language (Khalil et al, 2019;Zhang et al, 2019a;Fei et al, 2020;Amjad et al, 2020). Cross-lingual projections have even been used with English as a target language for detecting linguistic phenomena like modal sense and telicity that are easier to identify in a different language (Zhou et al, 2015;Marasović et al, 2016;Friedrich and Gateva, 2017).…”

Decomposing and Recomposing Event Structure

“…For the event-subevent subprotocol, the 100 sentences come from the portion of the MASC corpus (Ide et al, 2008) that Friedrich et al (2016) annotate for eventivity (EVENT v. STATE) and that Friedrich and Gateva (2017) annotate for telicity (TELIC v. ATELIC). For the event-event subprotocol, the 100 sentences come from the portions of the Richer Event Descriptions corpus (RED; O' Gorman et al, 2016) that are annotated for event subpart relations.…”

Aspectuality Across Genre: A Distributional Semantics Approach

“…For classifying telic and atelic events we are using the Telicity dataset of Friedrich and Gateva (2017), the Captions dataset of Alikhani and Stone (2019), as well as our own proposed DIASPORA dataset. The Telicity dataset contains 1863 sentences extracted from the MASC corpus, where a verb in context is labelled as either telic or atelic.…”

“…Similar to Falk and Martin (2016), we are concerned with classifying verb readings; however, we do not use engineered features as Falk and Martin (2016) do, but directly leverage local contextual information in the form of distributional representations. Our approach is also not reliant on the availability of a parallel corpus as in Friedrich and Gateva (2017). The major difference between our approach of using distributional word representations and previous approaches is that we are using the word representations directly for classification, rather than indirectly by computing similarity scores and using these as features.…”