Xianyang Chen


Emotion Arcs of Student Narratives
Swapna Somasundaran | Xianyang Chen | Michael Flor
Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events

This paper studies emotion arcs in student narratives. We construct emotion arcs based on event affect and implied sentiments, which correspond to plot elements in the story. We show that student narratives can show elements of plot structure in their emotion arcs and that properties of these arcs can be useful indicators of narrative quality. We build a system and perform analysis to show that our arc-based features are complementary to previously studied sentiment features in this area.

A Report on the 2020 VUA and TOEFL Metaphor Detection Shared Task
Chee Wee (Ben) Leong | Beata Beigman Klebanov | Chris Hamill | Egon Stemle | Rutuja Ubale | Xianyang Chen
Proceedings of the Second Workshop on Figurative Language Processing

In this paper, we report on the shared task on metaphor identification on VU Amsterdam Metaphor Corpus and on a subset of the TOEFL Native Language Identification Corpus. The shared task was conducted as apart of the ACL 2020 Workshop on Processing Figurative Language.

Go Figure! Multi-task transformer-based architecture for metaphor detection using idioms: ETS team in 2020 metaphor shared task
Xianyang Chen | Chee Wee (Ben) Leong | Michael Flor | Beata Beigman Klebanov
Proceedings of the Second Workshop on Figurative Language Processing

This paper describes the ETS entry to the 2020 Metaphor Detection shared task. Our contribution consists of a sequence of experiments using BERT, starting with a baseline, strengthening it by spell-correcting the TOEFL corpus, followed by a multi-task learning setting, where one of the tasks is the token-level metaphor classification as per the shared task, while the other is meant to provide additional training that we hypothesized to be relevant to the main task. In one case, out-of-domain data manually annotated for metaphor is used for the auxiliary task; in the other case, in-domain data automatically annotated for idioms is used for the auxiliary task. Both multi-task experiments yield promising results.