The Effect of Context on Metaphor Paraphrase Aptness Judgments

Yuri Bizzoni, Shalom Lappin


Abstract
We conduct two experiments to study the effect of context on metaphor paraphrase aptness judgments. The first is an AMT crowd source task in which speakers rank metaphor-paraphrase candidate sentence pairs in short document contexts for paraphrase aptness. In the second we train a composite DNN to predict these human judgments, first in binary classifier mode, and then as gradient ratings. We found that for both mean human judgments and our DNN’s predictions, adding document context compresses the aptness scores towards the center of the scale, raising low out-of-context ratings and decreasing high out-of-context scores. We offer a provisional explanation for this compression effect.
Anthology ID:
W19-0414
Volume:
Proceedings of the 13th International Conference on Computational Semantics - Long Papers
Month:
May
Year:
2019
Address:
Gothenburg, Sweden
Venues:
IWCS | WS
SIG:
SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
165–175
Language:
URL:
https://aclanthology.org/W19-0414
DOI:
10.18653/v1/W19-0414
Bibkey:
Cite (ACL):
Yuri Bizzoni and Shalom Lappin. 2019. The Effect of Context on Metaphor Paraphrase Aptness Judgments. In Proceedings of the 13th International Conference on Computational Semantics - Long Papers, pages 165–175, Gothenburg, Sweden. Association for Computational Linguistics.
Cite (Informal):
The Effect of Context on Metaphor Paraphrase Aptness Judgments (Bizzoni & Lappin, 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-0414.pdf
Code
 yuri-bizzoni/Metaphor-Paraphrase