Language is subject to a variety of pressures. Recent work has documented that many aspects of language structure have properties that appear to be shaped by biases for the efficient communication of semantic meaning. Other work has investigated the role of social pressures, whereby linguistic variants can acquire positive or negative evaluation based on who is perceived to be using them. While the influence of these two sets of biases on language change has been well documented, they have typically been treated separately, in distinct lines of research. We used a miniature language paradigm to test how these biases interact in language change. Specifically, we asked whether pressures to mark social meaning can lead linguistic systems to become less efficient at communicating semantic meaning. We exposed participants to a miniature language with uninformative constituent order and two dialects, one that employed case and one that did not. In the instructions, we socially biased participants toward users of the case dialect, users of the no-case dialect, or neither. Learners biased toward the no-case dialect dropped informative case, thus creating a linguistic system with high message uncertainty. They failed to compensate for this increased message uncertainty even after additional exposure to the novel language. Case was retained in all other conditions. These findings suggest that social biases not only interact with biases for efficient communication in language change but also can lead to linguistic systems that are less efficient at communicating semantic meaning.
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PropBank Comes of Age—Larger, Smarter, and more Diverse
This paper describes the evolution of the PropBank approach to semantic role labeling over the last two decades. During this time the PropBank frame files have been expanded to include non-verbal predicates such as adjectives, prepositions and multi-word expressions. The number of domains, genres and languages that have been PropBanked has also expanded greatly, creating an opportunity for much more challenging and robust testing of the generalization capabilities of PropBank semantic role labeling systems. We also describe the substantial effort that has gone into ensuring the consistency and reliability of the various annotated datasets and resources, to better support the training and evaluation of such systems
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- Award ID(s):
- 1764048
- NSF-PAR ID:
- 10396526
- Editor(s):
- Vivi Nastase; Ellie Pavlick; Mohammad Taher Pilehvar; Jose Camacho-Collados; Alessandro Raganato
- Date Published:
- Journal Name:
- Proceedings of the 11th Joint Conference on Lexical and Computational Semantics
- Page Range / eLocation ID:
- 278 to 288
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.18653/v1/2022.starsem-1.24
