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East Tukano languages are known for their developed nominal classification systems. Wa’ikhana (Piratapuyo) is in this sense a typical member of the family, since it has an open system with a large number of classes and with class markers which exercise derivational and agreement functions. Among all the Wa’ikhana inanimate classes, the class ‘round’ stands out for its semantic and morphosyntactic features. It is one of the most (if not the most) extensive classes, which includes round objects as well as objects of less prototypical shapes. Its markers in non-plural number have the biggest number of allomorphs, even though allomorphy of classifiers is not typical for this language. Besides, the class ‘round’ has a distinct plural marker, another feature absent from most classifiers. Comparison between Wa’ikhana and other related languages demonstrates that these peculiarities are shared by many East Tukano languages. Thus, the present paper aims to describe the class ‘round’ in Wa’ikhana and other languages of the family, and to show their common features as well as the features that distinguish Wa’ikhana.
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Deep Subjecthood: Higher-Order Grammatical Features in Multilingual BERT
We investigate how Multilingual BERT (mBERT) encodes grammar by examining how the high-order grammatical feature of morphosyntactic alignment (how different languages define what counts as a “subject”) is manifested across the embedding spaces of different languages. To understand if and how morphosyntactic alignment affects contextual embedding spaces, we train classifiers to recover the subjecthood of mBERT embeddings in transitive sentences (which do not contain overt information about morphosyntactic alignment) and then evaluate them zero-shot on intransitive sentences (where subjecthood classification depends on alignment), within and across languages. We find that the resulting classifier distributions reflect the morphosyntactic alignment of their training languages. Our results demonstrate that mBERT representations are influenced by high-level grammatical features that are not manifested in any one input sentence, and that this is robust across languages. Further examining the characteristics that our classifiers rely on, we find that features such as passive voice, animacy and case strongly correlate with classification decisions, suggesting that mBERT does not encode subjecthood purely syntactically, but that subjecthood embedding is continuous and dependent on semantic and discourse factors, as is proposed in much of the functional linguistics literature. Together, these results provide insight into how grammatical features manifest in contextual embedding spaces, at a level of abstraction not covered by previous work.
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- Award ID(s):
- 1947307
- PAR ID:
- 10259961
- Date Published:
- Journal Name:
- Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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