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Grammatical Gender, Neo-Whorfianism, and Word Embeddings: A Data-Driven Approach to Linguistic Relativity
Katharina Kann
The relation between language and thought has occupied linguists for at least a century. Neo-Whorfianism, a weak version of the controversial Sapir-Whorf hypothesis, holds that our…
Acquisition of Inflectional Morphology in Artificial Neural Networks With Prior Knowledge
Katharina Kann
How does knowledge of one language's morphology influence learning of inflection rules in a second one? In order to investigate this question in artificial neural network models, w…
Transductive Auxiliary Task Self-Training for Neural Multi-Task Models
Johannes Bjerva, Katharina Kann, Isabelle Augenstein
Multi-task learning and self-training are two common ways to improve a machine learning model's performance in settings with limited training data. Drawing heavily on ideas from th…
Towards Realistic Practices In Low-Resource Natural Language Processing: The Development Set
Katharina Kann, Kyunghyun Cho, Samuel R. Bowman
Development sets are impractical to obtain for real low-resource languages, since using all available data for training is often more effective. However, development sets are widel…
Probing for Semantic Classes: Diagnosing the Meaning Content of Word Embeddings
Yadollah Yaghoobzadeh, Katharina Kann, Timothy J. Hazen +2
Word embeddings typically represent different meanings of a word in a single conflated vector. Empirical analysis of embeddings of ambiguous words is currently limited by the small…
Subword-Level Language Identification for Intra-Word Code-Switching
Manuel Mager, Özlem Çetinoğlu, Katharina Kann
Language identification for code-switching (CS), the phenomenon of alternating between two or more languages in conversations, has traditionally been approached under the assumptio…