10 citations · 27 across the 11 of their papers we have counts for
11 papers
Neural Proto-Language Reconstruction
Chenxuan Cui, Ying Chen, Qinxin Wang +1
Proto-form reconstruction has been a painstaking process for linguists. Recently, computational models such as RNN and Transformers have been proposed to automate this process. We…
Improved Neural Protoform Reconstruction via Reflex Prediction
Liang Lu, Jingzhi Wang, David R. Mortensen
Protolanguage reconstruction is central to historical linguistics. The comparative method, one of the most influential theoretical and methodological frameworks in the history of t…
Verbing Weirds Language (Models): Evaluation of English Zero-Derivation in Five LLMs
David R. Mortensen, Valentina Izrailevitch, Yunze Xiao +2
Lexical-syntactic flexibility, in the form of conversion (or zero-derivation) is a hallmark of English morphology. In conversion, a word with one part of speech is placed in a non-…
Phonotactic Complexity across Dialects
Ryan Soh-Eun Shim, Kalvin Chang, David R. Mortensen
Received wisdom in linguistic typology holds that if the structure of a language becomes more complex in one dimension, it will simplify in another, building on the assumption that…
Automating Sound Change Prediction for Phylogenetic Inference: A Tukanoan Case Study
Kalvin Chang, Nathaniel R. Robinson, Anna Cai +3
We describe a set of new methods to partially automate linguistic phylogenetic inference given (1) cognate sets with their respective protoforms and sound laws, (2) a mapping from…
Calibrated Seq2seq Models for Efficient and Generalizable Ultra-fine Entity Typing
Yanlin Feng, Adithya Pratapa, David R Mortensen
Ultra-fine entity typing plays a crucial role in information extraction by predicting fine-grained semantic types for entity mentions in text. However, this task poses significant…