4 papers
Syntactic Inductive Bias in Transformer Language Models: Especially Helpful for Low-Resource Languages?
Luke Gessler, Nathan Schneider
A line of work on Transformer-based language models such as BERT has attempted to use syntactic inductive bias to enhance the pretraining process, on the theory that building synta…
BERT Has Uncommon Sense: Similarity Ranking for Word Sense BERTology
Luke Gessler, Nathan Schneider
An important question concerning contextualized word embedding (CWE) models like BERT is how well they can represent different word senses, especially those in the long tail of unc…
Supersense and Sensibility: Proxy Tasks for Semantic Annotation of Prepositions
Luke Gessler, Shira Wein, Nathan Schneider
Prepositional supersense annotation is time-consuming and requires expert training. Here, we present two sensible methods for obtaining prepositional supersense annotations by elic…
Supervised Grapheme-to-Phoneme Conversion of Orthographic Schwas in Hindi and Punjabi
Aryaman Arora, Luke Gessler, Nathan Schneider
Hindi grapheme-to-phoneme (G2P) conversion is mostly trivial, with one exception: whether a schwa represented in the orthography is pronounced or unpronounced (deleted). Previous w…