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Found 7 papers, 1 papers with code
An Embarrassingly Simple Method to Mitigate Undesirable Properties of Pretrained Language Model Tokenizers
We introduce FLOTA (Few Longest Token Approximation), a simple yet effective method to improve the tokenization of pretrained language models (PLMs).
Derivational Morphology Reveals Analogical Generalization in Large Language Models
As expected, rule-based and analogical models explain the predictions of GPT-J equally well for adjectives with regular nominalization patterns.
Not wacky vs. definitely wacky: A study of scalar adverbs in pretrained language models
In such models, words that are similar in their topical associations but differ in their logical force tend to emerge as semantically close, creating well-known challenges for NLP applications that involve logical reasoning.
A Graph Auto-encoder Model of Derivational Morphology
The auto-encoder models MWF in English surprisingly well by combining syntactic and semantic information with associative information from the mental lexicon.
Predicting the Growth of Morphological Families from Social and Linguistic Factors
We present the first study that examines the evolution of morphological families, i. e., sets of morphologically related words such as {``}trump{''}, {``}antitrumpism{''}, and {``}detrumpify{''}, in social media.
On Hapax Legomena and Morphological Productivity
Quantifying and predicting morphological productivity is a long-standing challenge in corpus linguistics and psycholinguistics.
Using Resource-Rich Languages to Improve Morphological Analysis of Under-Resourced Languages
In a case study of two such languages, Tagalog and Zulu, we show that an easily obtainable English wordlist can be deployed to seed a morphological analysis algorithm from a small training set of conversational transcripts.
