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Found 11 papers, 7 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).
Unsupervised Detection of Contextualized Embedding Bias with Application to Ideology
We propose a fully unsupervised method to detect bias in contextualized embeddings.
The Better Your Syntax, the Better Your Semantics? Probing Pretrained Language Models for the English Comparative Correlative
Construction Grammar (CxG) is a paradigm from cognitive linguistics emphasising the connection between syntax and semantics.
CaMEL: Case Marker Extraction without Labels
We introduce CaMEL (Case Marker Extraction without Labels), a novel and challenging task in computational morphology that is especially relevant for low-resource languages.
Geographic Adaptation of Pretrained Language Models
Evaluation on three tasks, namely fine-tuned as well as zero-shot geolocation prediction and zero-shot prediction of dialect features, shows that geoadaptation is very effective: e. g., we obtain state-of-the-art performance in supervised geolocation prediction and report massive gains over geographically uninformed PLMs on zero-shot geolocation prediction.
Modeling Ideological Salience and Framing in Polarized Online Groups with Graph Neural Networks and Structured Sparsity
The increasing polarization of online political discourse calls for computational tools that automatically detect and monitor ideological divides in social media.
Superbizarre Is Not Superb: Derivational Morphology Improves BERT's Interpretation of Complex Words
How does the input segmentation of pretrained language models (PLMs) affect their interpretations of complex words?
Dynamic Contextualized Word Embeddings
Static word embeddings that represent words by a single vector cannot capture the variability of word meaning in different linguistic and extralinguistic contexts.
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.
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.
DagoBERT: Generating Derivational Morphology with a Pretrained Language Model
Can pretrained language models (PLMs) generate derivationally complex words?
