Marten van Schijndel — accepted AI-conference papers

2025

Disentangling language change: sparse autoencoders quantify the semantic evolution of indigeneity in French

Jacob A. Matthews, Laurent Dubreuil, Imane Terhmina, Yunci Sun, Matthew Wilkens, Marten Van Schijndel

NAACL 2025long

This study presents a novel approach to analyzing historical language change, focusing on the evolving semantics of the French term “indigène(s)” (“indigenous”) between 1825 and 1950. While existing approaches to measuring semantic change with contextual word embeddings (CWE) rely primarily on simil…

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2023

Linguistic Compression in Single-Sentence Human-Written Summaries

Fangcong Yin, Marten Van Schijndel

EMNLP 2023long findings

Summarizing texts involves significant cognitive efforts to compress information. While advances in automatic summarization systems have drawn attention from the NLP and linguistics communities to this topic, there is a lack of computational studies of linguistic patterns in human-written summaries.…

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2022

Discourse Context Predictability Effects in Hindi Word Order

Sidharth Ranjan, Marten van Schijndel, Sumeet Agarwal, Rajakrishnan Rajkumar

EMNLP 2022main

We test the hypothesis that discourse predictability influences Hindi syntactic choice. While prior work has shown that a number of factors (e.g., information status, dependency length, and syntactic surprisal) influence Hindi word order preferences, the role of discourse predictability is underexpl…

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2021

All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Quality

William Timkey, Marten van Schijndel

EMNLP 2021main

Similarity measures are a vital tool for understanding how language models represent and process language. Standard representational similarity measures such as cosine similarity and Euclidean distance have been successfully used in static word embedding models to understand how words cluster in sem…

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2021

Uncovering Constraint-Based Behavior in Neural Models via Targeted Fine-Tuning

Forrest Davis, Marten van Schijndel

ACL 2021long

A growing body of literature has focused on detailing the linguistic knowledge embedded in large, pretrained language models. Existing work has shown that non-linguistic biases in models can drive model behavior away from linguistic generalizations. We hypothesized that competing linguistic processe…

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