Papers by Rob Van Der Goot
DistaLs: a Comprehensive Collection of Language Distance Measures (2025.emnlp-demos)
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| Challenge: | Existing work on how to measure distances between languages has focused on intuition and typological distance. |
| Approach: | They propose a toolkit that provides users with easy access to language distance measures. |
| Outcome: | The proposed toolkit provides easy access to a wide variety of language distance measures. |
DECAF: A Dynamically Extensible Corpus Analysis Framework (2025.acl-demo)
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| Challenge: | DeCAF is an open-source Python library that enables the analysis and filtering of linguistically-annotated datasets down to the character level. |
| Approach: | They propose a framework that enables the analysis and filtering of linguistically-annotated datasets down to the character level. |
| Outcome: | The proposed framework analyzes a parsed version of the 115M-word BabyLM corpus and generates highly controlled and reproducible experimental settings targeting specific research questions. |
Identifying Open Challenges in Language Identification (2025.acl-long)
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| Challenge: | Existing work on language identification has focused on cross-domain setups, but no systematic comparison is available. |
| Approach: | They propose to train an accurate multi-domain languageidentification model on 2,034 languages and analyze the remaining errors. |
| Outcome: | The proposed model performs well on 2,034 languages with training with 1,000 instances per language and a maximum input length of 100 characters. |
Crossing Domains without Labels: Distant Supervision for Term Extraction (2025.emnlp-industry)
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| Challenge: | Current state-of-the-art methods require expensive human annotation and struggle with domain transfer, limiting their practical deployment. |
| Approach: | They propose a benchmark spanning seven diverse domains to evaluate ATE performance . they propose psuedo-labels and post-hoc heuristics to ensure generalizability . |
| Outcome: | The proposed model outperforms supervised cross-domain encoder models and few-shot learning baselines on the document- and corpus-levels and its GPT-4o teacher on the benchmark. |