# Structural Ambiguity and its Disambiguation in Language Model Based Parsers: the Case of Dutch Clause Relativization

arXiv: https://arxiv.org/abs/2305.14917 · HTML: https://mlsanity.com/paper/2305.14917/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 45 references that are in the map.
- First posted on arXiv 24 May 2023.

## Builds on (most cited first, 24 of 24)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [A Primer in BERTology: What we know about how BERT works](https://mlsanity.com/paper/2002.12327/) (2020)
- [What do you learn from context? Probing for sentence structure in contextualized word representations](https://mlsanity.com/paper/1905.06316/) (2019)
- [A Systematic Assessment of Syntactic Generalization in Neural Language Models](https://mlsanity.com/paper/2005.03692/) (2020)
- [Neural Language Models as Psycholinguistic Subjects: Representations of Syntactic State](https://mlsanity.com/paper/1903.03260/) (2019)
- [BERTje: A Dutch BERT Model](https://mlsanity.com/paper/1912.09582/) (2019)
- [RobBERT: a Dutch RoBERTa-based Language Model](https://mlsanity.com/paper/2001.06286/) (2020)
- [Structural Persistence in Language Models: Priming as a Window into Abstract Language Representations](https://mlsanity.com/paper/2109.14989/) (2021)
- [Viable Dependency Parsing as Sequence Labeling](https://mlsanity.com/paper/1902.10505/) (2019)
- [Syntactic Surprisal From Neural Models Predicts, But Underestimates, Human Processing Difficulty From Syntactic Ambiguities](https://mlsanity.com/paper/2210.12187/) (2022)
- [SICKNL: A Dataset for Dutch Natural Language Inference](https://mlsanity.com/paper/2101.05716/) (2021)
- [Parsing as Pretraining](https://mlsanity.com/paper/2002.01685/) (2020)
- [The Language Model Understood the Prompt was Ambiguous: Probing Syntactic Uncertainty Through Generation](https://mlsanity.com/paper/2109.07848/) (2021)
- [SP-10K: A Large-scale Evaluation Set for Selectional Preference Acquisition](https://mlsanity.com/paper/1906.02123/) (2019)
- [Neural Proof Nets](https://mlsanity.com/paper/2009.12702/) (2020)
- [Something Old, Something New: Grammar-based CCG Parsing with Transformer Models](https://mlsanity.com/paper/2109.10044/) (2021)
- [Improving BERT Pretraining with Syntactic Supervision](https://mlsanity.com/paper/2104.10516/) (2021)
- [A Frobenius Algebraic Analysis for Parasitic Gaps](https://mlsanity.com/paper/2005.05639/) (2020)
- [Discontinuous Constituency and BERT: A Case Study of Dutch](https://mlsanity.com/paper/2203.01063/) (2022)
- [Lexical and Derivational Meaning in Vector-Based Models of Relativisation](https://mlsanity.com/paper/1711.11513/) (2017)
- [Binary and Multitask Classification Model for Dutch Anaphora Resolution: Die/Dat Prediction](https://mlsanity.com/paper/2001.02943/) (2020)
- [Where's the Learning in Representation Learning for Compositional Semantics and the Case of Thematic Fit](https://mlsanity.com/paper/2208.04749/) (2022)
- [SPINDLE: Spinning Raw Text into Lambda Terms with Graph Attention](https://mlsanity.com/paper/2302.12050/) (2023)

## Built on by (most cited first, 2 of 2)

- [Who Relies More on World Knowledge and Bias for Syntactic Ambiguity Resolution: Humans or LLMs?](https://mlsanity.com/paper/2503.10838/) (2025)
- [When Only Time Will Tell: Interpreting How Transformers Process Local Ambiguities Through the Lens of Restart-Incrementality](https://mlsanity.com/paper/2402.13113/) (2024)

## Abstract (arXiv)

This paper addresses structural ambiguity in Dutch relative clauses. By investigating the task of disambiguation by grounding, we study how the presence of a prior sentence can resolve relative clause ambiguities. We apply this method to two parsing architectures in an attempt to demystify the parsing and language model components of two present-day neural parsers. Results show that a neurosymbolic parser, based on proof nets, is more open to data bias correction than an approach based on universal dependencies, although both setups suffer from a comparable initial data bias.