Rowan Hall Maudslay

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VLDB 2026 Research / reviewers in the wild / expert

dblp:248/7613 · DBLP profile ↗

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10ranked-venue papers

6as first author

6since 2021 · last 2026

0009-0009-1339-0646ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 10 · 6 first-author · 6 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence

4 papers

Trustworthy machine learning · 42% Information extraction and text analysis · 35% Language models and text generation · 12%

Topics — the 9 heaviest of 9, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Machine learning › Representation and self-supervised learning › word representation contextual representation	0.4	1	2020	Speakers Fill Lexical Semantic Gaps with Context · EMNLP (1) 2020
Machine learning › Trustworthy machine learning information-theoretic probing	0.4	1	2020	Information-Theoretic Probing for Linguistic Structure · ACL 2020
Natural language and speech › Information extraction and text analysis › lexical semantics lexical ambiguity	0.4	1	2020	Speakers Fill Lexical Semantic Gaps with Context · EMNLP (1) 2020
Natural language and speech › Information extraction and text analysis lexical semantics	0.4	1	2020	Speakers Fill Lexical Semantic Gaps with Context · EMNLP (1) 2020
Natural language and speech › Language models and text generation › natural language understanding linguistic knowledge in language models	0.4	1	2020	A Tale of a Probe and a Parser · ACL 2020
Natural language and speech › Information extraction and text analysis linguistic probing	0.4	1	2020	Information-Theoretic Probing for Linguistic Structure · ACL 2020
Machine learning › Trustworthy machine learning counterfactual data augmentation	0.4	1	2019	It's All in the Name: Mitigating Gender Bias with Name-Based Counterfactual Data Substitution · EMNLP/IJCNLP (1) 2019
Machine learning › Trustworthy machine learning fairness	0.4	1	2019	It's All in the Name: Mitigating Gender Bias with Name-Based Counterfactual Data Substitution · EMNLP/IJCNLP (1) 2019
Machine learning › Trustworthy machine learning › fairness › bias mitigation gender bias mitigation	0.4	1	2019	It's All in the Name: Mitigating Gender Bias with Name-Based Counterfactual Data Substitution · EMNLP/IJCNLP (1) 2019

Methods — techniques the papers use, named apart from their topics

wordnet · 0.4probing · 0.4parsing · 0.4mutual information estimation · 0.4entropy estimation · 0.4BERT · 0.4counterfactual data substitution · 0.4

Year	Publication	Venue	Position
2026	Cygnet: Refactoring the Open Multilingual Wordnet Rowan Hall Maudslay, Francis Bond	LREC	1
2025	Large-Scale Japanese Metaphor Corpus Construction: Expanding BCCWJ-Metaphor with Automated Annotation Rowan Hall Maudslay, Kanako Komiya, Sachi Kato, Masayuki Asahara	PACLIC	2
2024	ChainNet: Structured Metaphor and Metonymy in WordNet abstract The senses of a word exhibit rich internal structure. In a typical lexicon, this structure is overlooked: A word’s senses are encoded as a list, without inter-sense relations. We present ChainNet, a lexical resource which for the first time explicitly identifies these structures, by expressing how senses in the Open English Wordnet are derived from one another. In ChainNet, every nominal sense of a word is either connected to another sense by metaphor or metonymy, or is disconnected (in the case of homonymy). Because WordNet senses are linked to resources which capture information about their meaning, ChainNet represents the first dataset of grounded metaphor and metonymy. Rowan Hall Maudslay, Simone Teufel, Francis Bond, James Pustejovsky	LREC/COLING	1
2022	Metaphorical Polysemy Detection: Conventional Metaphor Meets Word Sense Disambiguation abstract Linguists distinguish between novel and conventional metaphor, a distinction which the metaphor detection task in NLP does not take into account. Instead, metaphoricity is formulated as a property of a token in a sentence, regardless of metaphor type. In this paper, we investigate the limitations of treating conventional metaphors in this way, and advocate for an alternative which we name ‘metaphorical polysemy detection’ (MPD). In MPD, only conventional metaphoricity is treated, and it is formulated as a property of word senses in a lexicon. We develop the first MPD model, which learns to identify conventional metaphors in the English WordNet. To train it, we present a novel training procedure that combines metaphor detection with ‘word sense disambiguation’ (WSD). For evaluation, we manually annotate metaphor in two subsets of WordNet. Our model significantly outperforms a strong baseline based on a state-of-the-art metaphor detection model, attaining an ROC-AUC score of .78 (compared to .65) on one of the sets. Additionally, when paired with a WSD model, our approach outperforms a state-of-the-art metaphor detection model at identifying conventional metaphors in text (.659 F1 compared to .626). Rowan Hall Maudslay, Simone Teufel	COLING	1
2022	UniMorph 4.0: Universal Morphology abstract The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological inflection tables for hundreds of diverse world languages. The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation, and a type-level resource of annotated data in diverse languages realizing that schema. This paper presents the expansions and improvements on several fronts that were made in the last couple of years (since McCarthy et al. (2020)). Collaborative efforts by numerous linguists have added 66 new languages, including 24 endangered languages. We have implemented several improvements to the extraction pipeline to tackle some issues, e.g., missing gender and macrons information. We have amended the schema to use a hierarchical structure that is needed for morphological phenomena like multiple-argument agreement and case stacking, while adding some missing morphological features to make the schema more inclusive. In light of the last UniMorph release, we also augmented the database with morpheme segmentation for 16 languages. Lastly, this new release makes a push towards inclusion of derivational morphology in UniMorph by enriching the data and annotation schema with instances representing derivational processes from MorphyNet. Khuyagbaatar Batsuren, Omer Goldman, Salam Khalifa, Nizar Habash, Witold Kieras, Gábor Bella, Brian Leonard, Garrett Nicolai, Kyle Gorman, Yustinus Ghanggo Ate, Maria Ryskina, Sabrina J. Mielke, Elena Budianskaya, Charbel El-Khaissi, Tiago Pimentel, Michael Gasser, William Lane 0002, Mohit Raj, Matt Coler, Jaime Rafael Montoya Samame, Delio Siticonatzi Camaiteri, Esaú Zumaeta Rojas, Didier López Francis, Arturo Oncevay, Juan López Bautista, Gema Celeste Silva Villegas, Lucas Torroba Hennigen, Adam Ek, David Guriel, Peter Dirix, Jean-Philippe Bernardy, Andrey Scherbakov, Aziyana Bayyr-ool, Antonios Anastasopoulos, Roberto Zariquiey, Karina Sheifer, Sofya Ganieva, Hilaria Cruz, Ritván Karahóga, Stella Markantonatou, George Pavlidis, Matvey Plugaryov, Elena Klyachko, Ali Salehi, Candy Angulo, Jatayu Baxi, Andrew Krizhanovsky, Natalia Krizhanovskaya, Elizabeth Salesky, Clara Vania, Sardana Ivanova, Jennifer C. White, Rowan Hall Maudslay, Josef Valvoda, Ran Zmigrod, Paula Czarnowska, Irene Nikkarinen, Aelita Salchak, Brijesh Bhatt, Christopher Straughn, Zoey Liu, Jonathan Washington, Yuval Pinter, Duygu Ataman, Marcin Wolinski, Totok Suhardijanto, Anna Yablonskaya, Niklas Stoehr, Hossep Dolatian, Zahroh Nuriah, Shyam Ratan, Francis M. Tyers, Edoardo Maria Ponti, Grant Aiton, Aryaman Arora, Richard J. Hatcher, Ritesh Kumar 0002, Jeremiah Young, Daria Rodionova, Anastasia Yemelina, Taras Andrushko, Igor Marchenko, Polina Mashkovtseva, Alexandra Serova, Emily Tucker Prud'hommeaux, Maria Nepomniashchaya, Fausto Giunchiglia, Eleanor Chodroff, Mans Hulden, Miikka Silfverberg, Arya McCarthy, David Yarowsky, Ryan Cotterell, Reut Tsarfaty, Ekaterina Vylomova	LREC	53
2021	Do Syntactic Probes Probe Syntax? Experiments with Jabberwocky Probing abstract Analysing whether neural language models encode linguistic information has become popular in NLP.One method of doing so, which is frequently cited to support the claim that models like BERT encode syntax, is called probing; probes are small supervised models trained to extract linguistic information from another model's output.If a probe is able to predict a particular structure, it is argued that the model whose output it is trained on must have implicitly learnt to encode it.However, drawing a generalisation about a model's linguistic knowledge about a specific phenomena based on what a probe is able to learn may be problematic: in this work, we show that semantic cues in training data means that syntactic probes do not properly isolate syntax.We generate a new corpus of semantically nonsensical but syntactically well-formed Jabberwocky sentences, which we use to evaluate two probes trained on normal data.We train the probes on several popular language models (BERT, GPT-2, and RoBERTa), and find that in all settings they perform worse when evaluated on these data, for one probe by an average of 15.4 UUAS points absolute.Although in most cases they still outperform the baselines, their lead is reduced substantially, e.g. by 53% in the case of BERT for one probe.This begs the question: what empirical scores constitute knowing syntax? Rowan Hall Maudslay, Ryan Cotterell	NAACL-HLT	1
2020	A Tale of a Probe and a Parser abstract Measuring what linguistic information is encoded in neural models of language has become popular in NLP.Researchers approach this enterprise by training "probes"supervised models designed to extract linguistic structure from another model's output.One such probe is the structural probe (Hewitt and Manning, 2019), designed to quantify the extent to which syntactic information is encoded in contextualised word representations.The structural probe has a novel design, unattested in the parsing literature, the precise benefit of which is not immediately obvious.To explore whether syntactic probes would do better to make use of existing techniques, we compare the structural probe to a more traditional parser with an identical lightweight parameterisation.The parser outperforms structural probe on UUAS in seven of nine analysed languages, often by a substantial amount (e.g. by 11.1 points in English).Under a second less common metric, however, there is the opposite trend-the structural probe outperforms the parser.This begs the question: which metric should we prefer? Rowan Hall Maudslay, Josef Valvoda, Tiago Pimentel, Adina Williams, Ryan Cotterell	ACL	1
2020	Information-Theoretic Probing for Linguistic Structure abstract The success of neural networks on a diverse set of NLP tasks has led researchers to question how much these networks actually "know" about natural language.Probes are a natural way of assessing this.When probing, a researcher chooses a linguistic task and trains a supervised model to predict annotations in that linguistic task from the network's learned representations.If the probe does well, the researcher may conclude that the representations encode knowledge related to the task.A commonly held belief is that using simpler models as probes is better; the logic is that simpler models will identify linguistic structure, but not learn the task itself.We propose an information-theoretic operationalization of probing as estimating mutual information that contradicts this received wisdom: one should always select the highest performing probe one can, even if it is more complex, since it will result in a tighter estimate, and thus reveal more of the linguistic information inherent in the representation.The experimental portion of our paper focuses on empirically estimating the mutual information between a linguistic property and BERT, comparing these estimates to several baselines.We evaluate on a set of ten typologically diverse languages often underrepresented in NLP research-plus Englishtotalling eleven languages.Our implementation is available in https://github.com/ rycolab/info-theoretic-probing. Tiago Pimentel, Josef Valvoda, Rowan Hall Maudslay, Ran Zmigrod, Adina Williams, Ryan Cotterell	ACL	3
2020	Speakers Fill Lexical Semantic Gaps with Context abstract Lexical ambiguity is widespread in language, allowing for the reuse of economical word forms and therefore making language more efficient.If ambiguous words cannot be disambiguated from context, however, this gain in efficiency might make language less clearresulting in frequent miscommunication.For a language to be clear and efficiently encoded, we posit that the lexical ambiguity of a word type should correlate with how much information context provides about it, on average.To investigate whether this is the case, we operationalise the lexical ambiguity of a word as the entropy of meanings it can take, and provide two ways to estimate this-one which requires human annotation (using WordNet), and one which does not (using BERT), making it readily applicable to a large number of languages.We validate these measures by showing that, on six high-resource languages, there are significant Pearson correlations between our BERT-based estimate of ambiguity and the number of synonyms a word has in Word-Net (e.g.ρ = 0.40 in English).We then test our main hypothesis-that a word's lexical ambiguity should negatively correlate with its contextual uncertainty-and find significant correlations on all 18 typologically diverse languages we analyse.This suggests that, in the presence of ambiguity, speakers compensate by making contexts more informative. Tiago Pimentel, Rowan Hall Maudslay, Damián E. Blasi, Ryan Cotterell	EMNLP (1)	2
2019	It's All in the Name: Mitigating Gender Bias with Name-Based Counterfactual Data Substitution abstract Rowan Hall Maudslay, Hila Gonen, Ryan Cotterell, Simone Teufel. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019. Rowan Hall Maudslay, Hila Gonen, Ryan Cotterell, Simone Teufel	EMNLP/IJCNLP (1)	1