Search Results for author: Rowan Hall Maudslay

Found 8 papers, 4 papers with code

UniMorph 4.0: Universal Morphology

no code implementations7 May 2022 Khuyagbaatar Batsuren, Omer Goldman, Salam Khalifa, Nizar Habash, Witold Kieraś, 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, 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 Karahóǧa, 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 White, Rowan Hall Maudslay, Josef Valvoda, Ran Zmigrod, Paula Czarnowska, Irene Nikkarinen, Aelita Salchak, Brijesh Bhatt, Christopher Straughn, Zoey Liu, Jonathan North Washington, Yuval Pinter, Duygu Ataman, Marcin Wolinski, Totok Suhardijanto, Anna Yablonskaya, Niklas Stoehr, Hossep Dolatian, Zahroh Nuriah, Shyam Ratan, Francis M. Tyers, Edoardo M. Ponti, Grant Aiton, Aryaman Arora, Richard J. Hatcher, Ritesh Kumar, Jeremiah Young, Daria Rodionova, Anastasia Yemelina, Taras Andrushko, Igor Marchenko, Polina Mashkovtseva, Alexandra Serova, Emily Prud'hommeaux, Maria Nepomniashchaya, Fausto Giunchiglia, Eleanor Chodroff, Mans Hulden, Miikka Silfverberg, Arya D. McCarthy, David Yarowsky, Ryan Cotterell, Reut Tsarfaty, Ekaterina Vylomova

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.

Morphological Inflection

Do Syntactic Probes Probe Syntax? Experiments with Jabberwocky Probing

no code implementations NAACL 2021 Rowan Hall Maudslay, Ryan Cotterell

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.

Speakers Fill Lexical Semantic Gaps with Context

1 code implementation EMNLP 2020 Tiago Pimentel, Rowan Hall Maudslay, Damián Blasi, Ryan Cotterell

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.

Metaphor Detection using Context and Concreteness

no code implementations WS 2020 Rowan Hall Maudslay, Tiago Pimentel, Ryan Cotterell, Simone Teufel

We report the results of our system on the Metaphor Detection Shared Task at the Second Workshop on Figurative Language Processing 2020.

A Tale of a Probe and a Parser

1 code implementation ACL 2020 Rowan Hall Maudslay, Josef Valvoda, Tiago Pimentel, Adina Williams, Ryan Cotterell

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.

Contextualised Word Representations

Information-Theoretic Probing for Linguistic Structure

1 code implementation ACL 2020 Tiago Pimentel, Josef Valvoda, Rowan Hall Maudslay, Ran Zmigrod, Adina Williams, Ryan Cotterell

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.

Word Embeddings

It's All in the Name: Mitigating Gender Bias with Name-Based Counterfactual Data Substitution

no code implementations IJCNLP 2019 Rowan Hall Maudslay, Hila Gonen, Ryan Cotterell, Simone Teufel

An alternative approach is Counterfactual Data Augmentation (CDA), in which a corpus is duplicated and augmented to remove bias, e. g. by swapping all inherently-gendered words in the copy.

Data Augmentation Word Embeddings

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