Papers by Adam Ek
Can the Transformer Learn Nested Recursion with Symbol Masking? (2021.findings-acl)
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| Challenge: | Existing studies on self-attention models show they can generalise to context-free languages . |
| Approach: | They use encoder-only models to train to generalise nested symbols . they find that the predictions made correspond to a simple parenthesis counting strategy . |
| Outcome: | The proposed model can generalise to nested structures at higher nesting depth and with a push-down automaton. |
Identifying Speakers and Addressees in Dialogues Extracted from Literary Fiction (L18-1)
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Adam Ek, Mats Wirén, Robert Östling, Kristina N. Björkenstam, Gintarė Grigonytė, Sofia Gustafson Capková
| Challenge: | Using a sequence labeling approach, it is possible to identify speakers and addressees in dialogues extracted from literary fiction using a small amount of training data. |
| Approach: | They propose to use a sequence labeling approach applied to a given set of characters to identify speakers and addressees in dialogues extracted from literary fiction. |
| Outcome: | The proposed method allows for enriched search facilities and construction of social networks from the corpora. |
UniMorph 4.0: Universal Morphology (2022.lrec-1)
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Khuyagbaatar Batsuren, Omer Goldman, Salam Khalifa, Nizar Habash, Witold Kieraś, Gábor Bella, Brian Leonard, Garrett Nicolai, Kyle Gorman, Yustinus Ghanggo Ate, Maria Ryskina, Sabrina Mielke, Elena Budianskaya, Charbel El-Khaissi, Tiago Pimentel, Michael Gasser, William Abbott 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
| Challenge: | The Universal Morphology project provides broad-coverage instantiated morphological inflection tables for hundreds of diverse languages. |
| Approach: | They propose a language-independent feature schema for rich morphological annotation and a type-level resource of annotated data in diverse languages realizing that schema. |
| Outcome: | The proposed schema has added 66 new languages, including 24 endangered languages. |
Synthetic Propaganda Embeddings To Train A Linear Projection (D19-50)
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| Challenge: | Using contextualized token embeddings, we can extract features of propaganda from contextualized embeddnings without fine-tuning the large parameters of the base model. |
| Approach: | They propose a method for detecting fine-grained categories of propaganda in text by generating synthetically generated embeddings from pre-trained language models. |
| Outcome: | The proposed method is used in the first shared task in fine-grained propaganda detection at NLP4IF as Team Stalin. |