Papers by Jean-Philippe Bernardy
The Influence of Context on Sentence Acceptability Judgements (P18-2)
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| Challenge: | a paper examining the influence of document context on acceptability judgements for English sentences is published in journal journal of linguistics. |
| Approach: | They propose to use document context to assess acceptability judgements for English sentences . they also test the accuracy of neural models that incorporate document context during training . |
| Outcome: | The proposed model improves acceptability ratings for ill-formed sentences, but reduces them for well-formed ones. |
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 Sentiments in Algerian Code-switched User-generated Comments (2020.lrec-1)
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| Challenge: | a recent study has focused on sentiment analysis for the Arabic variety, but it has been extended to other domains. |
| Approach: | They build a corpus of 36,000 code-switched user-generated comments annotated for sentiments in Algerian Arabic. |
| Outcome: | The proposed model performs better on unedited code-switched and unbalanced data across sentiment classes. |
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. |
Normalising Non-standardised Orthography in Algerian Code-switched User-generated Data (D19-55)
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| Challenge: | a new corpus of unstructured data from social media is presenting challenges to NLP research . standardisation is neither natural nor universal, it is rather a human invention. |
| Approach: | They compile a parallel corpus of Arabic textual data matched with human annotations . they use a deep neural model designed to deal with context-dependent spelling correction . |
| Outcome: | The proposed model performs best with two CNN sub-network encoders and an LSTM decoder . pre-processing data token-by-token with edit-distance aligner significantly improves performance . |
Improving the Precision of Natural Textual Entailment Problem Datasets (2020.lrec-1)
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| Challenge: | a new method to modify natural textual entailment problem datasets is proposed . we aim to examine to what extent existing corpora for NLI capture precise reasoning . |
| Approach: | They propose to modify natural textual entailment problem datasets to reflect more precise notion of entropy. |
| Outcome: | The proposed method can be used to test precise natural-language inference systems . it uses crowd-sourced experts to make any missing hypotheses explicit . |