1 code implementation • ACL 2022 • Lauren Cassidy, Teresa Lynn, James Barry, Jennifer Foster
Modern Irish is a minority language lacking sufficient computational resources for the task of accurate automatic syntactic parsing of user-generated content such as tweets.
no code implementations • EAMT 2022 • Itziar Aldabe, Jane Dunne, Aritz Farwell, Owen Gallagher, Federico Gaspari, Maria Giagkou, Jan Hajic, Jens Peter Kückens, Teresa Lynn, Georg Rehm, German Rigau, Katrin Marheinecke, Stelios Piperidis, Natalia Resende, Tea Vojtěchová, Andy Way
This paper provides an overview of the ongoing European Language Equality(ELE) project, an 18-month action funded by the European Commission which involves 52 partners.
no code implementations • LREC (MWE) 2022 • Abigail Walsh, Teresa Lynn, Jennifer Foster
This paper reports on the investigation of using pre-trained language models for the identification of Irish verbal multiword expressions (vMWEs), comparing the results with the systems submitted for the PARSEME shared task edition 1. 2.
no code implementations • EAMT 2020 • Meghan Dowling, Sheila Castilho, Joss Moorkens, Teresa Lynn, Andy Way
With official status in both Ireland and the EU, there is a need for high-quality English-Irish (EN-GA) machine translation (MT) systems which are suitable for use in a professional translation environment.
no code implementations • COLING (MWE) 2020 • Abigail Walsh, Teresa Lynn, Jennifer Foster
This paper describes the creation of two Irish corpora (labelled and unlabelled) for verbal MWEs for inclusion in the PARSEME Shared Task 1. 2 on automatic identification of verbal MWEs, and the process of developing verbal MWE categories for Irish.
no code implementations • LREC 2022 • James Barry, Joachim Wagner, Lauren Cassidy, Alan Cowap, Teresa Lynn, Abigail Walsh, Mícheál J. Ó Meachair, Jennifer Foster
We compare our gaBERT model to multilingual BERT and the monolingual Irish WikiBERT, and we show that gaBERT provides better representations for a downstream parsing task.
no code implementations • UDW (COLING) 2020 • Sarah McGuinness, Jason Phelan, Abigail Walsh, Teresa Lynn
This paper reports on the analysis and annotation of Multiword Expressions in the Irish Universal Dependency Treebank.
no code implementations • 10 Jun 2024 • David Romero, Chenyang Lyu, Haryo Akbarianto Wibowo, Teresa Lynn, Injy Hamed, Aditya Nanda Kishore, Aishik Mandal, Alina Dragonetti, Artem Abzaliev, Atnafu Lambebo Tonja, Bontu Fufa Balcha, Chenxi Whitehouse, Christian Salamea, Dan John Velasco, David Ifeoluwa Adelani, David Le Meur, Emilio Villa-Cueva, Fajri Koto, Fauzan Farooqui, Frederico Belcavello, Ganzorig Batnasan, Gisela Vallejo, Grainne Caulfield, Guido Ivetta, Haiyue Song, Henok Biadglign Ademtew, Hernán Maina, Holy Lovenia, Israel Abebe Azime, Jan Christian Blaise Cruz, Jay Gala, Jiahui Geng, Jesus-German Ortiz-Barajas, Jinheon Baek, Jocelyn Dunstan, Laura Alonso Alemany, Kumaranage Ravindu Yasas Nagasinghe, Luciana Benotti, Luis Fernando D'Haro, Marcelo Viridiano, Marcos Estecha-Garitagoitia, Maria Camila Buitrago Cabrera, Mario Rodríguez-Cantelar, Mélanie Jouitteau, Mihail Mihaylov, Mohamed Fazli Mohamed Imam, Muhammad Farid Adilazuarda, Munkhjargal Gochoo, Munkh-Erdene Otgonbold, Naome Etori, Olivier Niyomugisha, Paula Mónica Silva, Pranjal Chitale, Raj Dabre, Rendi Chevi, Ruochen Zhang, Ryandito Diandaru, Samuel Cahyawijaya, Santiago Góngora, Soyeong Jeong, Sukannya Purkayastha, Tatsuki Kuribayashi, Teresa Clifford, Thanmay Jayakumar, Tiago Timponi Torrent, Toqeer Ehsan, Vladimir Araujo, Yova Kementchedjhieva, Zara Burzo, Zheng Wei Lim, Zheng Xin Yong, Oana Ignat, Joan Nwatu, Rada Mihalcea, Thamar Solorio, Alham Fikri Aji
Visual Question Answering (VQA) is an important task in multimodal AI, and it is often used to test the ability of vision-language models to understand and reason on knowledge present in both visual and textual data.
no code implementations • 26 Apr 2024 • Teresa Lynn, Malik H. Altakrori, Samar Mohamed Magdy, Rocktim Jyoti Das, Chenyang Lyu, Mohamed Nasr, Younes Samih, Alham Fikri Aji, Preslav Nakov, Shantanu Godbole, Salim Roukos, Radu Florian, Nizar Habash
The rapid evolution of Natural Language Processing (NLP) has favored major languages such as English, leaving a significant gap for many others due to limited resources.
no code implementations • 2 May 2023 • Chenyang Lyu, Zefeng Du, Jitao Xu, Yitao Duan, Minghao Wu, Teresa Lynn, Alham Fikri Aji, Derek F. Wong, Siyou Liu, Longyue Wang
We conclude by emphasizing the critical role of LLMs in guiding the future evolution of MT and offer a roadmap for future exploration in the sector.
1 code implementation • 27 Jul 2021 • James Barry, Joachim Wagner, Lauren Cassidy, Alan Cowap, Teresa Lynn, Abigail Walsh, Mícheál J. Ó Meachair, Jennifer Foster
We compare our gaBERT model to multilingual BERT and the monolingual Irish WikiBERT, and we show that gaBERT provides better representations for a downstream parsing task.
no code implementations • 11 May 2021 • Carla Parra Escartín, Teresa Lynn, Joss Moorkens, Jane Dunne
This article reports on a survey carried out across the Natural Language Processing (NLP) community.
no code implementations • 3 Nov 2020 • Manuela Sanguinetti, Lauren Cassidy, Cristina Bosco, Özlem Çetinoğlu, Alessandra Teresa Cignarella, Teresa Lynn, Ines Rehbein, Josef Ruppenhofer, Djamé Seddah, Amir Zeldes
This article presents a discussion on the main linguistic phenomena which cause difficulties in the analysis of user-generated texts found on the web and in social media, and proposes a set of annotation guidelines for their treatment within the Universal Dependencies (UD) framework of syntactic analysis.
no code implementations • LREC 2020 • Manuela Sanguinetti, Cristina Bosco, Lauren Cassidy, {\"O}zlem {\c{C}}etino{\u{g}}lu, Aless Cignarella, ra Teresa, Teresa Lynn, Ines Rehbein, Josef Ruppenhofer, Djam{\'e} Seddah, Amir Zeldes
The paper presents a discussion on the main linguistic phenomena of user-generated texts found in web and social media, and proposes a set of annotation guidelines for their treatment within the Universal Dependencies (UD) framework.
no code implementations • LREC 2020 • Thierry Etchegoyhen, Borja Anza Porras, Andoni Azpeitia, Eva Mart{\'\i}nez Garcia, Jos{\'e} Luis Fonseca, Patricia Fonseca, Paulo Vale, Jane Dunne, Federico Gaspari, Teresa Lynn, Helen McHugh, Andy Way, Victoria Arranz, Khalid Choukri, Herv{\'e} Pusset, Alex Sicard, re, Rui Neto, Maite Melero, David Perez, Ant{\'o}nio Branco, Ruben Branco, Lu{\'\i}s Gomes
We describe the European Language Resource Infrastructure (ELRI), a decentralised network to help collect, prepare and share language resources.
no code implementations • WS 2019 • Abigail Walsh, Teresa Lynn, Jennifer Foster
This paper describes the categorisation of Irish MWEs, and the construction of the first version of a lexicon of Irish MWEs for NLP purposes (Ilfhocail, meaning {`}Multiwords{'}), collected from a number of resources.
no code implementations • WS 2017 • Carla Parra Escart{\'\i}n, Wessel Reijers, Teresa Lynn, Joss Moorkens, Andy Way, Chao-Hong Liu
Shared tasks are increasingly common in our field, and new challenges are suggested at almost every conference and workshop.
no code implementations • COLING 2016 • Yvette Graham, Timothy Baldwin, Meghan Dowling, Maria Eskevich, Teresa Lynn, Lamia Tounsi
Human-targeted metrics provide a compromise between human evaluation of machine translation, where high inter-annotator agreement is difficult to achieve, and fully automatic metrics, such as BLEU or TER, that lack the validity of human assessment.
no code implementations • LREC 2012 • Teresa Lynn, {\"O}zlem {\c{C}}etino{\u{g}}lu, Jennifer Foster, Elaine U{\'\i} Dhonnchadha, Mark Dras, Josef van Genabith
This paper describes the early stages in the development of new language resources for Irish ― namely the first Irish dependency treebank and the first Irish statistical dependency parser.