22 research outputs found

    Generating Text from Anonymised Structures

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    International audienceSurface realisation maps a meaning representation (MR) to a text, usually a single sentence. In this paper, we introduce a new parallel dataset of deep meaning representations and French sentences and we present a novel method for MR-to-text generation which seeks to generalise by abstracting away from lexical content. Most current work on natural language generation focuses on generating text that matches a reference using BLEU as evaluation criteria. In this paper, we additionally consider the model's ability to reintroduce the function words that are absent from the deep input meaning representations. We show that our approach increases both BLEU score and the scores used to assess function words generation

    Generating Text from Anonymised Structures

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    International audienceSurface realisation maps a meaning representation (MR) to a text, usually a single sentence. In this paper, we introduce a new parallel dataset of deep meaning representations and French sentences and we present a novel method for MR-to-text generation which seeks to generalise by abstracting away from lexical content. Most current work on natural language generation focuses on generating text that matches a reference using BLEU as evaluation criteria. In this paper, we additionally consider the model's ability to reintroduce the function words that are absent from the deep input meaning representations. We show that our approach increases both BLEU score and the scores used to assess function words generation

    Corpus and Models for Lemmatisation and POS-tagging of Classical French Theatre

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    This paper describes the process of building an annotated corpus and training models for classical French literature, with a focus on theatre, and particularly comedies in verse. It was originally developed as a preliminary step to the stylometric analyses presented in Cafiero and Camps [2019]. The use of a recent lemmatiser based on neural networks and a CRF tagger allows to achieve accuracies beyond the current state-of-the art on the in-domain test, and proves to be robust during out-of-domain tests, i.e.up to 20th c.novels

    Indirectly Named Entity Recognition

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    [EN] We define here indirectly named entities, as a term to denote multiword expressions referring to known named entities by means of periphrasis.  While named entity recognition is a classical task in natural language processing, little attention has been paid to indirectly named entities and their treatment. In this paper, we try to address this gap, describing issues related to the detection and understanding of indirectly named entities in texts. We introduce a proof of concept for retrieving both lexicalised and non-lexicalised indirectly named entities in French texts. We also show example cases where this proof of concept is applied, and discuss future perspectives. We have initiated the creation of a first lexicon of 712 indirectly named entity entries that is available for future research.This research has been funded by the FEDER (Fonds europĂ©en de dĂ©veloppement rĂ©gional) and selected by the French-Swiss programme Interreg V. We would like to thank Claire Wuillemin for her preliminary work in the DecRIPT project about the State-of-the-Art in NER and SER in 2020. We would also like to thank for their advice Gilles Falquet, Luka Nerima, Eric Wehrli and Jean-Philippe Goldman at the University of Geneva.Kauffmann, A.; Rey, F.; Atanassova, I.; Gaudinat, A.; Greenfield, P.; Madinier, H.; Cardey, S. (2021). Indirectly Named Entity Recognition. Journal of Computer-Assisted Linguistic Research. 5(1):27-46. https://doi.org/10.4995/jclr.2021.15922OJS274651Abney, Steven. 1987. "The English Noun Phrase in its Sentential Aspect." PhD diss., Massachusetts Institute of Technology.Alsharaf, H., S. Cardey, P. Greenfield, D. Limame, and I. Skouratov. 2003. "Fixedness, the complexity and fragility of the phenomenon: some solutions for natural language processing." In Proceedings of ICL17. Prague, Czech Republic: Matfyzpress.Ananthanarayanan, Rema, Vijil Chenthamarakshan, Prasad M Deshpande, and Raghuram Krishnapuram. 2008. "Rule Based Synonyms for Entity Extraction from Noisy Text." In Proceedings of the Second Workshop on Analytics for Noisy Unstructured Text Data AND '08, 31-38. Singapore: Association for Computing Machinery. https://doi.org/10.1145/1390749.1390756Bachellier, Jean-Louis. 1972. "Sur-Nom." Le texte: de la thĂ©orie Ă  la recherche, no. 19: 69-92. doi :10.3406/comm.1972.1283. https://doi.org/10.3406/comm.1972.1283Baldwin, Timothy, and Su Nam Kim. 2013. "Multiword Expressions." In Handbook of Natural Language Processing, Second Edition, edited by Nitin Indurkhya and Fred J. Damerau, 267-292. Boca Raton, USA: CRCPress.Bohn, C., and Kjeti NĂžrvag. 2010. "Extracting Named Entities and Synonyms from Wikipedia." In Proceedings of the 24th IEEE International Conference on Advanced Information Networking and Applications, 1300-1307. https://doi.org/10.1109/AINA.2010.50Cai, Desheng, and Gongqing Wu. 2019. "Content-aware attributed entity embedding for synonymous named entity discovery." Neurocomputing 329: 237-247. https://doi.org/10.1016/j.neucom.2018.10.055Chakrabarti, K., S. Chaudhuri, T. Cheng, and Dong Xin. 2012. "A framework for robust discovery of entity synonyms." In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1384-1392, Beijing, China: Association for Computing Machinery. https://doi.org/10.1145/2339530.2339743Charton, Eric, Michel Gagnon, and Benoit Ozell. 2011. "GĂ©nĂ©ration automatique de motifs de dĂ©tection d'entitĂ©s nommĂ©es en utilisant des contenus encyclopĂ©diques (Automatic generation of named entity detection patterns using encyclopedic contents)" [in French]. In Actes de la 18e confĂ©rence sur le Traitement Automatique des Langues Naturelles. Articles longs, 13-24. Montpellier, France: ATALA.Cho, Hyejin, Wonjun Choi, and Hyunju Lee. 2017. "A method for named entity normalization in biomedical articles: application to diseases and plants." BMC bioinformatics 18, no. 1 ( 1-12. https://doi.org/10.1186/s12859-017-1857-8Devlin, Jacob, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding." In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 4171-4186. Minneapolis, Minnesota: Association for Computational Linguistics.Friburger, Nathalie. 2006. "Linguistique et reconnaissance automatique des noms propres." Meta 51, no. 4: 637-650. doi:10.7202/014331ar. https://doi.org/10.7202/014331arGuenoune, Hani, Kevin Cousot, Mathieu Lafourcade, Melissa Mekaoui, and CĂ©dric Lopez. 2020. "A Dataset for Anaphora Analysis in French Emails." In Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference, 165-175. Barcelona, Spain (online): Association for Computational Linguistics.Honnibal, Matthew, and Ines Montani. 2017. "spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing."Kampeera, Wannachai, and Sylviane Cardey-Greenfield. 2012. "Building a Lexically and Semantically-Rich Resource for Paraphrase Processing." In Advances in Natural Language Processing, edited by Hitoshi Isahara and Kyoko Kanzaki, 138-143. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-33983-7_14Kauffmann, Alexis. 2013. "Structural Asymmetries in Machine Translation: The case of English-Japanese". PhD diss., UniversitĂ© de GenĂšve. https://doi.org/10.13097/archive-ouverte/unige:34540.Lample, Guillaume, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer. 2016. "Neural Architectures for Named Entity Recognition." In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 260-270. San Diego, California: Association for Computational Linguistics. https://doi.org/10.18653/v1/N16-1030Lin, Bill Yuchen, Dong-Ho Lee, M. Shen, Ryan Rene Moreno, X. Huang, Prashant Shiralkar, and X. Ren. 2020. "TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition." In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 8503-8511. Online: Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.752Lopez, C., Melissa Mekaoui, K. Aubry, Jean Bort, and Philippe Garnier. 2019. "Reconnaissance d'entitĂ©s nommĂ©es itĂ©rative sur une structure en dĂ©pendances syntaxiques avec l'ontologie NERD." Revue des Nouvelles Technologies de l'Information, Extraction et Gestion des connaissances, RNTI-E-35, 81-92.Ma, Jie, Jun Liu, Y. Li, X. Hu, Yudai Pan, S. Sun, and Qika Lin. 2020. "Jointly Optimized Neural Coreference Resolution with Mutual Attention." In Proceedings of the 13th International Conference on Web Search and Data Mining. Houston, Texas, USA: Association for Computing Machinery. https://doi.org/10.1145/3336191.3371787Manning, Christopher D., Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J. Bethard, and David McClosky. 2014. The Stanford CoreNLP Natural Language Processing Toolkit In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55-60. Baltimore, Maryland: Association for Computational Linguistics. https://doi.org/10.3115/v1/P14-5010Martin, Louis, Benjamin Muller, Pedro Javier Ortiz Suarez, Yoann Dupont, Laurent Romary, Eric Villemonte de la Clergerie, Benoıt Sagot, and DjamĂ© Seddah. 2020. "Les modĂšles de langue contextuels CamemBERT pour le français: impact de la taille et de l'hĂ©tĂ©rogĂ©nĂ©itĂ© des donnĂ©es d'entrainement (CamemBERT Contextual Language Models for French: Impact of Training Data Size and Heterogeneity)" [in French]. In Actes de la 6e confĂ©rence conjointe JournĂ©es d'Etudes sur la Parole (JEP, 33e Ă©dition), Traitement Automatique des Langues Naturelles (TALN, 27e Ă©dition), Rencontre des Etudiants Chercheurs en Informatique pour le' Traitement Automatique des Langues (RECITAL, 22e Ă©dition). Volume 2: Traitement Automatique des Langues Naturelles, 54-65. Nancy, France: ATALA et AFCP.Mitkov, Ruslan. 2014. Anaphora resolution. Routledge. https://doi.org/10.4324/9781315840086Mohamed, Muhidin A., and Mourad Chabane Oussalah. 2020. "A hybrid approach for paraphrase identification based on knowledge-enriched semantic heuristics." Language Resources and Evaluation 54 : 457-485. https://doi.org/10.1007/s10579-019-09466-4Nadeau, David, and Satoshi Sekine. 2007. "A survey of named entity recognition and classification." Lingvisticae Investigationes 30: 3-26. https://doi.org/10.1075/li.30.1.03nadNayel, Hamada A., H. L. Shashirekha, Hiroyuki Shindo, and Yuji Matsumoto. 2019. "Improving Multi-Word Entity Recognition for Biomedical Texts." CoRRabs/1908.05691. arXiv:1908.05691.Nebhi, Kamel. 2013. "Named Entity Disambiguation using Freebase and Syntactic Parsing." In [email protected], Damien, Maud Ehrmann, and Sophie Rosset. 2016. "Evaluating Named Entity Recognition." Chap. 6 in Named Entities for Computational Linguistics, 111-129. John Wiley & Sons, Ltd. https://doi.org/10.1002/9781119268567.ch6Ortiz Suarez, Pedro Javier, Yoann Dupont, Benjamin Muller, Laurent Romary, and Benoıt Sagot. 2020. "Establishing a New State-of-the-Art for French Named Entity Recognition" [in English]. In Proceedings of the 12th Language Resources and Evaluation Conference, 4631-4638. Marseille, France: European Language Resources Association.Petit, GĂ©rard. 2006. "Le nom de marque dĂ©posĂ©e : nom propre, nom commun et terme." Meta 51, no. 4: 690-705. doi:10.7202/014335ar. https://doi.org/10.7202/014335arQu, Meng, Xiang Ren, and Jiawei Han. 2017. "Automatic Synonym Discovery with Knowledge Bases." In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 997-1005. KDD '17. Halifax, NS, Canada: Association for Computing Machinery. https://doi.org/10.1145/3097983.3098185Racicot, AndrĂ©. 2009. "Traduire le monde: Venise du Nord et autres surnoms." L'ActualitĂ© langagiĂšre, vol. 6, n° 2, 23. Travaux publics et Services gouvernementaux Canada.Rey, François-Claude, and Kauffmann Alexis. 2021. "French indirectly named entities (version 1.3) [Data set]." Zenodo. https://doi.org/10.5281/zenodo.5158253.Rosales-MĂ©ndez, Henry, Aidan Hogan, and Barbara Poblete. 2019. "Fine-Grained Evaluation for Entity Linking." In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 718-727. Hong Kong, China: Association for Computational Linguistics. https://doi.org/10.18653/v1/D19-1066Sales, Juliano Efson, AndrĂ© Freitas, Brian Davis, and Siegfried Handschuh. 2016. "A Compositional-Distributional Semantic Model for Searching Complex Entity Categories." In Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics, 199-208. Berlin, Germany: Association for Computational Linguistics. https://doi.org/10.18653/v1/S16-2025Schmitt, X., S. Kubler, J. Robert, M. Papadakis, and Y. LeTraon. 2019. "A Replicable Comparison Study of NER Software: StanfordNLP, NLTK, OpenNLP, SpaCy, Gate." In Proceedings of the Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS), 338-343. https://doi.org/10.1109/SNAMS.2019.8931850Shang, Jingbo, Liyuan Liu, Xiaotao Gu, Xiang Ren, Teng Ren, and Jiawei Han. 2018. "Learning Named Entity Tagger using Domain-Specific Dictionary." In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 2054-2064. Brussels, Belgium: Association for Computational Linguistics. https://doi.org/10.18653/v1/D18-1230Shen, Jiaming, Ruiliang Lyu, Xiang Ren, Michelle Vanni, Brian Sadler, and Jiawei Han. 2019. "Mining entity synonyms with efficient neural set generation." In Proceedings of the AAAI Conference on Artificial Intelligence, 33:249-256. doi:10.1609/aaai.v33i01.3301249. https://doi.org/10.1609/aaai.v33i01.3301249Shinyama, Yusuke, Satoshi Sekine, and Kiyoshi Sudo. 2002. "Automatic Paraphrase Acquisition from News Articles." In Proceedings of the Second International Conference on Human Language Technology Research, 313-318. HLT '02. San Diego, California: Morgan Kaufmann Publishers Inc. https://doi.org/10.3115/1289189.1289218Sjöblom, Paula. 2016. "Commercial names." Chap. V.31 in The Oxford Handbook of Names and Naming, edited by Carole Hough, 453-464. Oxford, UK: Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199656431.013.56Tenney, Ian, Dipanjan Das, and Ellie Pavlick. 2019. "BERT Rediscovers the Classical NLP Pipeline." In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 4593-4601. Florence, Italy: Association for Computational Linguistics. https://doi.org/10.18653/v1/P19-1452Treps, Marie. 2012. La rançon de la gloire - Les surnoms de nos politiques. Paris, France: Editions du Seuil.Watanabe, Taiki, Akihiro Tamura, Takashi Ninomiya, Takuya Makino, and Tomoya Iwakura. 2019. "Multi-Task Learning for Chemical Named Entity Recognition with Chemical Compound Paraphrasing." In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 6244-6249. Hong Kong, China: Association for Computational Linguistics. https://doi.org/10.18653/v1/D19-1648Wehrli, Eric, and Luka Nerima. 2018. "Anaphora resolution, collocations and translation." In Multiword units in machine translation and translation technology, edited by Johanna Monti, Violeta Seretan, Gloria Corpas Pastor, and Ruslan Mitkov, 244-256. John Benjamins. https://doi.org/10.1075/cilt.341.12wehWehrli, Eric, Violeta Seretan, and Luka Nerima. 2010. "Sentence Analysis and Collocation Identification." In Proceedings of the 2010 Workshop on Multiword Expressions: from Theory to Applications, 28-36. Beijing, China: Coling 2010 Organizing Committee.Weston, L., V. Tshitoyan, J. Dagdelen, O. Kononova, A. 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    When Collaborative Treebank Curation Meets Graph Grammars: Arborator With a Grew Back-End

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    International audienceIn this paper we present Arborator-Grew, a collaborative annotation tool for treebank development. Arborator-Grew combines the features of two preexisting tools: Arborator and Grew. Arborator is a widely used collaborative graphical online dependency treebank annotation tool. Grew is a tool for graph querying and rewriting specialized in structures needed in NLP, i.e. syntactic and semantic dependency trees and graphs. Grew also has an online version, Grew-match, where all Universal Dependencies treebanks in their classical, deep and surface-syntactic flavors can be queried. Arborator-Grew is a complete redevelopment and modernization of Arborator, replacing its own internal database storage by a new Grew API, which adds a powerful query tool to Arborator's existing treebank creation and correction features. This includes complex access control for parallel expert and crowd-sourced annotation, tree comparison visualization, and various exercise modes for teaching and training of annotators. Arborator-Grew opens up new paths of collectively creating, updating, maintaining, and curating syntactic treebanks and semantic graph banks

    Computer vision methods for unconstrained gesture recognition in the context of sign language annotation

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    Cette thĂšse porte sur l'Ă©tude des mĂ©thodes de vision par ordinateur pour la reconnaissance de gestes naturels dans le contexte de l'annotation de la Langue des Signes. La langue des signes (LS) est une langue gestuelle dĂ©veloppĂ©e par les sourds pour communiquer. Un Ă©noncĂ© en LS consiste en une sĂ©quence de signes rĂ©alisĂ©s par les mains, accompagnĂ©s d'expressions du visage et de mouvements du haut du corps, permettant de transmettre des informations en parallĂšles dans le discours. MĂȘme si les signes sont dĂ©finis dans des dictionnaires, on trouve une trĂšs grande variabilitĂ© liĂ©e au contexte lors de leur rĂ©alisation. De plus, les signes sont souvent sĂ©parĂ©s par des mouvements de co-articulation. Cette extrĂȘme variabilitĂ© et l'effet de co-articulation reprĂ©sentent un problĂšme important dans les recherches en traitement automatique de la LS. Il est donc nĂ©cessaire d'avoir de nombreuses vidĂ©os annotĂ©es en LS, si l'on veut Ă©tudier cette langue et utiliser des mĂ©thodes d'apprentissage automatique. Les annotations de vidĂ©o en LS sont rĂ©alisĂ©es manuellement par des linguistes ou experts en LS, ce qui est source d'erreur, non reproductible et extrĂȘmement chronophage. De plus, la qualitĂ© des annotations dĂ©pend des connaissances en LS de l'annotateur. L'association de l'expertise de l'annotateur aux traitements automatiques facilite cette tĂąche et reprĂ©sente un gain de temps et de robustesse. Le but de nos recherches est d'Ă©tudier des mĂ©thodes de traitement d'images afin d'assister l'annotation des corpus vidĂ©o: suivi des composantes corporelles, segmentation des mains, segmentation temporelle, reconnaissance de gloses. Au cours de cette thĂšse nous avons Ă©tudiĂ© un ensemble de mĂ©thodes permettant de rĂ©aliser l'annotation en glose. Dans un premier temps, nous cherchons Ă  dĂ©tecter les limites de dĂ©but et fin de signe. Cette mĂ©thode d'annotation nĂ©cessite plusieurs traitements de bas niveau afin de segmenter les signes et d'extraire les caractĂ©ristiques de mouvement et de forme de la main. D'abord nous proposons une mĂ©thode de suivi des composantes corporelles robuste aux occultations basĂ©e sur le filtrage particulaire. Ensuite, un algorithme de segmentation des mains est dĂ©veloppĂ© afin d'extraire la rĂ©gion des mains mĂȘme quand elles se trouvent devant le visage. Puis, les caractĂ©ristiques de mouvement sont utilisĂ©es pour rĂ©aliser une premiĂšre segmentation temporelle des signes qui est par la suite amĂ©liorĂ©e grĂące Ă  l'utilisation de caractĂ©ristiques de forme. En effet celles-ci permettent de supprimer les limites de segmentation dĂ©tectĂ©es en milieu des signes. Une fois les signes segmentĂ©s, on procĂšde Ă  l'extraction de caractĂ©ristiques visuelles pour leur reconnaissance en termes de gloses Ă  l'aide de modĂšles phonologiques. Nous avons Ă©valuĂ© nos algorithmes Ă  l'aide de corpus internationaux, afin de montrer leur avantages et limitations. L'Ă©valuation montre la robustesse de nos mĂ©thodes par rapport Ă  la dynamique et le grand nombre d'occultations entre les diffĂ©rents membres. L'annotation rĂ©sultante est indĂ©pendante de l'annotateur et reprĂ©sente un gain de robustese important.This PhD thesis concerns the study of computer vision methods for the automatic recognition of unconstrained gestures in the context of sign language annotation. Sign Language (SL) is a visual-gestural language developed by deaf communities. Continuous SL consists on a sequence of signs performed one after another involving manual and non-manual features conveying simultaneous information. Even though standard signs are defined in dictionaries, we find a huge variability caused by the context-dependency of signs. In addition signs are often linked by movement epenthesis which consists on the meaningless gesture between signs. The huge variability and the co-articulation effect represent a challenging problem during automatic SL processing. It is necessary to have numerous annotated video corpus in order to train statistical machine translators and study this language. Generally the annotation of SL video corpus is manually performed by linguists or computer scientists experienced in SL. However manual annotation is error-prone, unreproducible and time consuming. In addition de quality of the results depends on the SL annotators knowledge. Associating annotator knowledge to image processing techniques facilitates the annotation task increasing robustness and speeding up the required time. The goal of this research concerns on the study and development of image processing technique in order to assist the annotation of SL video corpus: body tracking, hand segmentation, temporal segmentation, gloss recognition. Along this PhD thesis we address the problem of gloss annotation of SL video corpus. First of all we intend to detect the limits corresponding to the beginning and end of a sign. This annotation method requires several low level approaches for performing temporal segmentation and for extracting motion and hand shape features. First we propose a particle filter based approach for robustly tracking hand and face robust to occlusions. Then a segmentation method for extracting hand when it is in front of the face has been developed. Motion is used for segmenting signs and later hand shape is used to improve the results. Indeed hand shape allows to delete limits detected in the middle of a sign. Once signs have been segmented we proceed to the gloss recognition using lexical description of signs. We have evaluated our algorithms using international corpus, in order to show their advantages and limitations. The evaluation has shown the robustness of the proposed methods with respect to high dynamics and numerous occlusions between body parts. Resulting annotation is independent on the annotator and represents a gain on annotation consistency

    An investigation of English-Irish machine translation and associated resources

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    As an official language in both Ireland and the European Union (EU), there is a high demand for English-Irish (EN-GA) translation in public administration. The difficulty that translators currently face in meeting this demand leads to the need for reliable domain-specific user-driven EN-GA machine translation (MT). This landscape provides a timely opportunity to address some research questions surrounding MT for the EN-GA language pair. To this end, we assess the corpora available for training data-driven MT systems, including publicly-available data, data collected through EU-supported data collection efforts and web-crawling, showing that though Irish is a low-resource language it is possible to increase the corpora available through concerted data collection efforts. We investigate how increased corpora affect domain-specific (public administration) statistical MT (SMT) and neural MT (NMT) systems using automatic metrics. The effect that different SMT and NMT parameters have on these automatic values is also explored, using sentence-level metrics to identify specific areas where output differs greatly between MT systems and providing a linguistic analysis of each. With EN-GA SMT and NMT automatic evaluation scores showing inconclusive results, we investigate the usefulness of EN-GA hybrid MT through the use of monolingual data as a source of artificial data creation via backtranslation. We evaluate these results using automatic metrics and linguistic analysis. Although results indicate that the addition of artificial data did not have a positive impact on EN-GA MT, repeated experiments involving Scottish Gaelic show that the method holds promise, given suitable conditions. Finally, given that the intended use-case of EN-GA MT is in the workflow of a professional translator, we conduct an in-depth human evaluation study for EN-GA SMT and NMT, providing a human-derived assessment of EN-GA MT quality and comparison of EN-GA SMT and NMT. We include a survey of translator opinions and recommendations surrounding EN-GA SMT and NMT as well as an analysis of data gathered through the post-editing of MT output. We compare these results to those generated automatically and provide recommendations for future work on EN-GA MT, in particular with regards to its use in a professional translation workflow within public administration
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