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Translation and Text Alignment
Gabriel Bodard edited this page Nov 18, 2021
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Thursday Nov 18, 17:15–18:45 CET
Convenors: Chiara Palladino (Furman), Farnoosh Shamsian (Leipzig), Maia Shukhoshvili (Tbilisi)
Youtube link: https://youtu.be/r-E9bB0laKk
Slides: tba
In this session, we will present Ugarit, a web-based tool for text and translation alignment, and provide an overview of its applications. First, we will introduce the notion of text and translation alignment, its foundations and methodologies across Humanities and Computer Science. Second, we will present Ugarit and give an overview of its main features. In the rest of the session, we will illustrate various applications and research projects that have used translation alignment or aligned data in Ugarit:
- translation alignment for scholarly analysis of multilingual sources;
- alignment to compare originals and translations in various languages;
- translation alignment in teaching and pedagogy;
- analysis on user behavior based on expertise and language background;
- big data approaches and automated translation alignment.
In the last part of the session, we will also offer a demo of Ugarit.
- C. Palladino, M. Foradi, T. Yousef, Translation alignment for historical language learning: a case study. DHQ 15.3 (2021). Available: http://www.digitalhumanities.org/dhq/vol/15/3/000563/000563.html.
- Tamara Pataridze & Bastien Kindt (2018). "Text Alignment in Ancient Greek and Georgian: A Case-Study on the First Homily of Gregory of Nazianzus." Journal of Data Mining and Digital Humanities. Available: https://jdmdh.episciences.org/4182/pdf
- Gregory Crane (2019), "Beyond Translation: Language Hacking and Philology." Harvard Data Science Review 1.2. Available: https://doi.org/10.1162/99608f92.282ad764
- Anise Ferreira, Christopher Blackwell, and Chiara Palladino, “Edições digitais nas Clássicas: elementos de gênero na produção e leitura da língua grega e do latim,” Linha D’Água 33, no. 2 (July 24, 2020): 113–35. Available: https://doi.org/10.11606/issn.2236-4242.v33i2p113-135
- Dimitar Iliev, “Laptops in the Auditorium: Facing Educational Challenges in Classics by Teaching Digital Tools,” Digital Presentation and Preservation of Cultural and Scientific Heritage, no. X (2020): 65–78. Available: https://dipp.math.bas.bg/images/2020/065-078_1.4_iDiPP2020-11_v.1c.pdf.
- Philipp Koehn (2009). Statistical Machine Translation, Chapter 4: “Word-Based Models.” Cambridge University Press
- C. Palladino, Reading Texts in Digital Environments: Applications of Translation Alignment for Classical Language Learning. Journal of Interactive Technology and Pedagogy 18 (2020). Available: https://jitp.commons.gc.cuny.edu/reading-texts-in-digital-environments-applications-of-translation-alignment-for-classical-language-learning/
- Despoina Panou (2013). “Equivalence in Translation Theories: A Critical Evaluation, Theory and Practice.” Language Studies 3.1, pp. 1-6. Available: http://www.academypublication.com/issues/past/tpls/vol03/01/01.pdf
- Raquel de Pedro (1999). The Translatability of Texts: A Historical Overview. Meta, XLIV, 4, 1999. Available: http://www3.uji.es/~aferna/EA0921/4a-Translatability.pdf
- Maia Shukhoskvili, “Methodology of Translation Alignment of Georgian Text of Plato’s ‘Theaetetus,’” International Journal of Language and Linguistics 4, no. 4 (December 2017): 63–69. Available: http://ijllnet.com/journals/Vol_4_No_4_December_2017/8.pdf.
- Tariq Yousef (2019), "Ugarit: Translation Alignment Visualization". LEVIA’19: Leipzig Symposium on Visualization in Applications 2019. Leipzig. Available: https://osf.io/thsp5.
- Jam, pedram (2019), Dastgerd and Daskara, Nāmeye Farhangestān: Farhangnevisi (15), 76-115. (in Persian)
- Ugarit: http://ugarit.ialigner.com/
- Alpheios: https://alpheios.net/
- Parthian resources: https://parthiansources.com/
- http://www.ugarit.ialigner.com/text.php?id=28776
- http://www.ugarit.ialigner.com/text.php?id=28777
- http://www.ugarit.ialigner.com/text.php?id=28778
- http://www.ugarit.ialigner.com/text.php?id=28503
- http://www.ugarit.ialigner.com/text.php?id=28504
- http://www.ugarit.ialigner.com/text.php?id=28502
- In language classrooms: Tufts University, Furman University, UNESP Brazil Araraquara, University of the Sciences Philadelphia, Dickinson College
- Digital Rosetta Stone: https://rosetta-stone.dh.uni-leipzig.de/rs/the-digital-rosetta-stone/
- Digital Fragmenta Historicorum Graecorum: http://www.dfhg-project.org/
- Digital Agathemerus: http://digitalagathemerus.org/
- Dynamic Lexicon: http://dynamiclexicon.com/
- Master Thesis: Elias D. Eells, “Multilevel Alignment of Iliadic Texts” (Tufts University, 2021), https://www.proquest.com/openview/b492730369975b12b6adcde592d63f28/1?pq-origsite=gscholar&cbl=18750&diss=y
- Option 1
Take a text in any language you want, and align it against one or more translations in Ugarit using bilingual alignment. How consistent can you be in aligning the same types of words across the two languages? After the first attempt, write down guidelines to establish how linguistic and syntactic patterns in the source language should be aligned to the target language. Do another alignment (on the same text or another) applying those guidelines as closely as possible.
Do you achieve more consistency? Where was it difficult to respect the guidelines? Where was there space for choice or contradiction?
- Option 2
For Computer Scientists and Coders: Use the data provided from these alignments of the Iliad Book 1 in Greek-Persian (http://www.ugarit.ialigner.com/text.php?id=28504, http://www.ugarit.ialigner.com/text.php?id=28502, http://www.ugarit.ialigner.com/text.php?id=28503). Extract the aligned pairs in tabular or XML format. Measure the intersection across the three Persian translations: where do the three translators overlap? Where do they differ? You can also try and measure various rates of alignment on each pair: 1-1, 1-N, N-1, N-N.