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Machine Translation and Automated Analysis of the Sumerian Language Machine Translation and Automated Analysis of the Sumerian Language Emilie´ Page-Perron´ †, Maria Sukhareva‡, Ilya Khait¶, Christian Chiarcos‡, † University of Toronto [email protected] ‡ University of Frankfurt [email protected] [email protected] ¶ University of Leipzig [email protected] Abstract cuses on the application of NLP methods to Sume- rian, a Mesopotamian language spoken in the This paper presents a newly funded in- 3rd millennium B.C. Assyriology, the study of ternational project for machine transla- ancient Mesopotamia, has benefited from early tion and automated analysis of ancient developments in NLP in the form of projects cuneiform1 languages where NLP special- which digitally compile large amounts of tran- ists and Assyriologists collaborate to cre- scriptions and metadata, using basic rule- and ate an information retrieval system for dictionary-based methodologies.4 However, the Sumerian.2 orthographic, morphological and syntactic com- This research is conceived in response to plexities of the Mesopotamian cuneiform lan- the need to translate large numbers of ad- guages have hindered further development of au- ministrative texts that are only available in tomated treatment of the texts. Additionally, dig- transcription, in order to make them acces- ital projects do not necessarily use the same stan- sible to a wider audience. The method- dards and encoding schemes across the board, and ology includes creation of a specialized this, coupled with closed or partial access to some NLP pipeline and also the use of linguis- projects’ data, limits larger scale investigation of tic linked open data to increase access to machine-assisted text processing. the results. The history and society of ancient Mesopotamia are mostly known to the general public through 1 Context works that draw on myths and royal inscriptions as primary sources, texts which are mostly translated The project Machine Translation and Automated 3 and readily available. Among these works the Analysis of Cuneiform Languages (MTAAC) fo- Sumerian texts and their translations form a per- 1The Cuneiform script was invented in Ancient Iraq more fect testbed for distantly supervised NLP methods than 5000 years ago. Signs were drawn, and later impressed, such as annotation projection and cross-lingual onto a tablet-shaped fresh lump of clay using a reed stylus. This script was in use for 4000 years to record texts in differ- tool adaptation. However, the aforementioned ent languages such as Sumerian, Akkadian and Elamite. See translated texts make up only around 10% of the figure 1 in section 1b for an example. total amount of transcribed Sumerian data. The 2We would like to thank the reviewers, and Robert K. En- glund and Heather D. Baker, for their insightful comments majority of the Sumerian texts are administrative and suggestions. 3The project is generously funded by the Deutsche 4Among others, the Cuneiform Digital Library Forschungsgemeinschaft, the Social Sciences and Humani- initiative (CDLI) http://cdli.ucla.edu/ and the Open ties Research Council, and the National Endowment for the Richly Annotated Cuneiform Corpus (ORACC) Humanities through the T-AP Digging into Data Challenge. http://oracc.museum.upenn.edu/ are two examples of See the project website at https://cdli-gh.github.io/mtaac. such endeavors. 10 Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature. Proceedings, pages 10–16, Vancouver, BC, August 4, 2017. c 2017 Association for Computational Linguistics , and legal in nature. The manual annotation and the ORACC platform in the form of various translation of these texts is hardly possible, ow- sub-projects,6 manual annotation of large enough ing to the large volume of the data and the need training sets to train a supervised classifier is not for an extremely rare expertise in Mesopotamian possible as it demands a rare expertise and is languages. However, having a parallel corpus, time-consuming. We thus propose a pipeline that the solution to automatic processing of these texts uses distantly supervised methods (e.g. annota- lies in using machine translation (MT) techniques: tion projection) to create automatic linguistic an- Sumerian texts can be automatically translated and notation of Sumerian. Figure2 shows the work- information extraction methods can be applied to flow of the NLP module. The majority of the data the resulting translations. at hand comprises untranslated Sumerian texts. In this paper we present a newly funded interna- The distantly supervised methods will be applied tional project that will apply state-of-the-art NLP to Sumerian texts and their English translations. methods to Sumerian texts. We seek to create a The core of the pipeline is the annotation projec- pipeline for cuneiform languages with three ma- tion module that will produce morphosyntactically jor components: NLP processing, machine trans- and syntactically annotated training data for super- lation, and information extraction. The NLP tools vised NLP tools. This section will further discuss for Sumerian created in the framework of the in detail each module of the NLP pipeline. project will also be applicable to other cuneiform languages. The resource interoperability will be 3.1 Data Preprocessing achieved through linking the annotation with lin- After verifying the uniformity in the standardiza- guistic linked open data ontologies (LLOD). tion of the texts, we will convert the data to a ma- chine readable format and sign readings will be 2 Data verified against our digital syllabary. Translitera- The data for this project takes the form of unanno- tions and translation of our gold standard will be tated raw transliterations of almost 68,000 Sume- tokenized, lemmatized, and morphologically ana- rian texts of the Ur III period (21st century lyzed. The error rate of the corpus transliterations B.C.) comprising 1.5 million transliteration lines. will be calculated against the curated gold stan- Around 1600 of these texts have also been trans- dard. lated. Each text entry is augmented with a set of 3.2 Morphological analysis metadata which describes the medium of the text, its context, and some elements of internal analysis. Our morphological analyzer will be partly based These texts are restricted in style and topic, and on existing tools such as Tablan et al.(2006)’s include a large proportion of numero-metrological rule-based morphology and Liu et al.(2015)’s al- elements. They are also repetitive, brief, and for- gorithm to identify named entities. We will de- mulaic. As the inscribed medium comes in var- sign a custom parser for numero-metrological con- ied sizes and shapes, structural elements in the tent for the occasion. Since Sumerian affixes are transliterations indicate on which surface of the ambiguous, we will build on previous work on artifact the text appears. Figure1 shows an ex- the disambiguation of morphologically rich lan- ample of an ASCII transliteration and translation guages, such as Sak et al.(2007)’s neural meth- of a cuneiform text, accompanied with a picture of ods for Turkish and Rios and Mamani(2014)’s the obverse and reverse of the artifact. 5 conditional random fields used to disambiguate Quechua morphology. Morphological tags as- 3 NLP Pipeline for Sumerian signed following rule-based algorithms will be re- ranked using different machine learning (ML) ap- State-of-the-art statistical NLP widely uses su- proaches. The disambiguated morphology will be pervised classifiers to produce automatic linguis- used for syntactic parsing, MT, and information tic annotation. Although some Sumerian and extraction. We plan to develop a lemmatizer that Akkadian corpora have been annotated through will exploit a high-coverage dictionary. The avail- 5Cuneiform text of the Ur III period from the settlement able off-the-shelf lemmatizer for Sumerian7 was of Garshana, Mesopotamia (Owen, 2011, no. 851) and its transliteration as stored in the Cuneiform Digital Library Ini- 6http://oracc.museum.upenn.edu/ tiative (CDLI) database http://cdli.ucla.edu/P322539 (picture 7http://oracc.museum.upenn.edu/doc/help/languages/ reproduced here with the kind permission of David I. Owen) sumerian/sumerianprimer/ 11 (1) P322539 = CUSAS 03, 0851. tablet. obverse. 1. 1(disz) kusz udu niga 1 hide, grain-fed sheep; 2. 1(disz) kusz masz2 niga 1 hide, grain-fed goat; 3. kusz udu sa2-du11 sheep hides, regular offerings, 4. ki d iszkur-illat-ta from{ Adda-illat,} reverse. 1. a-na-ah-i3-li2 Anah-ili; 2. szu ba-an-ti did receive. 3. iti ezem-an-na Month: An-festival, 4. mu na-ru2-a-mah mu-ne-du3 Year: He erected the great stele for them. (a) ASCII transliteration and English translation (b) Example of a Sumerian source text Figure 1: Artifact and its digitization applied to our corpus during the preparation of shelf word-alignment tool Giza++ (Och and Ney, this project and it was revealed that its coverage 2003), we can produce word alignment between and accuracy are not sufficient for our needs since English and the Sumerian texts. After we auto- headwords are assigned to tokens without taking matically tag English parallel texts, the assigned into account the textual context, although part of POS will be projected onto the aligned Sumerian this software might be reused. words. The general assumption behind the anno- tation projection based on the word alignment is 3.3 POS tagging that translated words are likely to have the same An important part of the NLP pipeline is the dis- POS as the source words. It is quite clear that this tantly supervised POS Tagging. As the corpus is is a very bold assumption and there are a number currently unannotated, a supervised approach to of exceptions. Thus, both manual and automatic POS tagging would not be applicable as it de- POS correction will be needed. However, the dis- mands annotated training data. The creation of tantly supervised solution is temporary as there are such training data through manual POS annotation parallel efforts to annotate the texts manually to of the data would demand an extremely rare exper- produce training data for a supervised classifier.
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