Thailand

In this paper we present a deep learning pipeline for automatically dating ancient Greek papyrus fragments based solely on fragment images. The overall pipeline consists of several stages, including handwritten text recognition (HTR) to detect and classify characters, filtering and grouping of detected characters, 24 character-level date prediction models, and a fragment-level date prediction model that utilizes the per-character predictions. A new dataset (containing approximately 7,000 fragment images and 778,000 character images) was created by scraping papyrus databases, extracting fragment images with known dates, and running them through our HTR models to obtain labeled character images. Transfer learning was then used to fine-tune separate ResNets to predict dates for individual characters which are then used, in aggregate, to train the fragment-level date prediction model. Experiments show that even though the average accuracies of character-level dating models is low, between 35%-45%, the fragment-level model can achieve up to 79% accuracy in predicting a broad, two-century date range for fragments with many characters. We then discuss the limitations of this approach and outline future work to improve temporal resolution and further testing on additional papyri. This image-based deep learning approach has great potential to assist scholars in the palaeographical analysis and dating of ancient Greek manuscripts.

ACL 2024

A deep learning pipeline for the palaeographical dating of ancient Greek papyrus fragments

date attribution

paleography

ancient greek

handwritten text recognition

image classification

deep learning

We are pleased to announce the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), which will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February, 2024.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-24 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

We expect for AAAI-24 to be an in-person conference – one author of all accepted papers will be expected to present work in person unless there are exceptional circumstances that prevent this.

In order to access the AAAI-24 event page you need to register [here](https://aaai.org/aaai-conference/registration/)

AAAI 2024

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. 

workshop paper

### Welcome!
The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. Our Virtual Poster Sessions will take place online Thursday, August 22, 2024.

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The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

We present a novel approach to extracting recurring narrative patterns, or type-scenes, in Biblical Hebrew and Biblical Greek with an information retrieval network. We use cross-references to train an encoder model to create similar representations for verses linked by a cross-reference. We then query our trained model with phrases informed by humanities scholarship and designed to elicit particular kinds of narrative scenes. Our models can surface relevant instances in the top-10 ranked candidates in many cases. Through manual error analysis and discussion, we address the limitations and challenges inherent in our approach. Our findings contribute to the field of Biblical scholarship by offering a new perspective on narrative analysis within ancient texts, and to computational modeling of narrative with a genre-agnostic approach for pattern-finding in long, literary texts.



Detecting Narrative Patterns in Biblical Hebrew and Greek

In this paper, we present a study of Named Entity Recognition (NER) as applied to Ancient Greek texts, with an emphasis on identifying individuals. Recent research shows that, while the task remains difficult, the use of transformer models results in significant improvements. In the first part of the paper, we therefore compare the performance of four transformer models on the task of NER for the categories of people, locations and groups, and add an out-of-domain test set to the existing datasets. Results on this set highlight the shortcomings of the models when confronted with a random sample of sentences. Hence, in the second part of the paper, we narrow down our approach to the category of people, to be able to include domain knowledge. First, we simplify the task to a binary PERS/MISC classification on the token level, starting from capitalised words. Next, we test the use of domain- and linguistic knowledge to improve the results. We found that including simple gazetteer information as a binary mask has a marginally positive effect on newly annotated data and that treebanks can be used to help identify multi-word individuals if they are scarcely or inconsistently annotated in the available training data. We conclude with a qualitative error analysis that identifies further areas of improvement. 


"Gotta catch `em all!": Retrieving people in Ancient Greek texts combining transformer models and domain knowledge

This paper explores the possibility to exploit different Pretrained Language Models (PLMs) to assist in a manual annotation task consisting in assigning the appropriate sense to verbal predicates in a Latin text. Indeed, this represents a crucial step when annotating data according to the Uniform Meaning Representation (UMR) framework, designed to annotate the semantic content of a text in a cross-linguistic perspective. We approach the study as a Word Sense Disambiguation task, with the primary goal of assessing the feasibility of leveraging available resources for Latin to streamline the labor-intensive annotation process. Our methodology revolves around the exploitation of contextual embeddings to compute token similarity, under the assumption that predicates sharing a similar sense would also share their context of occurrence. We discuss our findings, emphasizing applicability and limitations of this approach in the context of Latin, for which the limited amount of available resources poses additional challenges.

Predicate Sense Disambiguation for UMR Annotation of Latin: Challenges and Insights

Beginning with the discovery of the cuneiform writing system in 1835, there have been numerous grammars published illustrating the complexities of the Sumerian language. However, the one thing they have in common is their omission of dependency rules for syntax in Sumerian linguistics. For this reason we are working toward a better understanding of Sumerian syntax, by means of dependency-grammar in the Universal Dependencies (UD) framework. Therefore, in this study we articulate the methods and engineering techniques that can address the hardships in annotating dependency relationships in the Sumerian texts in transliteration from the Electronic Text Corpora of Sumerian (ETCSUX).

UD-ETCSUX: Toward a Better Understanding of Sumerian Syntax

This paper presents a research project on the application of machine learning to the edition of ancient Greek inscriptions. More specifically, it implements Ithaca, a deep neural network architecture, for elaborating a new critical edition of the enquiries to the oracle of Zeus and Dione which are preserved on the lead tablets discovered in the sanctuary of Dodona, in northern Greece.
The goal of the project is twofold: first, it constitutes an attempt to incorporate a new technological tool into the classical epigraphist’s specialized workflow, mainly state-of-the-art machine learning; second, it is conceived as a case study for evaluating the performance of deep learning for editing a corpus of Greek inscriptions which presents a high level of complexity.

Application of Machine Learning to the Critical Edition of Ancient Greek Inscriptions: Ithaca and the Corpus of Oracular Inscriptions of Dodona

We investigate the problem of restoring Mycenaean linear B clay tablets, dating from about 1400 B.C. to roughly 1200 B.C., by using text infilling methods based on machine learning models. Our goals here are: first to try to improve the results of the methods used in the related literature by focusing on the characteristics of the Mycenaean Linear B writing system (series D), second to examine the same problem for the first time on series A&B and finally to investigate transfer learning using series D as source and the smaller series A&B as target. Our results show promising results in the supervised learning tasks, while further investigation is needed to better exploit the merits of transfer learning.

Restoring Mycenaean Linear B 'A&B' series tablets using supervised and transfer learning

Natural language processing for Greek and Latin, inflectional languages with small corpora, requires special techniques. For morphological tagging, transformer models show promising potential, but the best approach to use these models is unclear. For both languages, this paper examines the impact of using morphological lexica, training different model types (a single model with a combined feature tag, multiple models for separate features, and a multi-task model for all features), and adding linguistic constraints. We find that, although simply fine-tuning transformers to predict a monolithic tag may already yield decent results, each of these adaptations can further improve tagging accuracy.

Adapting transformer models to morphological tagging of two highly inflectional languages: a case study on Ancient Greek and Latin

Past research has modelled statistically the language of the Homeric poems, assessing the degree of surprisal for each verse through diverse metrics and resulting to the HoLM resource. In this study we utilise the HoLM resource to explore cross poem affinity at the verse level, looking at Iliadic verses and passages that are less surprising to the Odyssean model than to the Iliadic one and vice-versa. Using the same tool, we investigate verses that evoke greater surprise when assessed by a local model trained solely on their source book, compared to a global model trained on the entire source poem. Investigating deeper on the distribution of such verses across the Homeric poems we employ machine learning text classification to further analyse quantitatively cross-poem affinity in selected books.

Exploring intertextuality across the Homeric poems through language models

There is a large volume of late antique and medieval Hebrew texts. They represent a crucial linguistic and cultural bridge between Biblical and modern Hebrew. Poetry is prominent in these corpora and one of its main characteristics is the frequent use of metaphor. Distinguishing figurative and literal language use is a major task for scholars of the Humanities, especially in the fields of literature, linguistics, and hermeneutics. This paper presents a new, challenging dataset of late antique and medieval Hebrew poetry with expert annotations of metaphor, as well as some baseline results, which we hope will facilitate further research in this area.

A Dataset for Metaphor Detection in Early Medieval Hebrew Poetry

This paper presents a course on neural networks based on the Transformer architecture targeted at diverse groups of people from academia and industry with experience in Python, Machine Learning, and Deep Learning but little or no experience with Transformers. The course covers a comprehensive overview of the Transformers NLP applications and their use for other data types. The course features 15 sessions, each consisting of a lecture and a practical part, and two homework assignments organized as CodaLab competitions. The first six sessions of the course are devoted to the Transformer and the variations of this architecture (e.g., encoders, decoders, encoder-decoders) as well as different techniques of model tuning. Subsequent sessions are devoted to multilingualism, multimodality (e.g., texts and images), efficiency, event sequences, and tabular data.

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Detecting Narrative Patterns in Biblical Hebrew and Greek

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Detecting Narrative Patterns in Biblical Hebrew and Greek

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