pwc-archive/papers-with-abstracts huggingface dataset:
From Inscription to Semi-automatic Annotation of Maya Hieroglyphic Texts
Authors: Cristina Vertan, Christian Prager
Date: 2022-06-01 00:00:00
Proceeding: LT4HALA (LREC) 2022 6
Tasks: Decipherment, Transliteration

The Maya script is the only readable autochthonous writing system of the Americas and consists of more than 1000 word signs and syllables. It is only partially deciphered and is the subject of the project “Text Database and Dictionary of the Classic Maya” . Texts are recorded in TEI XML and on the basis of a digital sign and graph catalog, which are stored in the TextGrid virtual repository. Due to the state of decipherment, it is not possible to record hieroglyphic texts directly in phonemically transliterated values. The texts are therefore documented numerically using numeric sign codes based on Eric Thompson’s catalog of the Maya script. The workflow for converting numerical transliteration into textual form involves several steps, with variable solutions possible at each step. For this purpose, the authors have developed ALMAH “Annotator for the Linguistic Analysis of Maya Hieroglyphs”. The tool is a client application and allows semi-automatic generation of phonemic transliteration from numerical transliteration and enables multi-step linguistic annotation. Alternative readings can be entered, and two or more decipherment proposals can be processed in parallel. ALMAH is implemented in JAVA, is based on a graph-data model, and has a user-friendly interface.
Multilingual Named Entity Recognition for Medieval Charters Using Stacked Embeddings and Bert-based Models.
Authors: Sergio Torres Aguilar
Date: 2022-06-01 00:00:00
Proceeding: LT4HALA (LREC) 2022 6
Tasks: HTR, Multilingual Named Entity Recognition, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Optical Character Recognition (OCR)

In recent years the availability of medieval charter texts has increased thanks to advances in OCR and HTR techniques. But the lack of models that automatically structure the textual output continues to hinder the extraction of large-scale lectures from these historical sources that are among the most important for medieval studies. This paper presents the process of annotating and modelling a corpus to automatically detect named entities in medieval charters in Latin, French and Spanish and address the problem of multilingual writing practices in the Late Middle Ages. It introduces a new annotated multilingual corpus and presents a training pipeline using two approaches: (1) a method using contextual and static embeddings coupled to a Bi-LSTM-CRF classifier; (2) a fine-tuning method using the pre-trained multilingual BERT and RoBERTa models. The experiments described here are based on a corpus encompassing about 2.3M words (7576 charters) coming from five charter collections ranging from the 10th to the 15th centuries. The evaluation proves that both multilingual classifiers based on general purpose models and those specifically designed achieve high-performance results and do not show performance drop compared to their monolingual counterparts. This paper describes the corpus and the annotation guideline, and discusses the issues related to the linguistic of the charters, the multilingual writing practices, so as to interpret the results within a larger historical perspective.
Linguistic Annotation of Neo-Latin Mathematical Texts: A Pilot-Study to Improve the Automatic Parsing of the Archimedes Latinus
Authors: Margherita Fantoli, Miryam de Lhoneux
Date: 2022-06-01 00:00:00
Proceeding: LT4HALA (LREC) 2022 6
Tasks: Translation

This paper describes the process of syntactically parsing the Latin translation by Jacopo da San Cassiano of the Greek mathematical work The Spirals of Archimedes. The Universal Dependencies formalism is adopted. First, we introduce the historical and linguistic importance of Jacopo da San Cassiano’s translation. Subsequently, we describe the deep Biaffine parser used for this pilot study. In particular, we motivate the choice of using the technique of treebank embeddings in light of the characteristics of mathematical texts. The paper then details the process of creation of training and test data, by highlighting the most compelling linguistic features of the text and the choices implemented in the current version of the treebank. Finally, the results of the parsing are discussed in comparison to a baseline and the most prominent errors are discussed. Overall, the paper shows the added value of creating specific training data, and of using targeted strategies (as treebank embeddings) to exploit existing annotated corpora while preserving the features of one specific text when performing syntactic parsing.
The First International Ancient Chinese Word Segmentation and POS Tagging Bakeoff: Overview of the EvaHan 2022 Evaluation Campaign
Authors: Bin Li, Yiguo Yuan, Jingya Lu, Minxuan Feng, Chao Xu, Weiguang Qu, Dongbo Wang
Date: 2022-06-01 00:00:00
Proceeding: LT4HALA (LREC) 2022 6
Tasks: Chinese Word Segmentation, POS, POS Tagging, Segmentation

This paper presents the results of the First Ancient Chinese Word Segmentation and POS Tagging Bakeoff (EvaHan), which was held at the Second Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA) 2022, in the context of the 13th Edition of the Language Resources and Evaluation Conference (LREC 2022). We give the motivation for having an international shared contest, as well as the data and tracks. The contest is consisted of two modalities, closed and open. In the closed modality, the participants are only allowed to use the training data, obtained the highest F1 score of 96.03% and 92.05% in word segmentation and POS tagging. In the open modality, the participants can use whatever resource they have, with the highest F1 score of 96.34% and 92.56% in word segmentation and POS tagging. The scores on the blind test dataset decrease around 3 points, which shows that the out-of-vocabulary words still are the bottleneck for lexical analyzers.
Automatic Word Segmentation and Part-of-Speech Tagging of Ancient Chinese Based on BERT Model
Authors: Yu Chang, Peng Zhu, Chaoping Wang, Chaofan Wang
Date: 2022-06-01 00:00:00
Proceeding: LT4HALA (LREC) 2022 6
Tasks: Language Modeling, Language Modelling, Machine Reading Comprehension, Part-Of-Speech Tagging, POS, POS Tagging, Reading Comprehension, Segmentation

In recent years, new deep learning methods and pre-training language models have been emerging in the field of natural language processing (NLP). These methods and models can greatly improve the accuracy of automatic word segmentation and part-of-speech tagging in the field of ancient Chinese research. In these models, the BERT model has made amazing achievements in the top-level test of machine reading comprehension SQuAD-1.1. In addition, it also showed better results than other models in 11 different NLP tests. In this paper, SIKU-RoBERTa pre-training language model based on the high-quality full-text corpus of SiKuQuanShu have been adopted, and part corpus of ZuoZhuan that has been word segmented and part-of-speech tagged is used as training sets to build a deep network model based on BERT for word segmentation and POS tagging experiments. In addition, we also use other classical NLP network models for comparative experiments. The results show that using SIKU-RoBERTa pre-training language model, the overall prediction accuracy of word segmentation and part-of-speech tagging of this model can reach 93.87% and 88.97%, with excellent overall performance.
Ancient Chinese Word Segmentation and Part-of-Speech Tagging Using Data Augmentation
Authors: Yanzhi Tian, Yuhang Guo
Date: 2022-06-01 00:00:00
Proceeding: LT4HALA (LREC) 2022 6
Tasks: Chinese Word Segmentation, Data Augmentation, Part-Of-Speech Tagging, POS, POS Tagging, Segmentation

We attended the EvaHan2022 ancient Chinese word segmentation and Part-of-Speech (POS) tagging evaluation. We regard the Chinese word segmentation and POS tagging as sequence tagging tasks. Our system is based on a BERT-BiLSTM-CRF model which is trained on the data provided by the EvaHan2022 evaluation. Besides, we also employ data augmentation techniques to enhance the performance of our model. On the Test A and Test B of the evaluation, the F1 scores of our system achieve 94.73% and 90.93% for the word segmentation, 89.19% and 83.48% for the POS tagging.
BERT 4EVER@EvaHan 2022: Ancient Chinese Word Segmentation and Part-of-Speech Tagging Based on Adversarial Learning and Continual Pre-training
Authors: Hailin Zhang, Ziyu Yang, Yingwen Fu, Ruoyao Ding
Date: 2022-06-01 00:00:00
Proceeding: LT4HALA (LREC) 2022 6
Tasks: Chinese Word Segmentation, Cultural Vocal Bursts Intensity Prediction, Ensemble Learning, Part-Of-Speech Tagging, POS, POS Tagging, Segmentation

With the development of artificial intelligence (AI) and digital humanities, ancient Chinese resources and language technology have also developed and grown, which have become an increasingly important part to the study of historiography and traditional Chinese culture. In order to promote the research on automatic analysis technology of ancient Chinese, we conduct various experiments on ancient Chinese word segmentation and part-of-speech (POS) tagging tasks for the EvaHan 2022 shared task. We model the word segmentation and POS tagging tasks jointly as a sequence tagging problem. In addition, we perform a series of training strategies based on the provided ancient Chinese pre-trained model to enhance the model performance. Concretely, we employ several augmentation strategies, including continual pre-training, adversarial training, and ensemble learning to alleviate the limited amount of training data and the imbalance between POS labels. Extensive experiments demonstrate that our proposed models achieve considerable performance on ancient Chinese word segmentation and POS tagging tasks. Keywords: ancient Chinese, word segmentation, part-of-speech tagging, adversarial learning, continuing pre-training
Construction of Segmentation and Part of Speech Annotation Model in Ancient Chinese
Authors: Longjie Jiang, Qinyu C. Chang, Huyin H. Xie, Zhuying Z. Xia
Date: 2022-06-01 00:00:00
Proceeding: LT4HALA (LREC) 2022 6
Tasks: Cultural Vocal Bursts Intensity Prediction, Part-Of-Speech Tagging

Among the four civilizations in the world with the longest history, only Chinese civilization has been inherited and never interrupted for 5000 years. An important factor is that the Chinese nation has the fine tradition of sorting out classics. Recording history with words, inheriting culture through continuous collation of indigenous accounts, and maintaining the spread of Chinese civilization. In this competition, the siku-roberta model was introduced into the part-of-speech tagging task of ancient Chinese by using the Zuozhuan data set, and good prediction results were obtained.
Simple Tagging System with RoBERTa for Ancient Chinese
Authors: Binghao Tang, Boda Lin, Si Li
Date: 2022-06-01 00:00:00
Proceeding: LT4HALA (LREC) 2022 6
Tasks: Language Modeling, Language Modelling, Part-Of-Speech Tagging

This paper describes the system submitted for the EvaHan 2022 Shared Task on word segmentation and part-of-speech tagging for Ancient Chinese. Our system is based on the pre-trained language model SIKU-RoBERTa and the simple tagging layers. Our system significantly outperforms the official baselines in the released test sets and shows the effectiveness.
Data Augmentation for Low-resource Word Segmentation and POS Tagging of Ancient Chinese Texts
Authors: Yutong Shen, Jiahuan Li, ShuJian Huang, Yi Zhou, Xiaopeng Xie, Qinxin Zhao
Date: 2022-06-01 00:00:00
Proceeding: LT4HALA (LREC) 2022 6
Tasks: Data Augmentation, Language Modeling, Language Modelling, Part-Of-Speech Tagging, POS, POS Tagging

Automatic word segmentation and part-of-speech tagging of ancient books can help relevant researchers to study ancient texts. In recent years, pre-trained language models have achieved significant improvements on text processing tasks. SikuRoberta is a pre-trained language model specially designed for automatic analysis of ancient Chinese texts. Although SikuRoberta significantly boosts performance on WSG and POS tasks on ancient Chinese texts, the lack of labeled data still limits the performance of the model. In this paper, to alleviate the problem of insufficient training data, We define hybrid tags to integrate WSG and POS tasks and design Roberta-CRF model to predict tags for each Chinese characters. Moreover, We generate synthetic labeled data based on the LSTM language model. To further mine knowledge in SikuRoberta, we generate the synthetic unlabeled data based on the Masked LM. Experiments show that the performance of the model is improved with the synthetic data, indicating that the effectiveness of the data augmentation methods.
A Joint Framework for Ancient Chinese WS and POS Tagging Based on Adversarial Ensemble Learning
Authors: Shuxun Yang
Date: 2022-06-01 00:00:00
Proceeding: LT4HALA (LREC) 2022 6
Tasks: Chinese Word Segmentation, Cultural Vocal Bursts Intensity Prediction, Ensemble Learning, Part-Of-Speech Tagging, POS, POS Tagging, Segmentation

Ancient Chinese word segmentation and part-of-speech tagging tasks are crucial to facilitate the study of ancient Chinese and the dissemination of traditional Chinese culture. Current methods face problems such as lack of large-scale labeled data, individual task error propagation, and lack of robustness and generalization of models. Therefore, we propose a joint framework for ancient Chinese WS and POS tagging based on adversarial ensemble learning, called AENet. On the basis of pre-training and fine-tuning, AENet uses a joint tagging approach of WS and POS tagging and treats it as a joint sequence tagging task. Meanwhile, AENet incorporates adversarial training and ensemble learning, which effectively improves the model recognition efficiency while enhancing the robustness and generalization of the model. Our experiments demonstrate that AENet improves the F1 score of word segmentation by 4.48% and the score of part-of-speech tagging by 2.29% on test dataset compared with the baseline, which shows high performance and strong generalization.
Glyph Features Matter: A Multimodal Solution for EvaHan in LT4HALA2022
Authors: Wei Xinyuan, Liu Weihao, Qing Zong, Zhang Shaoqing, Baotian Hu
Date: 2022-06-01 00:00:00
Proceeding: LT4HALA (LREC) 2022 6
Tasks: Data Augmentation, Part-Of-Speech Tagging

We participate in the LT4HALA2022 shared task EvaHan. This task has two subtasks. Subtask 1 is word segmentation, and subtask 2 is part-of-speech tagging. Each subtask consists of two tracks, a close track that can only use the data and models provided by the organizer, and an open track without restrictions. We employ three pre-trained models, two of which are open-source pre-trained models for ancient Chinese (Siku-Roberta and roberta-classical-chinese), and one is our pre-trained GlyphBERT combined with glyph features. Our methods include data augmentation, data pre-processing, model pretraining, downstream fine-tuning, k-fold cross validation and model ensemble. We achieve competitive P, R, and F1 scores on both our own validation set and the final public test set.
Overview of the EvaLatin 2022 Evaluation Campaign
Authors: Rachele Sprugnoli, Marco Passarotti, Flavio Massimiliano Cecchini, Margherita Fantoli, Giovanni Moretti
Date: 2022-06-01 00:00:00
Proceeding: LT4HALA (LREC) 2022 6
Tasks: Lemmatization, Part-Of-Speech Tagging

This paper describes the organization and the results of the second edition of EvaLatin, the campaign for the evaluation of Natural Language Processing tools for Latin. The three shared tasks proposed in EvaLatin 2022, i.,e.,Lemmatization, Part-of-Speech Tagging and Features Identification, are aimed to foster research in the field of language technologies for Classical languages. The shared dataset consists of texts mainly taken from the LASLA corpus. More specifically, the training set includes only prose texts of the Classical period, whereas the test set is organized in three sub-tasks: a Classical sub-task on a prose text of an author not included in the training data, a Cross-genre sub-task on poetic and scientific texts, and a Cross-time sub-task on a text of the 15th century. The results obtained by the participants for each task and sub-task are presented and discussed.
An ELECTRA Model for Latin Token Tagging Tasks
Authors: Wouter Mercelis, Alek Keersmaekers
Date: 2022-06-01 00:00:00
Proceeding: LT4HALA (LREC) 2022 6
Tasks: LEMMA, Lemmatization, model, Morphological Tagging, POS, POS Tagging

This report describes the KU Leuven / Brepols-CTLO submission to EvaLatin 2022. We present the results of our current small Latin ELECTRA model, which will be expanded to a larger model in the future. For the lemmatization task, we combine a neural token-tagging approach with the in-house rule-based lemma lists from Brepols’ ReFlex software. The results are decent, but suffer from inconsistencies between Brepols’ and EvaLatin’s definitions of a lemma. For POS-tagging, the results come up just short from the first place in this competition, mainly struggling with proper nouns. For morphological tagging, there is much more room for improvement. Here, the constraints added to our Multiclass Multilabel model were often not tight enough, causing missing morphological features. We will further investigate why the combination of the different morphological features, which perform fine on their own, leads to issues.
Transformer-based Part-of-Speech Tagging and Lemmatization for Latin
Authors: Krzysztof Wróbel, Krzysztof Nowak
Date: 2022-06-01 00:00:00
Proceeding: LT4HALA (LREC) 2022 6
Tasks: Lemmatization, Morphological Tagging, Part-Of-Speech Tagging

The paper presents a submission to the EvaLatin 2022 shared task. Our system places first for lemmatization, part-of-speech and morphological tagging in both closed and open modalities. The results for cross-genre and cross-time sub-tasks show that the system handles the diachronic and diastratic variation of Latin. The architecture employs state-of-the-art transformer models. For part-of-speech and morphological tagging, we use XLM-RoBERTa large, while for lemmatization a ByT5 small model was employed. The paper features a thorough discussion of part-of-speech and lemmatization errors which shows how the system performance may be improved for Classical, Medieval and Neo-Latin texts.
Multilingual Abstract Meaning Representation for Celtic Languages
Authors: Johannes Heinecke, Anastasia Shimorina
Date: 2022-06-01 00:00:00
Proceeding: CLTW (LREC) 2022 6
Tasks: Abstract Meaning Representation, Semantic Parsing

Deep Semantic Parsing into Abstract Meaning Representation (AMR) graphs has reached a high quality with neural-based seq2seq approaches. However, the training corpus for AMR is only available for English. Several approaches to process other languages exist, but only for high resource languages. We present an approach to create a multilingual text-to-AMR model for three Celtic languages, Welsh (P-Celtic) and the closely related Irish and Scottish-Gaelic (Q-Celtic). The main success of this approach are underlying multilingual transformers like mT5. We finally show that machine translated test corpora unfairly improve the AMR evaluation for about 1 or 2 points (depending on the language).
Diachronic Parsing of Pre-Standard Irish
Authors: Kevin Scannell
Date: 2022-06-01 00:00:00
Proceeding: CLTW (LREC) 2022 6
Tasks: Dependency Parsing, Lemmatization

Irish underwent a major spelling standardization in the 1940’s and 1950’s, and as a result it can be challenging to apply language technologies designed for the modern language to older, “pre-standard” texts. Lemmatization, tagging, and parsing of these pre-standard texts play an important role in a number of applications, including the lexicographical work on Foclóir Stairiúil na Gaeilge, a historical dictionary of Irish covering the period from 1600 to the present. We have two main goals in this paper. First, we introduce a small benchmark corpus containing just over 3800 words, annotated according to the Universal Dependencies guidelines and covering a range of dialects and time periods since 1600. Second, we establish baselines for lemmatization, tagging, and dependency parsing on this corpus by experimenting with a variety of machine learning approaches.
Creation of an Evaluation Corpus and Baseline Evaluation Scores for Welsh Text Summarisation
Authors: Mahmoud El-Haj, Ignatius Ezeani, Jonathan Morris, Dawn Knight
Date: 2022-06-01 00:00:00
Proceeding: CLTW (LREC) 2022 6

As part of the effort to increase the availability of Welsh digital technology, this paper introduces the first human vs metrics Welsh summarisation evaluation results and dataset, which we provide freely for research purposes to help advance the work on Welsh summarisation. The system summaries were created using an extractive graph-based Welsh summariser. The system summaries were evaluated by both human and a range of ROUGE metric variants (e.g. ROUGE 1, 2, L and SU4). The summaries and evaluation results will serve as benchmarks for the development of summarisers and evaluation metrics in other minority language contexts.
CLILSTORE.EU - A Multilingual online CLIL platform
Authors: Caoimhín Ó Dónaill
Date: 2022-06-01 00:00:00
Proceeding: CLTW (LREC) 2022 6

CLILSTORE.EU is an open educational resource (OER) that was created by the Erasmus + funded CLIL Open Online Learning (COOL) project which ran from 2018-2021. The project consortium included teaching practitioners from the primary, secondary, tertiary and vocational sectors who each brought their influence to bear on the design and functionality of the OER and subsequently evaluated its development within the learning contexts of their respective sectors. CLILSTORE.EU serves as both an authoring and sharing platform where multimedia learning materials can be created and accessed. Its name comprises the acronym CLIL, owing to its particular suitablity as a tool to support the Content and Language Integrated Learning methodology (Marsh, D. (ed.), 2002). The main educational aims of the OER are to provide teachers with a relatively straightforward means of creating reusable, multimodal learning units that can be used within the classroom or via remote learning to underpin and scaffold the delivery of curricular content in any subject area, especially in contexts where learners are acquiring new knowledge through the medium of a second or additional language. The following account details recent development work on the OER’s functionality and usability and presents case studies showing how it can benefit Celtic languages.
Evaluation of Three Welsh Language POS Taggers
Authors: Gruffudd Prys, Gareth Watkins
Date: 2022-06-01 00:00:00
Proceeding: CLTW (LREC) 2022 6
Tasks: Benchmarking, POS

In this paper we describe our quantitative and qualitative evaluation of three Welsh language Part of Speech (POS) taggers. Following an introductory section, we explore some of the issues which face POS taggers, discuss the state of the art in English language tagging, and describe the three Welsh language POS taggers that will be evaluated in this paper, namely WNLT2, CyTag and TagTeg. In section 3 we describe the challenges involved in evaluating POS taggers which make use of different tagsets, and introduce our mapping of the taggers’ individual tagsets to an Intermediate Tagset used to facilitate their comparative evaluation. Section 4 introduces our benchmarking corpus as an important component of our methodology. In section 5 we describe how the inconsistencies in text tokenization between the different taggers present an issue when undertaking such evaluations, and discuss the method used to overcome this complication. Section 6 illustrates how we annotated the benchmark corpus, while section 7 describes the scoring method used. Section 8 provides an in-depth analysis of the results, and a summary of the work is presented in the conclusion found in section 9. Keywords: POS Tagger, Welsh, Evaluation, Machine Learning