pwc-archive/papers-with-abstracts huggingface dataset:
Signed Language Transcription and the Creation of a Cross-linguistic Comparative Database
Authors: Justin Power, David Quinto-Pozos, Danny Law
Date: 2022-06-01 00:00:00
Proceeding: SignLang (LREC) 2022 6

As the availability of signed language data has rapidly increased, sign scholars have been confronted with the challenge of creating a common framework for the cross-linguistic comparison of the phonological forms of signs. While transcription techniques have played a fundamental role in the creation of cross-linguistic comparative databases for spoken languages, transcription has featured much less prominently in sign research and lexicography. Here we report the experiences of the Sign Change project in using the signed language transcription system HamNoSys to create a comparative database of basic vocabulary for thirteen signed languages. We report the results of a small-scale study, in which we measured (i) the average time required for two trained transcribers to complete a transcription and (ii) the similarity of their independently produced transcriptions. We find that, across the two transcribers, the transcription of one sign required, on average, one minute and a half. We also find that the similarity of transcriptions differed across phonological parameters. We consider the implications of our findings about transcription time and transcription similarity for other projects that plan to incorporate transcription techniques.
Integrating Auslan Resources into the Language Data Commons of Australia
Authors: River Tae Smith, Louisa Willoughby, Trevor Johnston
Date: 2022-06-01 00:00:00
Proceeding: SignLang (LREC) 2022 6

This paper describes a project to secure Auslan (Australian Sign Language) resources within a national language data network called the Language Data Commons of Australia (LDaCA). The resources are Auslan Signbank, a web-based multi-media dictionary, and the Auslan Corpus, a collection of video recordings of the language being used in various contexts with time-aligned ELAN annotation files. We aim to make these resources accessible to the language community, encourage community participation in the curation of the data, and facilitate and extend their uses in language teaching and linguistic research. The software platforms of both resources will be made compatible with other LDaCA resources; and the two will also be aggregated and linked so that (i) users of the dictionary can view attested corpus examples for an entry; and (ii) users of the corpus can instantly view the dictionary entry for an already glossed sign to check phonological, lexical and grammatical information about it, and/or to ensure that the correct annotation gloss (aka ‘ID-gloss’) for a sign token has been chosen. This will enhance additions to annotations in the Auslan Corpus, entries in Auslan Signbank and the integrity of research based on both.
Capturing Distalization
Authors: Rose Stamp, Lilyana Khatib, Hagit Hel-Or
Date: 2022-06-01 00:00:00
Proceeding: SignLang (LREC) 2022 6

Coding and analyzing large amounts of video data is a challenge for sign language researchers, who traditionally code 2D video data manually. In recent years, the implementation of 3D motion capture technology as a means of automatically tracking movement in sign language data has been an important step forward. Several studies show that motion capture technologies can measure sign language movement parameters – such as volume, speed, variance – with high accuracy and objectivity. In this paper, using motion capture technology and machine learning, we attempt to automatically measure a more complex feature in sign language known as distalization. In general, distalized signs use the joints further from the torso (such as the wrist), however, the measure is relative and therefore distalization is not straightforward to measure. The development of a reliable and automatic measure of distalization using motion tracking technology is of special interest in many fields of sign language research.
The Corpus of Israeli Sign Language
Authors: Rose Stamp, Ora Ohanin, Sara Lanesman
Date: 2022-06-01 00:00:00
Proceeding: SignLang (LREC) 2022 6

The Corpus of Israeli Sign Language is a four-year project (2020-2024) which aims to create a digital open-access corpus of spontaneous and elicited data from a representative sample of the Israeli deaf community. In this paper, the methodology for building the Corpus of Israeli Sign Language is described. Israeli Sign Language (ISL) is the main sign language used across Israel by around 10,000 people. As part of the corpus, data will be collected from 120 deaf ISL signers across four sites in Israel: Tel Aviv and the Centre, Haifa and the North, Be’er Sheva and the South and Jerusalem and the surrounding area. Participants will engage in a variety of tasks, eliciting a range of signing styles from free conversation to lexical elicitation. The dataset will consist of recordings of over 360 hours of video data which will be used to conduct sociolinguistic investigations of language contact, variation, and change in the near term, and other linguistic analyses in the future.
Segmentation of Signs for Research Purposes: Comparing Humans and Machines
Authors: Bencie Woll, Neil Fox, Kearsy Cormier
Date: 2022-06-01 00:00:00
Proceeding: SignLang (LREC) 2022 6
Tasks: Segmentation

Sign languages such as British Sign Language (BSL) are visual languages which lack standard writing systems. Annotation of sign language data, especially for the purposes of machine readability, is therefore extremely slow. Tools to help automate and thus speed up the annotation process are very much needed. Here we test the development of one such tool (VIA-SLA), which uses temporal convolutional networks (Renz et al., 2021a, b) for the purpose of segmenting continuous signing in any sign language, and is designed to integrate smoothly with ELAN, the widely used annotation software for analysis of videos of sign language. We compare automatic segmentation by machine with segmentation done by a human, both in terms of time needed and accuracy of segmentation, using samples taken from the BSL Corpus (Schembri et al., 2014). A small sample of four short video files is tested (mean duration 25 seconds). We find that mean accuracy in terms of number and location of segmentations is relatively high, at around 78%. This preliminary test suggests that VIA-SLA promises to be very useful for sign linguists.
Sign Language Video Anonymization
Authors: Zhaoyang Xia, Yuxiao Chen, Qilong Zhangli, Matt Huenerfauth, Carol Neidle, Dimitri Metaxas
Date: 2022-06-01 00:00:00
Proceeding: SignLang (LREC) 2022 6
Tasks: Decoder, Image Animation, Optical Flow Estimation

Deaf signers who wish to communicate in their native language frequently share videos on the Web. However, videos cannot preserve privacy—as is often desirable for discussion of sensitive topics—since both hands and face convey critical linguistic information and therefore cannot be obscured without degrading communication. Deaf signers have expressed interest in video anonymization that would preserve linguistic content. However, attempts to develop such technology have thus far shown limited success. We are developing a new method for such anonymization, with input from ASL signers. We modify a motion-based image animation model to generate high-resolution videos with the signer identity changed, but with the preservation of linguistically significant motions and facial expressions. An asymmetric encoder-decoder structured image generator is used to generate the high-resolution target frame from the low-resolution source frame based on the optical flow and confidence map. We explicitly guide the model to attain a clear generation of hands and faces by using bounding boxes to improve the loss computation. FID and KID scores are used for the evaluation of the realism of the generated frames. This technology shows great potential for practical applications to benefit deaf signers.
Identifying Cleartext in Historical Ciphers
Authors: Maria-Elena Gambardella, Beata Megyesi, Eva Pettersson
Date: 2022-06-01 00:00:00
Proceeding: LT4HALA (LREC) 2022 6
Tasks: Cryptanalysis

In historical encrypted sources we can find encrypted text sequences, also called ciphertext, as well as non-encrypted cleartexts written in a known language. While most of the cryptanalysis focuses on the decryption of ciphertext, cleartext is often overlooked although it can give us important clues about the historical interpretation and contextualisation of the manuscript. In this paper, we investigate to what extent we can automatically distinguish cleartext from ciphertext in historical ciphers and to what extent we are able to identify its language. The problem is challenging as cleartext sequences in ciphers are often short, up to a few words, in different languages due to historical code-switching. To identify the sequences and the language(s), we chose a rule-based approach and run 7 different models using historical language models on various ciphertexts.
Detecting Diachronic Syntactic Developments in Presence of Bias Terms
Authors: Oliver Hellwig, Sven Sellmer
Date: 2022-06-01 00:00:00
Proceeding: LT4HALA (LREC) 2022 6

Corpus-based studies of diachronic syntactic changes are typically guided by the results of previous qualitative research. When such results are missing or, as is the case for Vedic Sanskrit, are restricted to small parts of a transmitted corpus, an exploratory framework that detects such changes in a data-driven fashion can substantially support the research process. In this paper, we introduce a customized version of the infinite relational model that groups syntactic constituents based on their structural similarities and their diachronic distributions. We propose a simple way to control for register and intellectual affiliation, and discuss our findings for four syntactic structures in Vedic texts.
Accurate Dependency Parsing and Tagging of Latin
Authors: Sebastian Nehrdich, Oliver Hellwig
Date: 2022-06-01 00:00:00
Proceeding: LT4HALA (LREC) 2022 6
Tasks: Dependency Parsing, Word Embeddings

Having access to high-quality grammatical annotations is important for downstream tasks in NLP as well as for corpus-based research. In this paper, we describe experiments with the Latin BERT word embeddings that were recently be made available by Bamman and Burns (2020). We show that these embeddings produce competitive results in the low-level task morpho-syntactic tagging. In addition, we describe a graph-based dependency parser that is trained with these embeddings and that clearly outperforms various baselines.
Annotating “Absolute” Preverbs in the Homeric and Vedic Treebanks
Authors: Luca Brigada Villa, Erica Biagetti, Chiara Zanchi
Date: 2022-06-01 00:00:00
Proceeding: LT4HALA (LREC) 2022 6
Tasks: Position

Indo-European preverbs are uninflected morphemes attaching to verbs and modifying their meaning. In Early Vedic and Homeric Greek, these morphemes held ambiguous morphosyntactic status raising issues for syntactic annotation. This paper focuses on the annotation of preverbs in so-called “absolute” position in two Universal Dependencies treebanks. This issue is related to the broader topic of how to annotate ellipsis in Universal Dependencies. After discussing some of the current annotations, we propose a new scheme that better accounts for the variety of absolute constructions.
CHJ-WLSP: Annotation of ‘Word List by Semantic Principles’ Labels for the Corpus of Historical Japanese
Authors: Masayuki Asahara, Nao Ikegami, Tai Suzuki, Taro Ichimura, Asuko Kondo, Sachi Kato, Makoto Yamazaki
Date: 2022-06-01 00:00:00
Proceeding: LT4HALA (LREC) 2022 6

This article presents a word-sense annotation for the Corpus of Historical Japanese: a mashed-up Japanese lexicon based on the ‘Word List by Semantic Principles’ (WLSP). The WLSP is a large-scale Japanese thesaurus that includes 98,241 entries with syntactic and hierarchical semantic categories. The historical WLSP is also compiled for the words in ancient Japanese. We utilized a morpheme-word sense alignment table to extract all possible word sense candidates for each word appearing in the target corpus. Then, we manually disambiguated the word senses for 647,751 words in the texts from the 10th century to 1910.
The IKUVINA Treebank
Authors: Mathieu Dehouck
Date: 2022-06-01 00:00:00
Proceeding: LT4HALA (LREC) 2022 6

In this paper, we introduce the first dependency treebank for the Umbrian language (an extinct Indo-European language from the Italic family, once spoken in modern day Italy). We present the source of the corpus : a set of seven bronze tablets describing religious ceremonies, written using two different scripts, unearthed in Umbria in the XVth century. The corpus itself has already been studied extensively by specialists of old Italic and classical Indo-European languages. So we discuss a number of challenges that we encountered as we annotated the corpus following Universal Dependencies’ guidelines from existing textual analyses.
Machine Translation of 16Th Century Letters from Latin to German
Authors: Lukas Fischer, Patricia Scheurer, Raphael Schwitter, Martin Volk
Date: 2022-06-01 00:00:00
Proceeding: LT4HALA (LREC) 2022 6
Tasks: Machine Translation, NMT, Translation

This paper outlines our work in collecting training data for and developing a Latin–German Neural Machine Translation (NMT) system, for translating 16th century letters. While Latin–German is a low-resource language pair in terms of NMT, the domain of 16th century epistolary Latin is even more limited in this regard. Through our efforts in data collection and data generation, we are able to train a NMT model that provides good translations for short to medium sentences, and outperforms GoogleTranslate overall. We focus on the correspondence of the Swiss reformer Heinrich Bullinger, but our parallel corpus and our NMT system will be of use for many other texts of the time.
A Treebank-based Approach to the Supprema Constructio in Dante’s Latin Works
Authors: Flavio Massimiliano Cecchini, Giulia Pedonese
Date: 2022-06-01 00:00:00
Proceeding: LT4HALA (LREC) 2022 6
Tasks: Sentence

This paper aims to apply a corpus-driven approach to Dante Alighieri’s Latin works using UDante, a treebank based on Dante Search and part of the Universal Dependencies project. We present a method based on the notion of barycentre applied to a dependency tree as a way to calculate the “syntactic balance” of a sentence. Its application to Dante’s Latin works shows its potential in analysing the style of an author, and contributes to the interpretation of the supprema constructio mentioned in DVE II vi 7 as a well balanced syntactic pattern modeled on Latin literary writing.
From Inscriptions to Lexica and Back: A Platform for Editing and Linking the Languages of Ancient Italy
Authors: Valeria Quochi, Andrea Bellandi, Fahad Khan, Michele Mallia, Francesca Murano, Silvia Piccini, Luca Rigobianco, Alessandro Tommasi, Cesare Zavattari
Date: 2022-06-01 00:00:00
Proceeding: LT4HALA (LREC) 2022 6

Available language technology is hardly applicable to scarcely attested ancient languages, yet their digital semantic representation, though challenging, is an asset for the purpose of sharing and preserving existing cultural knowledge. In the context of a project on the languages and cultures of ancient Italy, we took up this challenge. The paper thus describes the development of a user friendly web platform, EpiLexO, for the creation and editing of an integrated system of language resources for ancient fragmentary languages centered on the lexicon, in compliance with current digital humanities and Linked Open Data principles. EpiLexo allows for the editing of lexica with all relevant cross-references: for their linking to their testimonies, as well as to bibliographic information and other (external) resources and common vocabularies. The focus of the current implementation is on the languages of ancient Italy, in particular Oscan, Faliscan, Celtic and Venetic; however, the technological solutions are designed to be general enough to be potentially applicable to different scenarios.
BERToldo, the Historical BERT for Italian
Authors: Alessio Palmero Aprosio, Stefano Menini, Sara Tonelli
Date: 2022-06-01 00:00:00
Proceeding: LT4HALA (LREC) 2022 6
Tasks: POS, POS Tagging

Recent works in historical language processing have shown that transformer-based models can be successfully created using historical corpora, and that using them for analysing and classifying data from the past can be beneficial compared to standard transformer models. This has led to the creation of BERT-like models for different languages trained with digital repositories from the past. In this work we introduce the Italian version of historical BERT, which we call BERToldo. We evaluate the model on the task of PoS-tagging Dante Alighieri’s works, considering not only the tagger performance but also the model size and the time needed to train it. We also address the problem of duplicated data, which is rather common for languages with a limited availability of historical corpora. We show that deduplication reduces training time without affecting performance. The model and its smaller versions are all made available to the research community.
In Search of the Flocks: How to Perform Onomasiological Queries in an Ancient Greek Corpus?
Authors: Alek Keersmaekers, Toon Van Hal
Date: 2022-06-01 00:00:00
Proceeding: LT4HALA (LREC) 2022 6

This paper explores the possibilities of onomasiologically querying corpus data of Ancient Greek. The significance of the onomasiological approach has been highlighted in recent studies, yet the possibilities of performing ‘word-finding’ investigations into corpus data have not been dealt with in depth. The case study chosen focuses on collective nouns denoting animate groups (such as flocks of people, herds of cattle). By relying on a large automatically annotated corpus of Ancient Greek and on token-based vector information, a longlist of collective nouns was compiled through morpho-syntactic extraction and successive clustering procedures. After reducing this longlist to a shortlist, the results obtained are evaluated. In general, we find that πλῆθος can be considered to be the default collective noun of both humans and animals, becoming especially prominent during the Hellenistic period. In addition, specific tendencies in the use of collective nouns are discerned for specific semantic classes (e.g. gods and insects) and over time. Throughout the paper, special attention is paid to methodological issues related to onomasiologically searching.
Contextual Unsupervised Clustering of Signs for Ancient Writing Systems
Authors: Michele Corazza, Fabio Tamburini, Miguel Valério, Silvia Ferrara
Date: 2022-06-01 00:00:00
Proceeding: LT4HALA (LREC) 2022 6
Tasks: Clustering

The application of machine learning techniques to ancient writing systems is a relatively new idea, and it poses interesting challenges for researchers. One particularly challenging aspect is the scarcity of data for these scripts, which contrasts with the large amounts of data usually available when applying neural models to computational linguistics and other fields. For this reason, any method that attempts to work on ancient scripts needs to be ad-hoc and consider paleographic aspects, in addition to computational ones. Considering the peculiar characteristics of the script that we used is therefore be a crucial part of our work, as any solution needs to consider the particular nature of the writing system that it is applied to. In this work we propose a preliminary evaluation of a novel unsupervised clustering method on Cypro-Greek syllabary, a writing system from Cyprus. This evaluation shows that our method improves clustering performance using information about the attested sequences of signs in combination with an unsupervised model for images, with the future goal of applying the methodology to undeciphered writing systems from a related and typologically similar script.
Towards the Creation of a Diachronic Corpus for Italian: A Case Study on the GDLI Quotations
Authors: Manuel Favaro, Elisa Guadagnini, Eva Sassolini, Marco Biffi, Simonetta Montemagni
Date: 2022-06-01 00:00:00
Proceeding: LT4HALA (LREC) 2022 6
Tasks: Lemmatization, POS, POS Tagging

In this paper we describe some experiments related to a corpus derived from an authoritative historical Italian dictionary, namely the Grande dizionario della lingua italiana (‘Great Dictionary of Italian Language’, in short GDLI). Thanks to the digitization and structuring of this dictionary, we have been able to set up the first nucleus of a diachronic annotated corpus that selects—according to specific criteria, and distinguishing between prose and poetry—some of the quotations that within the entries illustrate the different definitions and sub-definitions. In fact, the GDLI presents a huge collection of quotations covering the entire history of the Italian language and thus ranging from the Middle Ages to the present day. The corpus was enriched with linguistic annotation and used to train and evaluate NLP models for POS tagging and lemmatization, with promising results.
Automatic Translation Alignment for Ancient Greek and Latin
Authors: Tariq Yousef, Chiara Palladino, David J. Wright, Monica Berti
Date: 2022-06-01 00:00:00
Proceeding: LT4HALA (LREC) 2022 6
Tasks: Language Modeling, Language Modelling, Translation

This paper presents the results of automatic translation alignment experiments on a corpus of texts in Ancient Greek translated into Latin. We used a state-of-the-art alignment workflow based on a contextualized multilingual language model that is fine-tuned on the alignment task for Ancient Greek and Latin. The performance of the alignment model is evaluated on an alignment gold standard consisting of 100 parallel fragments aligned manually by two domain experts, with a 90.5% Inter-Annotator-Agreement (IAA). An interactive online interface is provided to enable users to explore the aligned fragments collection and examine the alignment model’s output.
Handling Stress in Finite-State Morphological Analyzers for Ancient Greek and Ancient Hebrew
Authors: Daniel Swanson, Francis Tyers
Date: 2022-06-01 00:00:00
Proceeding: LT4HALA (LREC) 2022 6
Tasks: Form, Morphological Analysis

Modeling stress placement has historically been a challenge for computational morphological analysis, especially in finite-state systems because lexically conditioned stress cannot be modeled using only rewrite rules on the phonological form of a word. However, these phenomena can be modeled fairly easily if the lexicon’s internal representation is allowed to contain more information than the pure phonological form. In this paper we describe the stress systems of Ancient Greek and Ancient Hebrew and we present two prototype finite-state morphological analyzers, one for each language, which successfully implement these stress systems by inserting a small number of control characters into the phonological form, thus conclusively refuting the claim that finite-state systems are not powerful enough to model such stress systems and arguing in favor of the continued relevance of finite-state systems as an appropriate tool for modeling the morphology of historical languages.