pwc-archive/papers-with-abstracts huggingface dataset:
A Framework for Shared Agreement of Language Tags beyond ISO 639
Authors: Frances Gillis-Webber, Sabine Tittel
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: TAG

The identification and annotation of languages in an unambiguous and standardized way is essential for the description of linguistic data. It is the prerequisite for machine-based interpretation, aggregation, and re-use of the data with respect to different languages. This makes it a key aspect especially for Linked Data and the multilingual Semantic Web. The standard for language tags is defined by IETF{'}s BCP 47 and ISO 639 provides the language codes that are the tags{'} main constituents. However, for the identification of lesser-known languages, endangered languages, regional varieties or historical stages of a language, the ISO 639 codes are insufficient. Also, the optional language sub-tags compliant with BCP 47 do not offer a possibility fine-grained enough to represent linguistic variation. We propose a versatile pattern that extends the BCP 47 sub-tag {`}privateuse{'} and is, thus, able to overcome the limits of BCP 47 and ISO 639. Sufficient coverage of the pattern is demonstrated with the use case of linguistic Linked Data of the endangered Gascon language. We show how to use a URI shortcode for the extended sub-tag, making the length compliant with BCP 47. We achieve this with a web application and API developed to encode and decode the language tag.
Gigafida 2.0: The Reference Corpus of Written Standard Slovene
Authors: Simon Krek, {\v{S}}pela Arhar Holdt, Toma{\v{z}} Erjavec, Jaka {\v{C}}ibej, Andraz Repar, Polona Gantar, Nikola Ljube{\v{s}}i{\'c}, Iztok Kosem, Kaja Dobrovoljc
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

We describe a new version of the Gigafida reference corpus of Slovene. In addition to updating the corpus with new material and annotating it with better tools, the focus of the upgrade was also on its transformation from a general reference corpus, which contains all language variants including non-standard language, to the corpus of standard (written) Slovene. This decision could be implemented as new corpora dedicated specifically to non-standard language emerged recently. In the new version, the whole Gigafida corpus was deduplicated for the first time, which facilitates automatic extraction of data for the purposes of compilation of new lexicographic resources such as the collocations dictionary and the thesaurus of Slovene.
Corpus Query Lingua Franca part II: Ontology
Authors: Stefan Evert, Oleg Harlamov, Philipp Heinrich, Piotr Banski
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

The present paper outlines the projected second part of the Corpus Query Lingua Franca (CQLF) family of standards: CQLF Ontology, which is currently in the process of standardization at the International Standards Organization (ISO), in its Technical Committee 37, Subcommittee 4 (TC37SC4) and its national mirrors. The first part of the family, ISO 24623-1 (henceforth CQLF Metamodel), was successfully adopted as an international standard at the beginning of 2018. The present paper reflects the state of the CQLF Ontology at the moment of submission for the Committee Draft ballot. We provide a brief overview of the CQLF Metamodel, present the assumptions and aims of the CQLF Ontology, its basic structure, and its potential extended applications. The full ontology is expected to emerge from a community process, starting from an initial version created by the authors of the present paper.
A CLARIN Transcription Portal for Interview Data
Authors: Christoph Draxler, Henk van den Heuvel, Arjan van Hessen, Silvia Calamai, Louise Corti
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

In this paper we present a first version of a transcription portal for audio files based on automatic speech recognition (ASR) in various languages. The portal is implemented in the CLARIN resources research network and intended for use by non-technical scholars. We explain the background and interdisciplinary nature of interview data, the perks and quirks of using ASR for transcribing the audio in a research context, the dos and don{'}ts for optimal use of the portal, and future developments foreseen. The portal is promoted in a range of workshops, but there are a number of challenges that have to be met. These challenges concern privacy issues, ASR quality, and cost, amongst others.
Ellogon Casual Annotation Infrastructure
Authors: Georgios Petasis, Leonidas Tsekouras
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Sentiment Analysis

This paper presents a new annotation paradigm, casual annotation, along with a proposed architecture and a reference implementation, the Ellogon Casual Annotation Tool, which implements this paradigm and architecture. The novel aspects of the proposed paradigm originate from the vision to tightly integrate annotation with the casual, everyday activities of users. Annotating in a less {``}controlled{''} environment, and removing the bottleneck of selecting content and importing it to annotation infrastructures, casual annotation provides the ability to vastly increase the content that can be annotated and ease the annotation process through automatic pre-training. The proposed paradigm, architecture and reference implementation has been evaluated for more than two years on an annotation task related to sentiment analysis. Evaluation results suggest that, at least for this annotation task, there is a huge improvement in productivity after casual annotation adoption, in comparison to the more traditional annotation paradigms followed in the early stages of the annotation task.
The Competitiveness Analysis of the European Language Technology Market
Authors: Andrejs Vasi{\c{l}}jevs, Inguna Skadi{\c{n}}a, Indra Samite, Kaspars Kauli{\c{n}}{\v{s}}, {\=E}riks Ajausks, J{\=u}lija Me{\c{l}}{\c{n}}ika, Aivars B{\=e}rzi{\c{n}}{\v{s}}
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Machine Translation, Translation

This paper presents the key results of a study on the global competitiveness of the European Language Technology market for three areas {--} Machine Translation, speech technology, and cross-lingual search. EU competitiveness is analyzed in comparison to North America and Asia. The study focuses on seven dimensions (research, innovations, investments, market dominance, industry, infrastructure, and Open Data) that have been selected to characterize the language technology market. The study concludes that while Europe still has strong positions in Research and Innovation, it lags behind North America and Asia in scaling innovations and conquering market share.
Constructing a Bilingual Hadith Corpus Using a Segmentation Tool
Authors: Shatha Altammami, Eric Atwell, Ammar Alsalka
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

This article describes the process of gathering and constructing a bilingual parallel corpus of Islamic Hadith, which is the set of narratives reporting different aspects of the prophet Muhammad{'}s life. The corpus data is gathered from the six canonical Hadith collections using a custom segmentation tool that automatically segments and annotates the two Hadith components with 92{\%} accuracy. This Hadith segmenter minimises the costs of language resource creation and produces consistent results independently from previous knowledge and experiences that usually influence human annotators. The corpus includes more than 10M tokens and will be freely available via the LREC repository.
Facilitating Corpus Usage: Making Icelandic Corpora More Accessible for Researchers and Language Users
Authors: Stein{\th}{\'o}r Steingr{\'\i}msson, Starka{\dh}ur Barkarson, Gunnar Thor {\"O}rn{\'o}lfsson
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Word Embeddings

We introduce an array of open and accessible tools to facilitate the use of the Icelandic Gigaword Corpus, in the field of Natural Language Processing as well as for students, linguists, sociologists and others benefitting from using large corpora. A KWIC engine, powered by the Swedish Korp tool is adapted to the specifics of the corpus. An n-gram viewer, highly customizable to suit different needs, allows users to study word usage throughout the period of our text collection. A frequency dictionary provides much sought after information about word frequency statistics, computed for each subcorpus as well as aggregate, disambiguating homographs based on their respective lemmas and morphosyntactic tags. Furthermore, we provide n-grams based on the corpus, and a variety of pre-trained word embeddings models, based on word2vec, GloVe, fastText and ELMo. For three of the model types, multiple word embedding models are available trained with different algorithms and using either lemmatised or unlemmatised texts.
Interoperability in an Infrastructure Enabling Multidisciplinary Research: The case of CLARIN
Authors: Franciska de Jong, Bente Maegaard, Darja Fi{\v{s}}er, Dieter van Uytvanck, Andreas Witt
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

CLARIN is a European Research Infrastructure providing access to language resources and technologies for researchers in the humanities and social sciences. It supports the use and study of language data in general and aims to increase the potential for comparative research of cultural and societal phenomena across the boundaries of languages and disciplines, all in line with the European agenda for Open Science. Data infrastructures such as CLARIN have recently embarked on the emerging frameworks for the federation of infrastructural services, such as the European Open Science Cloud and the integration of services resulting from multidisciplinary collaboration in federated services for the wider SSH domain. In this paper we describe the interoperability requirements that arise through the existing ambitions and the emerging frameworks. The interoperability theme will be addressed at several levels, including organisation and ecosystem, design of workflow services, data curation, performance measurement and collaboration.
Privacy by Design and Language Resources
Authors: Pawel Kamocki, Andreas Witt
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Privacy by Design (also referred to as Data Protection by Design) is an approach in which solutions and mechanisms addressing privacy and data protection are embedded through the entire project lifecycle, from the early design stage, rather than just added as an additional lawyer to the final product. Formulated in the 1990 by the Privacy Commissionner of Ontario, the principle of Privacy by Design has been discussed by institutions and policymakers on both sides of the Atlantic, and mentioned already in the 1995 EU Data Protection Directive (95/46/EC). More recently, Privacy by Design was introduced as one of the requirements of the General Data Protection Regulation (GDPR), obliging data controllers to define and adopt, already at the conception phase, appropriate measures and safeguards to implement data protection principles and protect the rights of the data subject. Failing to meet this obligation may result in a hefty fine, as it was the case in the Uniontrad decision by the French Data Protection Authority (CNIL). The ambition of the proposed paper is to analyse the practical meaning of Privacy by Design in the context of Language Resources, and propose measures and safeguards that can be implemented by the community to ensure respect of this principle.
Related Works in the Linguistic Data Consortium Catalog
Authors: Daniel Jaquette, Christopher Cieri, Denise DiPersio
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Defining relations between language resources provides an archive with the ability to better serve its users. This paper covers the development and implementation of a Related Works addition to the Linguistic Data Consortium{'}s (LDC) catalog. The authors go step-by-step through the development of the Related Works schema, implementation of the software and database changes, and data entry of the relations. The Related Work schema involved developing of a set of controlled terms for relations based on previous work and other schema. Software and database changes consisted of both front and back end interface additions, along with modification and additions to the LDC Catalog database tables. Data entry consisted of two parts: seed data from previous work and 2019 language resources, and ongoing legacy population. Previous work in this area is discussed as well as overview information about the LDC Catalog. A list of the full LDC Related Works terms is included with brief explanations.
Language Data Sharing in European Public Services -- Overcoming Obstacles and Creating Sustainable Data Sharing Infrastructures
Authors: Lilli Smal, Andrea L{\"o}sch, Josef van Genabith, Maria Giagkou, Thierry Declerck, Stephan Busemann
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Management

Data is key in training modern language technologies. In this paper, we summarise the findings of the first pan-European study on obstacles to sharing language data across 29 EU Member States and CEF-affiliated countries carried out under the ELRC White Paper action on Sustainable Language Data Sharing to Support Language Equality in Multilingual Europe. Why Language Data Matters. We present the methodology of the study, the obstacles identified and report on recommendations on how to overcome those. The obstacles are classified into (1) lack of appreciation of the value of language data, (2) structural challenges, (3) disposition towards CAT tools and lack of digital skills, (4) inadequate language data management practices, (5) limited access to outsourced translations, and (6) legal concerns. Recommendations are grouped into addressing the European/national policy level, and the organisational/institutional level.
A Progress Report on Activities at the Linguistic Data Consortium Benefitting the LREC Community
Authors: Christopher Cieri, James Fiumara, Stephanie Strassel, Jonathan Wright, Denise DiPersio, Mark Liberman
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

This latest in a series of Linguistic Data Consortium (LDC) progress reports to the LREC community does not describe any single language resource, evaluation campaign or technology but sketches the activities, since the last report, of a data center devoted to supporting the work of LREC attendees among other research communities. Specifically, we describe 96 new corpora released in 2018-2020 to date, a new technology evaluation campaign, ongoing activities to support multiple common task human language technology programs, and innovations to advance the methodology of language data collection and annotation.
Digital Language Infrastructures -- Documenting Language Actors
Authors: Verena Lyding, Alex K{\"o}nig, er, Monica Pretti
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

The major European language infrastructure initiatives like CLARIN (Hinrichs and Krauwer, 2014), DARIAH (Edmond et al., 2017) or Europeana (Europeana Foundation, 2015) have been built by focusing in the first place on institutions of larger scale, like specialized research departments and larger official units like national libraries, etc. However, besides these principal players also a large number of smaller language actors could contribute to and benefit from language infrastructures. Especially since these smaller institutions, like local libraries, archives and publishers, often collect, manage and host language resources of particular value for their geographical and cultural region, it seems highly relevant to find ways of engaging and connecting them to existing European infrastructure initiatives. In this article, we first highlight the need for reaching out to smaller local language actors and discuss challenges related to this ambition. Then we present the first step in how this objective was approached within a local language infrastructure project, namely by means of a structured documentation of the local language actors landscape in South Tyrol. We describe how the documentation efforts were structured and organized, and what tool we have set up to distribute the collected data online, by adapting existing CLARIN solutions.
Samr\'omur: Crowd-sourcing Data Collection for Icelandic Speech Recognition
Authors: David Erik Mollberg, {\'O}lafur Helgi J{\'o}nsson, Sunneva {\TH}orsteinsd{\'o}ttir, Stein{\th}{\'o}r Steingr{\'\i}msson, Eyd{\'\i}s Huld Magn{\'u}sd{\'o}ttir, Jon Gudnason
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Marketing, speech-recognition, Speech Recognition

This contribution describes an ongoing project of speech data collection, using the web application Samr{\'o}mur which is built upon Common Voice, Mozilla Foundation{'}s web platform for open-source voice collection. The goal of the project is to build a large-scale speech corpus for Automatic Speech Recognition (ASR) for Icelandic. Upon completion, Samr{\'o}mur will be the largest open speech corpus for Icelandic collected from the public domain. We discuss the methods used for the crowd-sourcing effort and show the importance of marketing and good media coverage when launching a crowd-sourcing campaign. Preliminary results exceed our expectations, and in one month we collected data that we had estimated would take three months to obtain. Furthermore, our initial dataset of around 45 thousand utterances has good demographic coverage, is gender-balanced and with proper age distribution. We also report on the task of validating the recordings, which we have not promoted, but have had numerous hours invested by volunteers.
CLFD: A Novel Vectorization Technique and Its Application in Fake News Detection
Authors: Michail Mersinias, Stergos Afantenos, Georgios Chalkiadakis
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: BIG-bench Machine Learning, Deep Learning, Fake News Detection, Word Embeddings

In recent years, fake news detection has been an emerging research area. In this paper, we put forward a novel statistical approach for the generation of feature vectors to describe a document. Our so-called class label frequency distance (clfd), is shown experimentally to provide an effective way for boosting the performance of machine learning methods. Specifically, our experiments, carried out in the fake news detection domain, verify that efficient traditional machine learning methods that use our vectorization approach, consistently outperform deep learning methods that use word embeddings for small and medium sized datasets, while the results are comparable for large datasets. In addition, we demonstrate that a novel hybrid method that utilizes both a clfd-boosted logistic regression classifier and a deep learning one, clearly outperforms deep learning methods even in large datasets.
SimplifyUR: Unsupervised Lexical Text Simplification for Urdu
Authors: Namoos Hayat Qasmi, Haris Bin Zia, Awais Athar, Agha Ali Raza
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Lexical Simplification, Text Simplification, Word Embeddings

This paper presents the first attempt at Automatic Text Simplification (ATS) for Urdu, the language of 170 million people worldwide. Being a low-resource language in terms of standard linguistic resources, recent text simplification approaches that rely on manually crafted simplified corpora or lexicons such as WordNet are not applicable to Urdu. Urdu is a morphologically rich language that requires unique considerations such as proper handling of inflectional case and honorifics. We present an unsupervised method for lexical simplification of complex Urdu text. Our method only requires plain Urdu text and makes use of word embeddings together with a set of morphological features to generate simplifications. Our system achieves a BLEU score of 80.15 and SARI score of 42.02 upon automatic evaluation on manually crafted simplified corpora. We also report results for human evaluations for correctness, grammaticality, meaning-preservation and simplicity of the output. Our code and corpus are publicly available to make our results reproducible.
Jamo Pair Encoding: Subcharacter Representation-based Extreme Korean Vocabulary Compression for Efficient Subword Tokenization
Authors: Sangwhan Moon, Naoaki Okazaki
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Language Modeling, Language Modelling

In the context of multilingual language model pre-training, vocabulary size for languages with a broad set of potential characters is an unsolved problem. We propose two algorithms applicable in any unsupervised multilingual pre-training task, increasing the elasticity of budget required for building the vocabulary in Byte-Pair Encoding inspired tokenizers, significantly reducing the cost of supporting Korean in a multilingual model.
Semi-supervised Deep Embedded Clustering with Anomaly Detection for Semantic Frame Induction
Authors: Zheng Xin Yong, Tiago Timponi Torrent
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Anomaly Detection, Clustering

Although FrameNet is recognized as one of the most fine-grained lexical databases, its coverage of lexical units is still limited. To tackle this issue, we propose a two-step frame induction process: for a set of lexical units not yet present in Berkeley FrameNet data release 1.7, first remove those that cannot fit into any existing semantic frame in FrameNet; then, assign the remaining lexical units to their correct frames. We also present the Semi-supervised Deep Embedded Clustering with Anomaly Detection (SDEC-AD) model{---}an algorithm that maps high-dimensional contextualized vector representations of lexical units to a low-dimensional latent space for better frame prediction and uses reconstruction error to identify lexical units that cannot evoke frames in FrameNet. SDEC-AD outperforms the state-of-the-art methods in both steps of the frame induction process. Empirical results also show that definitions provide contextual information for representing and characterizing the frame membership of lexical units.
Search Query Language Identification Using Weak Labeling
Authors: Ritiz Tambi, Ajinkya Kale, Tracy Holloway King
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Language Identification

Language identification is a well-known task for natural language documents. In this paper we explore search query language identification which is usually the first task before any other query understanding. Without loss of generalization, we run our experiments on the Adobe Stock search engine. Even though the domain is relatively generic because Adobe Stock queries cover a broad range of objects and concepts, out-of-the-box language identifiers do not perform well due to the extremely short text found in queries. Unlike other well-studied supervised approaches for this task, we examine a practical approach for the cold start problem for automatically getting large-scale query-language pairs for training. We describe the process of creating weak-labeled training data and then human-annotated evaluation data for the search query language identification task. The effectiveness of this technique is demonstrated by training a gradient boosting model for language classification given a query. We out-perform the open domain text model baselines by a large margin.