pwc-archive/papers-with-abstracts huggingface dataset:
Evaluating Pre-Trained Language Models for Focused Terminology Extraction from Swedish Medical Records
Authors: Oskar Jerdhaf, Marina Santini, Peter Lundberg, Tomas Bjerner, Yosef Al-Abasse, Arne Jonsson, Thomas Vakili
Date: 2022-06-01 00:00:00
Proceeding: TERM (LREC) 2022 6
Tasks: Language Modeling, Language Modelling

In the experiments briefly presented in this abstract, we compare the performance of a generalist Swedish pre-trained language model with a domain-specific Swedish pre-trained model on the downstream task of focussed terminology extraction of implant terms, which are terms that indicate the presence of implants in the body of patients. The fine-tuning is identical for both models. For the search strategy we rely on KD-Tree that we feed with two different lists of term seeds, one with noise and one without noise. Results shows that the use of a domain-specific pre-trained language model has a positive impact on focussed terminology extraction only when using term seeds without noise.
D-Terminer: Online Demo for Monolingual and Bilingual Automatic Term Extraction
Authors: Ayla Rigouts Terryn, Veronique Hoste, Els Lefever
Date: 2022-06-01 00:00:00
Proceeding: TERM (LREC) 2022 6
Tasks: Term Extraction, Translation

This contribution presents D-Terminer: an open access, online demo for monolingual and multilingual automatic term extraction from parallel corpora. The monolingual term extraction is based on a recurrent neural network, with a supervised methodology that relies on pretrained embeddings. Candidate terms can be tagged in their original context and there is no need for a large corpus, as the methodology will work even for single sentences. With the bilingual term extraction from parallel corpora, potentially equivalent candidate term pairs are extracted from translation memories and manual annotation of the results shows that good equivalents are found for most candidate terms. Accompanying the release of the demo is an updated version of the ACTER Annotated Corpora for Term Extraction Research (version 1.5).
Introducing EM-FT for Manipuri-English Neural Machine Translation
Authors: Rudali Huidrom, Yves Lepage
Date: 2022-06-01 00:00:00
Proceeding: WILDRE (LREC) 2022 6
Tasks: Machine Translation, NMT, Translation

This paper introduces a pretrained word embedding for Manipuri, a low-resourced Indian language. The pretrained word embedding based on FastText is capable of handling the highly agglutinating language Manipuri (mni). We then perform machine translation (MT) experiments using neural network (NN) models. In this paper, we confirm the following observations. Firstly, the reported BLEU score of the Transformer architecture with FastText word embedding model EM-FT performs better than without in all the NMT experiments. Secondly, we observe that adding more training data from a different domain of the test data negatively impacts translation accuracy. The resources reported in this paper are made available in the ELRA catalogue to help the low-resourced languages community with MT/NLP tasks.
Leveraging Sub Label Dependencies in Code Mixed Indian Languages for Part-Of-Speech Tagging using Conditional Random Fields.
Authors: Akash Kumar Gautam
Date: 2022-06-01 00:00:00
Proceeding: WILDRE (LREC) 2022 6
Tasks: Part-Of-Speech Tagging

Code-mixed text sequences often lead to challenges in the task of correct identification of Part-Of-Speech tags. However, lexical dependencies created while alternating between multiple languages can be leveraged to improve the performance of such tasks. Indian languages with rich morphological structure and highly inflected nature provide such an opportunity. In this work, we exploit these sub-label dependencies using conditional random fields (CRFs) by defining feature extraction functions on three distinct language pairs (Hindi-English, Bengali-English, and Telugu-English). Our results demonstrate a significant increase in the tagging performance if the feature extraction functions employ the rich inner structure of such languages.
HindiWSD: A package for word sense disambiguation in Hinglish & Hindi
Authors: Mirza Yusuf, Praatibh Surana, Chethan Sharma
Date: 2022-06-01 00:00:00
Proceeding: WILDRE (LREC) 2022 6
Tasks: Cross-Lingual Information Retrieval, Information Retrieval, POS, POS Tagging, Retrieval, Transliteration, Word Sense Disambiguation, Word Similarity

A lot of commendable work has been done, especially in high resource languages such as English, Spanish, French, etc. However, work done for Indic languages such as Hindi, Tamil, Telugu, etc is relatively less due to difficulty in finding relevant datasets, and the complexity of these languages. With the advent of IndoWordnet, we can explore important tasks such as word sense disambiguation, word similarity, and cross-lingual information retrieval, and carry out effective research regarding the same. In this paper, we worked on improving word sense disambiguation for 20 of the most common ambiguous Hindi words by making use of knowledge-based methods. We also came up with “hindiwsd”, an easy-to-use framework developed in Python that acts as a pipeline for transliteration of Hinglish code-mixed text followed by spell correction, POS tagging, and word sense disambiguation of Hindi text. We also curated a dataset of these 20 most used ambiguous Hindi words. This dataset was then used to enhance a modified Lesk’s algorithm and more accurately carry out word sense disambiguation. We achieved an accuracy of about 71% using our customized Lesk’s algorithm which was an improvement to the accuracy of about 34% using the original Lesk’s algorithm on the test set.
Pāṇinian Phonological Changes: Computation and Development of Online Access System
Authors: Sanju ., Subhash Chandra
Date: 2022-06-01 00:00:00
Proceeding: WILDRE (LREC) 2022 6
Tasks: Sentence

Pāṇini used the term saṃhitā for phonological changes. Any Sound change which alters phonemes in a particular language is called Phonological Change. It arises when two sounds are pronounced in a language with uninterrupted speed, then those letters are affected by each other due to Articulatory, Acoustic and Auditory principles in language. The pronunciation of two sounds that are in extreme proximity, affects each other and changes them. In simple words, this phenomenon is known as sandhi. Sanskrit is considered one of the oldest languages in the world. It has produced one of the most huge literary text corpora in the world. The tradition of Sanskrit started in the Vedic period. Pāṇini’s Aṣṭādhyāyī (AD) is a complete grammar of Sanskrit. It also covers Sanskrit sounds and phonology. Phonological changes are a natural phenomenon in any language during speech but in Sanskrit, it is highly reflected. Sanskrit corpora contain numerous long words. It looks like a single sentence due to sandhi between multiple words. The process of phonological changes occurred based on certain rules of pronunciation and it is codified by the Pāṇini in AD. Pāṇini has codified these rules systemically but the computation of these rules is a challenging task. Therefore, the objective of the paper is to compute the rules and demonstrate an online access system for Sanskrit sandhi. The system also generates the whole process of phonological changes based on Pāṇinian Rules. It also plays a very effective role in Digital classroom teaching, boosting teaching skills and the learning process.
Identifying Emotions in Code Mixed Hindi-English Tweets
Authors: Sanket Sonu, Rejwanul Haque, Mohammed Hasanuzzaman, Paul Stynes, Pramod Pathak
Date: 2022-06-01 00:00:00
Proceeding: WILDRE (LREC) 2022 6
Tasks: text-classification, Text Classification

Emotion detection (ED) in tweets is a text classification problem that is of interest to Natural Language Processing (NLP) researchers. Code-mixing (CM) is a process of mixing linguistic units such as words of two different languages. The CM languages are characteristically different from the languages whose linguistic units are used for mixing. Whilst NLP has been shown to be successful for low-resource languages, it becomes challenging to perform NLP tasks on CM languages. As for ED, it has been rarely investigated on CM languages such as Hindi—English due to the lack of training data that is required for today’s data-driven classification algorithms. This research proposes a gold standard dataset for detecting emotions in CM Hindi–English tweets. This paper also presents our results about the investigation of the usefulness of our gold-standard dataset while testing a number of state-of-the-art classification algorithms. We found that the ED classifier built using SVM provided us the highest accuracy (75.17%) on the hold-out test set. This research would benefit the NLP community in detecting emotions from social media platforms in multilingual societies.
Digital Accessibility and Information Mining of Dharmaśāstric Knowledge Traditions
Authors: Arooshi Nigam, Subhash Chandra
Date: 2022-06-01 00:00:00
Proceeding: WILDRE (LREC) 2022 6
Tasks: Cultural Vocal Bursts Intensity Prediction, Management

The heritage of Dharmaśāstra (DS) carries extensive cultural history and encapsulates the treatises of Ancient Indian Social Institutions (SI). DS is reckoned as an epitome of the primitive Indian knowledge tradition as it incorporates a variety of genres for sciences and arts such as family law and legislation, civilization, culture, ritualistic procedures, environment, economics, commerce and finance studies, management, mathematical and medical sciences etc. SI represents a distinct tradition of civilization formation, society development and community living. The texts of the DS are primarily written in the Sanskrit language and due to its expansive subject stream, it is later translated into various other languages globally. With the ingress of the internet, the development of advanced digital technologies and IT boom, information is accessed and exchanged via digital platforms. DS texts are studied not only by Sanskrit scholars but also referred by historians, sociologists, political scientists, economists, law enthusiasts and linguists worldwide. Despite its eminence, there is a major setback in digitizing and online information mining for DS texts. The major objective of the paper is to digitize and develop an instant referencing system to amplify the digital accessibility of DS texts. This will act as an effective and immediate learning tool for researchers who are keen on intensive studying of DS concepts.
Language Resource Building and English-to-Mizo Neural Machine Translation Encountering Tonal Words
Authors: Vanlalmuansangi Khenglawt, Sahinur Rahman Laskar, Santanu Pal, Partha Pakray, Ajoy Kumar Khan
Date: 2022-06-01 00:00:00
Proceeding: WILDRE (LREC) 2022 6
Tasks: Diversity, Machine Translation, Translation

Multilingual country like India has an enormous linguistic diversity and has an increasing demand towards developing language resources such that it will outreach in various natural language processing applications like machine translation. Low-resource language translation possesses challenges in the field of machine translation. The challenges include the availability of corpus and differences in linguistic information. This paper investigates a low-resource language pair, English-to-Mizo exploring neural machine translation by contributing an Indian language resource, i.e., English-Mizo corpus. In this work, we explore one of the main challenges to tackling tonal words existing in the Mizo language, as they add to the complexity on top of low-resource challenges for any natural language processing task. Our approach improves translation accuracy by encountering tonal words of Mizo and achieved a state-of-the-art result in English-to-Mizo translation.
Classification of Multiword Expressions in Malayalam
Authors: Treesa Cyriac, Sobha Lalitha Devi
Date: 2022-06-01 00:00:00
Proceeding: WILDRE (LREC) 2022 6
Tasks: Classification

Multiword expression is an interesting concept in languages and the MWEs of a language are not easy for a non-native speaker to understand. It includes lexicalized phrases, idioms, collocations etc. Data on multiwords are helpful in language processing. ‘Multiword expressions in Malayalam’ is a less studied area. In this paper, we are trying to explore multiwords in Malayalam and to classify them as per the three idiosyncrasies: semantic idiosyncrasy, syntactic idiosyncrasy, and statistic idiosyncrasy. Though these are already identified, they are not being studied in Malayalam. The classification and features are given and are studied using Malayalam multiwords. Through this study, we identified how the linguistic features of Malayalam such as agglutination influence its multiword expressions in terms of pronunciation and spelling. Malayalam has a set of code-mixed multiword expressions which is also addressed in this study.
Bengali and Magahi PUD Treebank and Parser
Authors: Pritha Majumdar, Deepak Alok, Akanksha Bansal, Atul Kr. Ojha, John P. McCrae
Date: 2022-06-01 00:00:00
Proceeding: WILDRE (LREC) 2022 6

This paper presents the development of the Parallel Universal Dependency (PUD) Treebank for two Indo-Aryan languages: Bengali and Magahi. A treebank of 1,000 sentences has been created using a parallel corpus of English and the UD framework. A preliminary set of sentences was annotated manually - 600 for Bengali and 200 for Magahi. The rest of the sentences were built using the Bengali and Magahi parser. The sentences have been translated and annotated manually by the authors, some of whom are also native speakers of the languages. The objective behind this work is to build a syntactically-annotated linguistic repository for the aforementioned languages, that can prove to be a useful resource for building further NLP tools. Additionally, Bengali and Magahi parsers were also created which is built on machine learning approach. The accuracy of the Bengali parser is 78.13% in the case of UPOS; 76.99% in the case of XPOS, 56.12% in the case of UAS; and 47.19% in the case of LAS. The accuracy of Magahi parser is 71.53% in the case of UPOS; 66.44% in the case of XPOS, 58.05% in the case of UAS; and 33.07% in the case of LAS. This paper also includes an illustration of the annotation schema followed, the findings of the Parallel Universal Dependency (PUD) treebank, and it’s resulting linguistic analysis
Makadi: A Large-Scale Human-Labeled Dataset for Hindi Semantic Parsing
Authors: Shashwat Vaibhav, Nisheeth Srivastava
Date: 2022-06-01 00:00:00
Proceeding: WILDRE (LREC) 2022 6
Tasks: Diversity, Natural Language Queries, Semantic Parsing, Text to SQL, Text-To-SQL

Parsing natural language queries into formal database calls is a very well-studied problem. Because of the rich diversity of semantic markers across the world’s languages, progress in solving this problem is irreducibly language-dependent. This has created an asymmetry in progress in NLIDB solutions, with most state-of-the-art efforts focused on the resource-rich English language, with limited progress seen for low resource languages. In this short paper, we present Makadi, a large-scale, complex, cross-lingual, cross-domain semantic parsing and text-to-SQL dataset for semantic parsing in the Hindi language. Produced by translating the recently introduced English language Spider NLIDB dataset, it consists of 9693 questions and SQL queries on 166 databases with multiple tables which cover multiple domains. This is the first large-scale dataset in the Hindi language for semantic parsing and related language understanding tasks. Our dataset is publicly available at: Link removed to preserve anonymization during peer review.
Automatic Identification of Explicit Connectives in Malayalam
Authors: Kumari Sheeja S, Sobha Lalitha Devi
Date: 2022-06-01 00:00:00
Proceeding: WILDRE (LREC) 2022 6
Tasks: POS

This work presents an automatic identification of explicit connectives and its arguments using supervised method, Conditional Random Fields (CRFs). In this work, we focus on the identification of connectives and their arguments in the corpus. We consider explicit connectives and its arguments for the present study. The corpus we have considered has 4,000 sentences from Malayalam documents and manually annotated the corpus for POS, chunk, clause, discourse connectives and its arguments. The corpus thus annotated is used for building the base engine. The percentage of the performance of the system is evaluated based on the precision, recall and F-score and obtained encouraging results. We have analysed the errors generated by the system and used the features obtained from the anlaysis to improve the performance of the system
Web based System for Derivational Process of Kṛdanta based on Pāṇinian Grammatical Tradition
Authors: Sumit Sharma, Subhash Chandra
Date: 2022-06-01 00:00:00
Proceeding: WILDRE (LREC) 2022 6

Each text of the Sanskrit literature is wadded with the uses of Sanskrit kṛdanta (participles). The knowledge and formation process of Sanskrit kṛdanta play a key role to understand the meaning of a particular kṛdanta word in Sanskrit. Without proper analysis of the kṛdanta, the Sanskrit text cannot be understood. Currently, the mode of Sanskrit learning is traditional classroom teaching which is accessible to the students but not to general Sanskrit learners. The acute growth of Information Technology (IT) is changed the educational pedagogy and web-based learning systems evolved to enhance the teaching-learning process. Though many online tools are being developed by researchers for Sanskrit these are still scarce and untasted. Globe genuinely demands the high impacted tools for Sanskrit. Undoubtedly, Sanskrit kṛdanta is part of the syllabus of all universities offering Sanskrit courses. Approximately 100 plus kṛt suffixes are added with verb roots to generate kṛdanta forms and due to complexity, the learning of these forms is a challenging task. Therefore, the objective of the paper is to present an online system for teaching the derivational process of kṛdantas based on Pāṇinian rules and generate a complete derivational process of the kṛdantas for teaching and learning. It will also provide a platform for e-learning for the derivational process of Sanskrit kṛdantas.
Computational Referencing System for Sanskrit Grammar
Authors: Baldev Khandoliyan, Ram Kishor
Date: 2022-06-01 00:00:00
Proceeding: WILDRE (LREC) 2022 6
Tasks: Management

The goal of this project was to reconstitute and storage the text of Aṣṭādhyāyī (AD) in a computer text system so that everyone may read it. The proposed work was to do study the structure of AD and to create a relational database system for storing and interacting with AD. The system is available online, including Devanāgari Unicode and other major Indian characters as input and output, MS SQL Server, a Relational Database Management System (RDBMS)-based system, and Java Server Pages (JSP) were used. For AD, the system works as a multi-dimensional interactive knowledge-based computer system. The approach can also be applied to all Sanskrit sūtra texts that have a similar format. Sanskrit heritage texts are projected to benefit from the system’s preservation and promotion. A research is being made here for preparing an AD text as a computer aided dynamic search, learning and instruction system in the Indian context.
Keynote Speech - Major developments in the legal framework concerning language resources
Authors: Pawel Kamocki
Date: 2022-06-01 00:00:00
Proceeding: LEGAL (LREC) 2022 6

Introductory Talk for the Workshop on Legal and Ethical Issues in Human Language Technologies, LREC 2022, Marseille, 24 June 2022
Sentiment Analysis and Topic Modeling for Public Perceptions of Air Travel: COVID Issues and Policy Amendments
Authors: Avery Field, Aparna Varde, Pankaj Lal
Date: 2022-06-01 00:00:00
Proceeding: LEGAL (LREC) 2022 6
Tasks: Ethics, Sentiment Analysis

Among many industries, air travel is impacted by the COVID pandemic. Airlines and airports rely on public sector information to enforce guidelines for ensuring health and safety of travelers. Such guidelines can be policy amendments or laws during the pandemic. In response to the inception of COVID preventive policies, travelers have exercised freedom of expression via the avenue of online reviews. This avenue facilitates voicing public concern while anonymizing / concealing user identity as needed. It is important to assess opinions on policy amendments to ensure transparency and openness, while also preserving confidentiality and ethics. Hence, this study leverages data science to analyze, with identity protection, the online reviews of airlines and airports since 2017, considering impacts of COVID issues and relevant policy amendments since 2020. Supervised learning with VADER sentiment analysis is deployed to predict changes in opinion from 2017 to date. Unsupervised learning with LDA topic modeling is employed to discover air travelers’ major areas of concern before and after the pandemic. This study reveals that COVID policies have worsened public perceptions of air travel and aroused notable new concerns, affecting economics, environment and health.
Data Protection, Privacy and US Regulation
Authors: Denise DiPersio
Date: 2022-06-01 00:00:00
Proceeding: LEGAL (LREC) 2022 6

This paper examines the state of data protection and privacy in the United States. There is no comprehensive federal data protection or data privacy law despite bipartisan and popular support. There are several data protection bills pending in the 2022 session of the US Congress, five of which are examined in Section 2 below. Although it is not likely that any will be enacted, the growing number reflects the concerns of citizens and lawmakers about the power of big data. Recent actions against data abuses, including data breaches, litigation and settlements, are reviewed in Section 3 of this paper. These reflect the real harm caused when personal data is misused. Section 4 contains a brief US copyright law update on the fair use exemption, highlighting a recent court decision and indications of a re-thinking of the fair use analysis. In Section 5, some observations are made on the role of privacy in data protection regulation. It is argued that privacy should be considered from the start of the data collection and technology development process. Enhanced awareness of ethical issues, including privacy, through university-level data science programs will also lay the groundwork for best practices throughout the data and development cycles.
Pseudonymisation of Speech Data as an Alternative Approach to GDPR Compliance
Authors: Pawel Kamocki, Ingo Siegert
Date: 2022-06-01 00:00:00
Proceeding: LEGAL (LREC) 2022 6

The debate on the use of personal data in language resources usually focuses — and rightfully so — on anonymisation. However, this very same debate usually ends quickly with the conclusion that proper anonymisation would necessarily cause loss of linguistically valuable information. This paper discusses an alternative approach — pseudonymisation. While pseudonymisation does not solve all the problems (inasmuch as pseudonymised data are still to be regarded as personal data and therefore their processing should still comply with the GDPR principles), it does provide a significant relief, especially — but not only — for those who process personal data for research purposes. This paper describes pseudonymisation as a measure to safeguard rights and interests of data subjects under the GDPR (with a special focus on the right to be informed). It also provides a concrete example of pseudonymisation carried out within a research project at the Institute of Information Technology and Communications of the Otto von Guericke University Magdeburg.