pwc-archive/papers-with-abstracts huggingface dataset:
Investigating Active Learning Sampling Strategies for Extreme Multi Label Text Classification
Authors: Lukas Wertz, Katsiaryna Mirylenka, Jonas Kuhn, Jasmina Bogojeska
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Active Learning, Extreme Multi-Label Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Multi Label Text Classification, Multi-Label Text Classification, text-classification, Text Classification

Large scale, multi-label text datasets with high numbers of different classes are expensive to annotate, even more so if they deal with domain specific language. In this work, we aim to build classifiers on these datasets using Active Learning in order to reduce the labeling effort. We outline the challenges when dealing with extreme multi-label settings and show the limitations of existing Active Learning strategies by focusing on their effectiveness as well as efficiency in terms of computational cost. In addition, we present five multi-label datasets which were compiled from hierarchical classification tasks to serve as benchmarks in the context of extreme multi-label classification for future experiments. Finally, we provide insight into multi-class, multi-label evaluation and present an improved classifier architecture on top of pre-trained transformer language models.
German Light Verb Constructions in Business Process Models
Authors: Kristin Kutzner, Ralf Laue
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

We present a resource of German light verb constructions extracted from textual labels in graphical business process models. Those models depict the activities in processes in an organization in a semi-formal way. From a large range of sources, we compiled a repository of 2,301 business process models. Their textual labels (altogether 52,963 labels) were analyzed. This produced a list of 5,246 occurrences of 846 light verb constructions. We found that the light verb constructions that occur in business process models differ from light verb constructions that have been analyzed in other texts. Hence, we conclude that texts in graphical business process models represent a specific type of texts that is worth to be studied on its own. We think that our work is a step towards better automatic analysis of business process models because understanding the actual meaning of activity labels is a prerequisite for detecting certain types of modelling problems.
HECTOR: A Hybrid TExt SimplifiCation TOol for Raw Texts in French
Authors: Amalia Todirascu, Rodrigo Wilkens, Eva Rolin, Thomas François, Delphine Bernhard, Núria Gala
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Lexical Simplification, Text Simplification, Word Embeddings

Reducing the complexity of texts by applying an Automatic Text Simplification (ATS) system has been sparking interest inthe area of Natural Language Processing (NLP) for several years and a number of methods and evaluation campaigns haveemerged targeting lexical and syntactic transformations. In recent years, several studies exploit deep learning techniques basedon very large comparable corpora. Yet the lack of large amounts of corpora (original-simplified) for French has been hinderingthe development of an ATS tool for this language. In this paper, we present our system, which is based on a combination ofmethods relying on word embeddings for lexical simplification and rule-based strategies for syntax and discourse adaptations. We present an evaluation of the lexical, syntactic and discourse-level simplifications according to automatic and humanevaluations. We discuss the performances of our system at the lexical, syntactic, and discourse levels
AiRO - an Interactive Learning Tool for Children at Risk of Dyslexia
Authors: Peter Juel Henrichsen, Stine Fuglsang Engmose
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Speech Synthesis

This paper presents the AiRO learning tool, which is designed for use in classrooms and homes by children at risk of developing dyslexia. The tool is based on the client-server architecture with a graphical and auditive front end (providing the interaction with the learner) and all NLP-related components located at the back end (analysing the pupil’s input, deciding on the system’s response, preparing speech synthesis and other feedback, logging the pupil’s performance etc). AiRO software consists of independent modules for easy maintenance, e.g., upgrading the didactics or preparing AiROs for other languages. This paper also reports on our first tests ‘in vivo’ (November 2021) with 49 pupils (aged 6). The subjects completed 16 AiRO sessions over a four-week period. The subjects were pre- and post-tested on spelling and reading. The experimental group significantly out-performed the control group, suggesting that a new IT-supported teaching strategy may be within reach. A collection of AiRO resources (language materials, software, synthetic voice) are available as open source. At LREC, we shall present a demo of the AiRO learning tool.
Creating a Basic Language Resource Kit for Faroese
Authors: Annika Simonsen, Sandra Saxov Lamhauge, Iben Nyholm Debess, Peter Juel Henrichsen
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Diversity

The biggest challenges we face in developing LR and LT for Faroese is the lack of existing resources. A few resources already exist for Faroese, but many of them are either of insufficient size and quality or are not easily accessible. Therefore, the Faroese ASR project, Ravnur, set out to make a BLARK for Faroese. The BLARK is still in the making, but many of its resources have already been produced or collected. The LR status is framed by mentioning existing LR of relevant size and quality. The specific components of the BLARK are presented as well as the working principles behind the BLARK. The BLARK will be a pillar in Faroese LR, being relatively substantial in both size, quality, and diversity. It will be open-source, inviting other small languages to use it as an inspiration to create their own BLARK. We comment on the faulty yet sprouting LT situation in the Faroe Islands. The LR and LT challenges are not solved with just a BLARK. Some initiatives are therefore proposed to better the prospects of Faroese LT. The open-source principle of the project should facilitate further development.
Developing a Spell and Grammar Checker for Icelandic using an Error Corpus
Authors: Hulda Óladóttir, Þórunn Arnardóttir, Anton Ingason, Vilhjálmur Þorsteinsson
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Grammatical Error Correction, Sentence

A lack of datasets for spelling and grammatical error correction in Icelandic, along with language-specific issues, has caused a dearth of spell and grammar checking systems for the language. We present the first open-source spell and grammar checking tool for Icelandic, using an error corpus at all stages. This error corpus was in part created to aid in the development of the tool. The system is built with a rule-based tool stack comprising a tokenizer, a morphological tagger, and a parser. For token-level error annotation, tokenization rules, word lists, and a trigram model are used in error detection and correction. For sentence-level error annotation, we use specific error grammar rules in the parser as well as regex-like patterns to search syntax trees. The error corpus gives valuable insight into the errors typically made when Icelandic text is written, and guided each development phase in a test-driven manner. We assess the system’s performance with both automatic and human evaluation, using the test set in the error corpus as a reference in the automatic evaluation. The data in the error corpus development set proved useful in various ways for error detection and correction.
Automating Idea Unit Segmentation and Alignment for Assessing Reading Comprehension via Summary Protocol Analysis
Authors: Marcello Gecchele, Hiroaki Yamada, Takenobu Tokunaga, Yasuyo Sawaki, Mika Ishizuka
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Reading Comprehension, Segmentation, Word Embeddings

In this paper, we approach summary evaluation from an applied linguistics (AL) point of view. We provide computational tools to AL researchers to simplify the process of Idea Unit (IU) segmentation. The IU is a segmentation unit that can identify chunks of information. These chunks can be compared across documents to measure the content overlap between a summary and its source text. We propose a full revision of the annotation guidelines to allow machine implementation. The new guideline also improves the inter-annotator agreement, rising from 0.547 to 0.785 (Cohen’s Kappa). We release L2WS 2021, a IU gold standard corpus composed of 40 manually annotated student summaries. We propose IUExtract; i.e. the first automatic segmentation algorithm based on the IU. The algorithm was tested over the L2WS 2021 corpus. Our results are promising, achieving a precision of 0.789 and a recall of 0.844. We tested an existing approach to IU alignment via word embeddings with the state of the art model SBERT. The recorded precision for the top 1 aligned pair of IUs was 0.375. We deemed this result insufficient for effective automatic alignment. We propose “SAT”, an online tool to facilitate the collection of alignment gold standards for future training.
IRAC: A Domain-Specific Annotated Corpus of Implicit Reasoning in Arguments
Authors: Keshav Singh, Naoya Inoue, Farjana Sultana Mim, Shoichi Naito, Kentaro Inui
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

The task of implicit reasoning generation aims to help machines understand arguments by inferring plausible reasonings (usually implicit) between argumentative texts. While this task is easy for humans, machines still struggle to make such inferences and deduce the underlying reasoning. To solve this problem, we hypothesize that as human reasoning is guided by innate collection of domain-specific knowledge, it might be beneficial to create such a domain-specific corpus for machines. As a starting point, we create the first domain-specific resource of implicit reasonings annotated for a wide range of arguments, which can be leveraged to empower machines with better implicit reasoning generation ability. We carefully design an annotation framework to collect them on a large scale through crowdsourcing and show the feasibility of creating a such a corpus at a reasonable cost and high-quality. Our experiments indicate that models trained with domain-specific implicit reasonings significantly outperform domain-general models in both automatic and human evaluations. To facilitate further research towards implicit reasoning generation in arguments, we present an in-depth analysis of our corpus and crowdsourcing methodology, and release our materials (i.e., crowdsourcing guidelines and domain-specific resource of implicit reasonings).
Conversational Speech Recognition Needs Data? Experiments with Austrian German
Authors: Julian Linke, Philip N. Garner, Gernot Kubin, Barbara Schuppler
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, speech-recognition, Speech Recognition

Conversational speech represents one of the most complex of automatic speech recognition (ASR) tasks owing to the high inter-speaker variation in both pronunciation and conversational dynamics. Such complexity is particularly sensitive to low-resourced (LR) scenarios. Recent developments in self-supervision have allowed such scenarios to take advantage of large amounts of otherwise unrelated data. In this study, we characterise an (LR) Austrian German conversational task. We begin with a non-pre-trained baseline and show that fine-tuning of a model pre-trained using self-supervision leads to improvements consistent with those in the literature; this extends to cases where a lexicon and language model are included. We also show that the advantage of pre-training indeed arises from the larger database rather than the self-supervision. Further, by use of a leave-one-conversation out technique, we demonstrate that robustness problems remain with respect to inter-speaker and inter-conversation variation. This serves to guide where future research might best be focused in light of the current state-of-the-art.
Building a Dataset for Automatically Learning to Detect Questions Requiring Clarification
Authors: Ivano Lauriola, Kevin Small, Alessandro Moschitti
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Question Answering

Question Answering (QA) systems aim to return correct and concise answers in response to user questions. QA research generally assumes all questions are intelligible and unambiguous, which is unrealistic in practice as questions frequently encountered by virtual assistants are ambiguous or noisy. In this work, we propose to make QA systems more robust via the following two-step process: (1) classify if the input question is intelligible and (2) for such questions with contextual ambiguity, return a clarification question. We describe a new open-domain clarification corpus containing user questions sampled from Quora, which is useful for building machine learning approaches to solving these tasks.
The ALPIN Sentiment Dictionary: Austrian Language Polarity in Newspapers
Authors: Thomas Kolb, Sekanina Katharina, Bettina Manuela Johanna Kern, Julia Neidhardt, Tanja Wissik, Andreas Baumann
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Diversity, Sentiment Analysis

This paper introduces the Austrian German sentiment dictionary ALPIN to account for the lack of resources for dictionary-based sentiment analysis in this specific variety of German, which is characterized by lexical idiosyncrasies that also affect word sentiment. The proposed language resource is based on Austrian news media in the field of politics, an austriacism list based on different resources and a posting data set based on a popular Austrian news media. Different resources are used to increase the diversity of the resulting language resource. Extensive crowd-sourcing is performed followed by evaluation and automatic conversion into sentiment scores. We show that crowd-sourcing enables the creation of a sentiment dictionary for the Austrian German domain. Additionally, the different parts of the sentiment dictionary are evaluated to show their impact on the resulting resource. Furthermore, the proposed dictionary is utilized in a web application and available for future research and free to use for anyone.
Text Classification and Prediction in the Legal Domain
Authors: Minh-Quoc Nghiem, Paul Baylis, André Freitas, Sophia Ananiadou
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Classification, Management, Prediction, text-classification, Text Classification

We present a case study on the application of text classification and legal judgment prediction for flight compensation. We combine transformer-based classification models to classify responses from airlines and incorporate text data with other data types to predict a legal claim being successful. Our experimental evaluations show that our models achieve consistent and significant improvements over baselines and even outperformed human prediction when predicting a claim being successful. These models were integrated into an existing claim management system, providing substantial productivity gains for handling the case lifecycle, currently supporting several thousands of monthly processes.
Morphological Complexity of Children Narratives in Eight Languages
Authors: Gordana Hržica, Chaya Liebeskind, Kristina Š. Despot, Olga Dontcheva-Navratilova, Laura Kamandulytė-Merfeldienė, Sara Košutar, Matea Kramarić, Giedrė Valūnaitė Oleškevičienė
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

The aim of this study was to compare the morphological complexity in a corpus representing the language production of younger and older children across different languages. The language samples were taken from the Frog Story subcorpus of the CHILDES corpora, which comprises oral narratives collected by various researchers between 1990 and 2005. We extracted narratives by typically developing, monolingual, middle-class children. Additionally, samples of Lithuanian language, collected according to the same principles, were added. The corpus comprises 249 narratives evenly distributed across eight languages: Croatian, English, French, German, Italian, Lithuanian, Russian and Spanish. Two subcorpora were formed for each language: a younger children corpus and an older children corpus. Four measures of morphological complexity were calculated for each subcorpus: Bane, Kolmogorov, Word entropy and Relative entropy of word structure. The results showed that younger children corpora had lower morphological complexity than older children corpora for all four measures for Spanish and Russian. Reversed results were obtained for English and French, and the results for the remaining four languages showed variation. Relative entropy of word structure proved to be indicative of age differences. Word entropy and relative entropy of word structure show potential to demonstrate typological differences.
EXPRES Corpus for A Field-specific Automated Exploratory Study of L2 English Expert Scientific Writing
Authors: Ana-Maria Bucur, Madalina Chitez, Valentina Muresan, Andreea Dinca, Roxana Rogobete
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Articles

Field Specific Expert Scientific Writing in English as a Lingua Franca is essential for the effective research networking and dissemination worldwide. Extracting the linguistic profile of the research articles written in L2 English can help young researchers and expert scholars in various disciplines adapt to the scientific writing norms of their communities of practice. In this exploratory study, we present and test an automated linguistic assessment model that includes features relevant for the cross-disciplinary second language framework: Text Complexity Analysis features, such as Syntactic and Lexical Complexity, and Field Specific Academic Word Lists. We analyse how these features vary across four disciplinary fields (Economics, IT, Linguistics and Political Science) in a corpus of L2-English Expert Scientific Writing, part of the EXPRES corpus (Corpus of Expert Writing in Romanian and English). The variation in field specific writing is also analysed in groups of linguistic features extracted from the higher visibility (Hv) versus lower visibility (Lv) journals. After applying lexical sophistication, lexical variation and syntactic complexity formulae, significant differences between disciplines were identified, mainly that research articles from Lv journals have higher lexical complexity, but lower syntactic complexity than articles from Hv journals; while academic vocabulary proved to have discipline specific variation.
Complex Labelling and Similarity Prediction in Legal Texts: Automatic Analysis of France’s Court of Cassation Rulings
Authors: Thibault Charmet, Inès Cherichi, Matthieu Allain, Urszula Czerwinska, Amaury Fouret, Benoît Sagot, Rachel Bawden
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

Detecting divergences in the applications of the law (where the same legal text is applied differently by two rulings) is an important task. It is the mission of the French Cour de Cassation. The first step in the detection of divergences is to detect similar cases, which is currently done manually by experts. They rely on summarised versions of the rulings (syntheses and keyword sequences), which are currently produced manually and are not available for all rulings. There is also a high degree of variability in the keyword choices and the level of granularity used. In this article, we therefore aim to provide automatic tools to facilitate the search for similar rulings. We do this by (i) providing automatic keyword sequence generation models, which can be used to improve the coverage of the analysis, and (ii) providing measures of similarity based on the available texts and augmented with predicted keyword sequences. Our experiments show that the predictions improve correlations of automatically obtained similarities against our specially colelcted human judgments of similarity.
Cyrillic-MNIST: a Cyrillic Version of the MNIST Dataset
Authors: Bolat Tleubayev, Zhanel Zhexenova, Kenessary Koishybay, Anara Sandygulova
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

This paper presents a new handwritten dataset, Cyrillic-MNIST, a Cyrillic version of the MNIST dataset, comprising of 121,234 samples of 42 Cyrillic letters. The performance of Cyrillic-MNIST is evaluated using standard deep learning approaches and is compared to the Extended MNIST (EMNIST) dataset. The dataset is available at https://github.com/bolattleubayev/cmnist
gaBERT — an Irish Language Model
Authors: James Barry, Joachim Wagner, Lauren Cassidy, Alan Cowap, Teresa Lynn, Abigail Walsh, Mícheál J. Ó Meachair, Jennifer Foster
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Language Modeling, Language Modelling, model

The BERT family of neural language models have become highly popular due to their ability to provide sequences of text with rich context-sensitive token encodings which are able to generalise well to many NLP tasks. We introduce gaBERT, a monolingual BERT model for the Irish language. We compare our gaBERT model to multilingual BERT and the monolingual Irish WikiBERT, and we show that gaBERT provides better representations for a downstream parsing task. We also show how different filtering criteria, vocabulary size and the choice of subword tokenisation model affect downstream performance. We compare the results of fine-tuning a gaBERT model with an mBERT model for the task of identifying verbal multiword expressions, and show that the fine-tuned gaBERT model also performs better at this task. We release gaBERT and related code to the community.
A Dataset of Offensive German Language Tweets Annotated for Speech Acts
Authors: Melina Plakidis, Georg Rehm
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Sentence

We present a dataset consisting of German offensive and non-offensive tweets, annotated for speech acts. These 600 tweets are a subset of the dataset by Struß et al. (2019) and comprises three levels of annotation, i.e., six coarse-grained speech acts, 23 fine-grained speech acts and 14 different sentence types. Furthermore, we provide an evaluation in both qualitative and quantitative terms. The dataset is made publicly available under a CC-BY-4.0 license.
Tracing Syntactic Change in the Scientific Genre: Two Universal Dependency-parsed Diachronic Corpora of Scientific English and German
Authors: Marie-Pauline Krielke, Luigi Talamo, Mahmoud Fawzi, Jörg Knappen
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Sentence

We present two comparable diachronic corpora of scientific English and German from the Late Modern Period (17th c.–19th c.) annotated with Universal Dependencies. We describe several steps of data pre-processing and evaluate the resulting parsing accuracy showing how our pre-processing steps significantly improve output quality. As a sanity check for the representativity of our data, we conduct a case study comparing previously gained insights on grammatical change in the scientific genre with our data. Our results reflect the often reported trend of English scientific discourse towards heavy noun phrases and a simplification of the sentence structure (Halliday, 1988; Halliday and Martin, 1993; Biber and Gray, 2011; Biber and Gray, 2016). We also show that this trend applies to German scientific discourse as well. The presented corpora are valuable resources suitable for the contrastive analysis of syntactic diachronic change in the scientific genre between 1650 and 1900. The presented pre-processing procedures and their evaluations are applicable to other languages and can be useful for a variety of Natural Language Processing tasks such as syntactic parsing.