pwc-archive/papers-with-abstracts huggingface dataset:
Describing Language Variation in the Colophons of Armenian Manuscripts
Authors: Bastien Kindt, Emmanuel Van Elverdinghe
Date: 2022-06-01 00:00:00
Proceeding: DigitAm (LREC) 2022 6
Tasks: Cultural Vocal Bursts Intensity Prediction, Lemmatization, POS, POS Tagging

The colophons of Armenian manuscripts constitute a large textual corpus spanning a millennium of written culture. These texts are highly diverse and rich in terms of linguistic variation. This poses a challenge to NLP tools, especially considering the fact that linguistic resources designed or suited for Armenian are still scarce. In this paper, we deal with a sub-corpus of colophons written to commemorate the rescue of a manuscript and dating from 1286 to ca. 1450, a thematic group distinguished by a particularly high concentration of words exhibiting linguistic variation. The text is processed (lemmatization, POS-tagging, and inflectional tagging) using the tools of the GREgORI Project and evaluated. Through a selection of examples, we show how variation is dealt with at each linguistic level (phonology, orthography, flexion, vocabulary, syntax). Complex variation, at the level of tokens or lemmata, is considered as well. The results of this work are used to enrich and refine the linguistic resources of the GREgORI project, which in turn benefits the processing of other texts.
Eastern Armenian National Corpus: State of the Art and Perspectives
Authors: Victoria Khurshudyan, Timofey Arkhangelskiy, Misha Daniel, Vladimir Plungian, Dmitri Levonian, Alex Polyakov, Sergei Rubakov
Date: 2022-06-01 00:00:00
Proceeding: DigitAm (LREC) 2022 6

Eastern Armenian National Corpus (EANC) is a comprehensive corpus of Modern Eastern Armenian with about 110 million tokens, covering written and oral discourses from the mid-19th century to the present. The corpus is provided with morphological, semantic and metatext annotation, as well as English translations. EANC is open access and available at www.eanc.net.
Towards a Unified ASR System for the Armenian Standards
Authors: Samuel Chakmakjian, Ilaine Wang
Date: 2022-06-01 00:00:00
Proceeding: DigitAm (LREC) 2022 6
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Armenian is a traditionally under-resourced language, which has seen a recent uptick in interest in the development of its tools and presence in the digital domain. Some of this recent interest has centred around the development of Automatic Speech Recognition (ASR) technologies. However, the language boasts two standard variants which diverge on multiple typological and structural levels. In this work, we examine some of the available bodies of data for ASR construction, present the challenges in the processing of these data and propose a methodology going forward.
Exploring the GLIDE model for Human Action Effect Prediction
Authors: Fangjun Li, David C. Hogg, Anthony G. Cohn
Date: 2022-06-01 00:00:00
Proceeding: PVLAM (LREC) 2022 6
Tasks: Prediction

We address the following action-effect prediction task. Given an image depicting an initial state of the world and an action expressed in text, predict an image depicting the state of the world following the action. The prediction should have the same scene context as the input image. We explore the use of the recently proposed GLIDE model for performing this task. GLIDE is a generative neural network that can synthesize (inpaint) masked areas of an image, conditioned on a short piece of text. Our idea is to mask-out a region of the input image where the effect of the action is expected to occur. GLIDE is then used to inpaint the masked region conditioned on the required action. In this way, the resulting image has the same background context as the input image, updated to show the effect of the action. We give qualitative results from experiments using the EPIC dataset of ego-centric videos labelled with actions.
Do Multimodal Emotion Recognition Models Tackle Ambiguity?
Authors: Hélène Tran, Issam Falih, Xavier Goblet, Engelbert Mephu Nguifo
Date: 2022-06-01 00:00:00
Proceeding: PVLAM (LREC) 2022 6
Tasks: Emotion Recognition, Multimodal Emotion Recognition

Most databases used for emotion recognition assign a single emotion to data samples. This does not match with the complex nature of emotions: we can feel a wide range of emotions throughout our lives with varying degrees of intensity. We may even experience multiple emotions at once. Furthermore, each person physically expresses emotions differently, which makes emotion recognition even more challenging: we call this emotional ambiguity. This paper investigates the problem as a review of ambiguity in multimodal emotion recognition models. To lay the groundwork, the main representations of emotions along with solutions for incorporating ambiguity are described, followed by a brief overview of ambiguity representation in multimodal databases. Thereafter, only models trained on a database that incorporates ambiguity have been studied in this paper. We conclude that although databases provide annotations with ambiguity, most of these models do not fully exploit them, showing that there is still room for improvement in multimodal emotion recognition systems.
Development of a MultiModal Annotation Framework and Dataset for Deep Video Understanding
Authors: Erika Loc, Keith Curtis, George Awad, Shahzad Rajput, Ian Soboroff
Date: 2022-06-01 00:00:00
Proceeding: PVLAM (LREC) 2022 6
Tasks: Knowledge Graphs, Video Understanding

In this paper we introduce our approach and methods for collecting and annotating a new dataset for deep video understanding. The proposed dataset is composed of 3 seasons (15 episodes) of the BBC Land Girls TV Series in addition to 14 Creative Common movies with total duration of 28.5 hr. The main contribution of this paper is a novel annotation framework on the movie and scene levels to support an automatic query generation process that can capture the high-level movie features (e.g. how characters and locations are related to each other) as well as fine grained scene-level features (e.g. character interactions, natural language descriptions, and sentiments). Movie-level annotations include constructing a global static knowledge graph (KG) to capture major relationships, while the scene-level annotations include constructing a sequence of knowledge graphs (KGs) to capture fine-grained features. The annotation framework supports generating multiple query types. The objective of the framework is to provide a guide to annotating long duration videos to support tasks and challenges in the video and multimedia understanding domains. These tasks and challenges can support testing automatic systems on their ability to learn and comprehend a movie or long video in terms of actors, entities, events, interactions and their relationship to each other.
Cognitive States and Types of Nods
Authors: Taiga Mori, Kristiina Jokinen, Yasuharu Den
Date: 2022-06-01 00:00:00
Proceeding: PVLAM (LREC) 2022 6

In this paper we will study how different types of nods are related to the cognitive states of the listener. The distinction is made between nods with movement starting upwards (up-nods) and nods with movement starting downwards (down-nods) as well as between single or repetitive nods. The data is from Japanese multiparty conversations, and the results accord with the previous findings indicating that up-nods are related to the change in the listener’s cognitive state after hearing the partner’s contribution, while down-nods convey the meaning that the listener’s cognitive state is not changed.
Examining the Effects of Language-and-Vision Data Augmentation for Generation of Descriptions of Human Faces
Authors: Nikolai Ilinykh, Rafal Černiavski, Eva Elžbieta Sventickaitė, Viktorija Buzaitė, Simon Dobnik
Date: 2022-06-01 00:00:00
Proceeding: PVLAM (LREC) 2022 6
Tasks: Caption Generation, Data Augmentation, Image Captioning

We investigate how different augmentation techniques on both textual and visual representations affect the performance of the face description generation model. Specifically, we provide the model with either original images, sketches of faces, facial composites or distorted images. In addition, on the language side, we experiment with different methods to augment the original dataset with paraphrased captions, which are semantically equivalent to the original ones, but differ in terms of their form. We also examine if augmenting the dataset with descriptions from a different domain (e.g., image captions of real-world images) has an effect on the performance of the models. We train models on different combinations of visual and linguistic features and perform both (i) automatic evaluation of generated captions and (ii) examination of how useful different visual features are for the task of facial feature classification. Our results show that although original images encode the best possible representation for the task, the model trained on sketches can still perform relatively well. We also observe that augmenting the dataset with descriptions from a different domain can boost performance of the model. We conclude that face description generation systems are more susceptible to language rather than vision data augmentation. Overall, we demonstrate that face caption generation models display a strong imbalance in the utilisation of language and vision modalities, indicating a lack of proper information fusion. We also describe ethical implications of our study and argue that future work on human face description generation should create better, more representative datasets.
The NIEUW Project: Developing Language Resources through Novel Incentives
Authors: James Fiumara, Christopher Cieri, Mark Liberman, Chris Callison-Burch, Jonathan Wright, Robert Parker
Date: 2022-06-01 00:00:00
Proceeding: NIDCP (LREC) 2022 6

This paper provides an overview and update on the Linguistic Data Consortium’s (LDC) NIEUW (Novel Incentives and Workflows) project supported by the National Science Foundation and part of LDC’s larger goal of improving the cost, variety, scale, and quality of language resources available for education, research, and technology development. NIEUW leverages the power of novel incentives to elicit linguistic data and annotations from a wide variety of contributors including citizen scientists, game players, and language students and professionals. In order to align appropriate incentives with the various contributors, LDC has created three distinct web portals to bring together researchers and other language professionals with participants best suited to their project needs. These portals include LanguageARC designed for citizen scientists, Machina Pro Linguistica designed for students and language professionals, and LingoBoingo designed for game players. The design, interface, and underlying tools for each web portal were developed to appeal to the different incentives and motivations of their respective target audiences.
Use of a Citizen Science Platform for the Creation of a Language Resource to Study Bias in Language Models for French: A Case Study
Authors: Karën Fort, Aurélie Névéol, Yoann Dupont, Julien Bezançon
Date: 2022-06-01 00:00:00
Proceeding: NIDCP (LREC) 2022 6
Tasks: Fairness, Translation

There is a growing interest in the evaluation of bias, fairness and social impact of Natural Language Processing models and tools. However, little resources are available for this task in languages other than English. Translation of resources originally developed for English is a promising research direction. However, there is also a need for complementing translated resources by newly sourced resources in the original languages and social contexts studied. In order to collect a language resource for the study of biases in Language Models for French, we decided to resort to citizen science. We created three tasks on the LanguageARC citizen science platform to assist with the translation of an existing resource from English into French as well as the collection of complementary resources in native French. We successfully collected data for all three tasks from a total of 102 volunteer participants. Participants from different parts of the world contributed and we noted that although calls sent to mailing lists had a positive impact on participation, some participants pointed barriers to contributions due to the collection platform.
Fearless Steps APOLLO: Advanced Naturalistic Corpora Development
Authors: John H.L. Hansen, Aditya Joglekar, Szu-Jui Chen, Meena Chandra Shekar, Chelzy Belitz
Date: 2022-06-01 00:00:00
Proceeding: NIDCP (LREC) 2022 6

In this study, we present the Fearless Steps APOLLO Community Resource, a collection of audio and corresponding meta-data diarized from the NASA Apollo Missions. Massive naturalistic speech data which is time-synchronized, without any human subject privacy constraints is very rare and difficult to organize, collect, and deploy. The Apollo Missions Audio is the largest collection of multi-speaker multi-channel data, where over 600 personnel are communicating over multiple missions to achieve strategic space exploration goals. A total of 12 manned missions over a six-year period produced extensive 30-track 1-inch analog tapes containing over 150,000 hours of audio. This presents the wider research community a unique opportunity to extract multi-modal knowledge in speech science, team cohesion and group dynamics, and historical archive preservation. We aim to make this entire resource and supporting speech technology meta-data creation publicly available as a Community Resource for the development of speech and behavioral science. Here we present the development of this community resource, our outreach efforts, and technological developments resulting from this data. We finally discuss the planned future directions for this community resource.
Creating Mexican Spanish Language Resources through the Social Service Program
Authors: Carlos Daniel Hernandez Mena, Ivan Vladimir Meza Ruiz
Date: 2022-06-01 00:00:00
Proceeding: NIDCP (LREC) 2022 6

This work presents the path toward the creation of eight Spoken Language Resources under the umbrella of the Mexican Social Service national program. This program asks undergraduate students to donate time and work for the benefit of their society as a requirement to receive their degree. The program has thousands of options for the students who enroll. We show how we created a program which has resulted in the creation of open language resources which now are freely available in different repositories. We estimate that this exercise is equivalent to a budget of more than half a million US dollars. However, since the program is based on retribution from the students to their communities there has not been a necessity of a financial budget.
Fictionary-Based Games for Language Resource Creation
Authors: Steinunn Rut Friðriksdóttir, Hafsteinn Einarsson
Date: 2022-06-01 00:00:00
Proceeding: NIDCP (LREC) 2022 6

In this paper, we present a novel approach to data collection for natural language processing (NLP), linguistic research and lexicographic work. Using the parlor game Fictionary as a framework, data can be crowd-sourced in a gamified manner, which carries the potential of faster, cheaper and better data when compared to traditional methods due to the engaging and competitive nature of the game. To improve data quality, the game includes a built-in review process where players review each other’s data and evaluate its quality. The paper proposes several games that can be used within this framework, and explains the value of the data generated by their use. These proposals include games that collect named entities along with their corresponding type tags, question-answer pairs, translation pairs and neologism, to name only a few. We are currently working on a digital platform that will host these games in Icelandic but wish to open the discussion around this topic and encourage other researchers to explore their own versions of the proposed games, all of which are language-independent.
Using Mixed Incentives to Document Xi’an Guanzhong
Authors: Juhong Zhan, Yue Jiang, Christopher Cieri, Mark Liberman, Jiahong Yuan, Yiya Chen, Odette Scharenborg
Date: 2022-06-01 00:00:00
Proceeding: NIDCP (LREC) 2022 6

This paper describes our use of mixed incentives and the citizen science portal LanguageARC to prepare, collect and quality control a large corpus of object namings for the purpose of providing speech data to document the under-represented Guanzhong dialect of Chinese spoken in the Shaanxi province in the environs of Xi’an.
Crowdsourced Participants’ Accuracy at Identifying the Social Class of Speakers from South East England
Authors: Amanda Cole
Date: 2022-06-01 00:00:00
Proceeding: NIDCP (LREC) 2022 6

Five participants, each located in distinct locations (USA, Canada, South Africa, Scotland and (South East) England), identified the self-determined social class of a corpus of 227 speakers (born 1986–2001; from South East England) based on 10-second passage readings. This pilot study demonstrates the potential for using crowdsourcing to collect sociolinguistic data, specifically using LanguageARC, especially when geographic spread of participants is desirable but not easily possible using traditional fieldwork methods. Results show that, firstly, accuracy at identifying social class is relatively low when compared to other factors, including when the same speech stimuli were used (e.g., ethnicity: Cole 2020). Secondly, participants identified speakers’ social class significantly better than chance for a three-class distinction (working, middle, upper) but not for a six-class distinction. Thirdly, despite some differences in performance, the participant located in South East England did not perform significantly better than other participants, suggesting that the participant’s presumed greater familiarity with sociolinguistic variation in the region may not have been advantageous. Finally, there is a distinction to be made between participants’ ability to pinpoint a speaker’s exact social class membership and their ability to identify the speaker’s relative class position. This paper discusses the role of social identification tasks in illuminating how speech is categorised and interpreted.
About the Applicability of Combining Implicit Crowdsourcing and Language Learning for the Collection of NLP Datasets
Authors: Verena Lyding, Lionel Nicolas, Alexander König
Date: 2022-06-01 00:00:00
Proceeding: NIDCP (LREC) 2022 6

In this article, we present a recent trend of approaches, hereafter referred to as Collect4NLP, and discuss its applicability. Collect4NLP-based approaches collect inputs from language learners through learning exercises and aggregate the collected data to derive linguistic knowledge of expert quality. The primary purpose of these approaches is to improve NLP resources, however sincere concern with the needs of learners is crucial for making Collect4NLP work. We discuss the applicability of Collect4NLP approaches in relation to two perspectives. On the one hand, we compare Collect4NLP approaches to the two crowdsourcing trends currently most prevalent in NLP, namely Crowdsourcing Platforms (CPs) and Games-With-A-Purpose (GWAPs), and identify strengths and weaknesses of each trend. By doing so we aim to highlight particularities of each trend and to identify in which kind of settings one trend should be favored over the other two. On the other hand, we analyze the applicability of Collect4NLP approaches to the production of different types of NLP resources. We first list the types of NLP resources most used within its community and second propose a set of blueprints for mapping these resources to well-established language learning exercises as found in standard language learning textbooks.
The Influence of Intrinsic and Extrinsic Motivation on the Creation of Language Resources in a Citizen Linguistics Project about Lexicography
Authors: Barbara Heinisch
Date: 2022-06-01 00:00:00
Proceeding: NIDCP (LREC) 2022 6

In the field of citizen linguistics, various initiatives are aimed at the creation of language resources by members of the public. To recruit and retain these participants different incentives informed by different motivations, extrinsic and intrinsic ones, play a role at different project stages. Illustrated by a project in the field of lexicography which draws on the extrinsic and/or intrinsic motivation of participants, the complexity of providing the ‘right’ incentives is addressed. This complexity does not only surface when considering cultural differences and the heterogeneity of the motivations participants might have but also through the changing motivations over time. Here, identifying target groups may help to guide recruitment, retention and dissemination activities. In addition, continuous adaptations may be required during the course of the project to strike a balance between necessary and feasible incentives.
Challenges in Creating a Representative Corpus of Romanian Micro-Blogging Text
Authors: Vasile Pais, Maria Mitrofan, Verginica Barbu Mititelu, Elena Irimia, Roxana Micu, Carol Luca Gasan
Date: 2022-06-01 00:00:00
Proceeding: CMLC (LREC) 2022 6

Following the successful creation of a national representative corpus of contemporary Romanian language, we turned our attention to the social media text, as present in micro-blogging platforms. In this paper, we present the current activities as well as the challenges faced when trying to apply existing tools (for both annotation and indexing) to a Romanian language micro-blogging corpus. These challenges are encountered at all annotation levels, including tokenization, and at the indexing stage. We consider that existing tools for Romanian language processing must be adapted to recognize features such as emoticons, emojis, hashtags, unusual abbreviations, elongated words (commonly used for emphasis in micro-blogging), multiple words joined together (within oroutside hashtags), and code-mixed text.
Exhaustive Indexing of PubMed Records with Medical Subject Headings
Authors: Modest von Korff
Date: 2022-06-01 00:00:00
Proceeding: CMLC (LREC) 2022 6

With fourteen million publication records the PubMed database is one of the largest repositories in medical science. Analysing this database to relate biological targets to diseases is an important task in pharmaceutical research. We developed a software tool, MeSHTreeIndexer, for indexing the PubMed medical literature with disease terms. The disease terms were taken from the Medical Subject Heading (MeSH) Terms compiled by the National Institutes of Health (NIH) of the US. In a first semi-automatic step we identified about 5’900 terms as disease related. The MeSH terms contain so-called entry points that are synonymously used for the terms. We created an inverted index for these 5’900 MeSH terms and their 58’000 entry points. From the PubMed database fourteen million publication records were stored in Lucene. These publication records were tagged by the inverted MeSH term index. In this contribution we demonstrate that our approach provided a significant higher enrichment in MeSH terms than the indexing of the PubMed records by the NIH themselves. Manual control proved that our enrichment is meaningful. Our software was written in Java and is available as open source.
UDeasy: a Tool for Querying Treebanks in CoNLL-U Format
Authors: Luca Brigada Villa
Date: 2022-06-01 00:00:00
Proceeding: CMLC (LREC) 2022 6

Many tools are available to query a dependency treebank, but they require the users to know a query language. In this paper I present UDeasy, an application whose main goal is to allow the users to easily query and extract patterns from a dependency treebank in CoNLL-U format.