pwc-archive/papers-with-abstracts huggingface dataset:
Investigating the Quality of Static Anchor Embeddings from Transformers for Under-Resourced Languages
Authors: Pranaydeep Singh, Orphee De Clercq, Els Lefever
Date: 2022-06-01 00:00:00
Proceeding: SIGUL (LREC) 2022 6
Tasks: Cross-Lingual Transfer

This paper reports on experiments for cross-lingual transfer using the anchor-based approach of Schuster et al. (2019) for English and a low-resourced language, namely Hindi. For the sake of comparison, we also evaluate the approach on three very different higher-resourced languages, viz. Dutch, Russian and Chinese. Initially designed for ELMo embeddings, we analyze the approach for the more recent BERT family of transformers for a variety of tasks, both mono and cross-lingual. The results largely prove that like most other cross-lingual transfer approaches, the static anchor approach is underwhelming for the low-resource language, while performing adequately for the higher resourced ones. We attempt to provide insights into both the quality of the anchors, and the performance for low-shot cross-lingual transfer to better understand this performance gap. We make the extracted anchors and the modified train and test sets available for future research at https://github.com/pranaydeeps/Vyaapak
Introducing YakuToolkit. Yakut Treebank and Morphological Analyzer.
Authors: Tatiana Merzhevich, Fabrício Ferraz Gerardi
Date: 2022-06-01 00:00:00
Proceeding: SIGUL (LREC) 2022 6

This poster presents the first publicly available treebank of Yakut, a Turkic language spoken in Russia, and a morphological analyzer for this language. The treebank was annotated following the Universal Dependencies (UD) framework and the mor- phological analyzer can directly access and use its data. Yakut is an under-represented language whose prominence can be raised by making reliably annotated data and NLP tools that could process it freely accessible. The publication of both the treebank and the analyzer serves this purpose with the prospect of evolving into a benchmark for the development of NLP online tools for other languages of the Turkic family in the future.
A Language Model for Spell Checking of Educational Texts in Kurdish (Sorani)
Authors: Roshna Abdulrahman, Hossein Hassani
Date: 2022-06-01 00:00:00
Proceeding: SIGUL (LREC) 2022 6
Tasks: Language Modeling, Language Modelling

Spell checkers are an integrated feature of most software applications handling text inputs. When we write an email or compile a report on a desktop or a smartphone editor, a spell checker could be activated that assists us to write more correctly. However, this assistance does not exist for all languages equally. The Kurdish language, which still is considered a less-resourced language, currently lacks spell checkers for its various dialects. We present a trigram language model for the Sorani dialect of the Kurdish language that is created using educational text. We also showcase a spell checker for the Sorani dialect of Kurdish that can assist in writing texts in the Persian/Arabic script. The spell checker was developed as a testing environment for the language model. Primarily, we use the probabilistic method and our trigram language model with Stupid Backoff smoothing for the spell checking algorithm. Our spell checker has been trained on the KTC (Kurdish Textbook Corpus) dataset. Hence the system aims at assisting spell checking in the related context. We test our approach by developing a text processing environment that checks for spelling errors on a word and context basis. It suggests a list of corrections for misspelled words. The developed spell checker shows 88.54% accuracy on the texts in the related context and it has an F1 score of 43.33%, and the correct suggestion has an 85% chance of being in the top three positions of the corrections.
ENRICH4ALL: A First Luxembourgish BERT Model for a Multilingual Chatbot
Authors: Dimitra Anastasiou
Date: 2022-06-01 00:00:00
Proceeding: SIGUL (LREC) 2022 6
Tasks: Chatbot, Language Modeling, Language Modelling, Machine Translation, Translation, Visual Dialog

Machine Translation (MT)-empowered chatbots are not established yet, however, we see an amazing future breaking language barriers and enabling conversation in multiple languages without time-consuming language model building and training, particularly for under-resourced languages. In this paper we focus on the under-resourced Luxembourgish language. This article describes the experiments we have done with a dataset containing administrative questions that we have manually created to offer BERT QA capabilities to a multilingual chatbot. The chatbot supports visual dialog flow diagram creation (through an interface called BotStudio) in which a dialog node manages the user question at a specific step. Dialog nodes can be matched to the user’s question by using a BERT classification model which labels the question with a dialog node label.
Toward a Semi-Automated Scoping Review of Virtual Human Smiles
Authors: Sharon Mozgai, Jade Winn, Cari Kaurloto, Andrew Leeds, Dirk Heylen, Arno Hartholt
Date: 2022-06-01 00:00:00
Proceeding: SmiLa (LREC) 2022 6

Smiles are a fundamental facial expression for successful human-agent communication. The growing number of publications in this domain presents an opportunity for future research and design to be informed by a scoping review of the extant literature. This semi-automated review expedites the first steps toward the mapping of Virtual Human (VH) smile research. This paper contributes an overview of the status quo of VH smile research, identifies research streams through cluster analysis, identifies prolific authors in the field, and provides evidence that a full scoping review is needed to synthesize the findings in the expanding domain of VH smile research. To enable collaboration, we provide full access to the refined VH smile dataset, key word and author word clouds, as well as interactive evidence maps.
Are You Smiling When I Am Speaking?
Authors: Auriane Boudin, Roxane Bertrand, Magalie Ochs, Philippe Blache, Stephane Rauzy
Date: 2022-06-01 00:00:00
Proceeding: SmiLa (LREC) 2022 6

The aim of this study is to investigate conversational feedbacks that contain smiles and laughs. Firstly, we propose a statistical analysis of smiles and laughs used as generic and specific feedbacks in a corpus of French talk-in-interaction. Our results show that smiles of low intensity are preferentially used to produce generic feedbacks while high intensity smiles and laughs are preferentially used to produce specific feedbacks. Secondly, based on a machine learning approach, we propose a hierarchical classification of feedback to automatically predict not only the presence/absence of a smile but, also the type of smiles according to an intensity-scale (low or high).
Gender Differences, Smiling, and Economic Negotiation Outcomes
Authors: Paulina Hiersch, Gary McKeown, Ioana Latu, Magdalena Rychlowska
Date: 2022-06-01 00:00:00
Proceeding: SmiLa (LREC) 2022 6

Research documents gender differences in nonverbal behavior and negotiation outcomes. Women tend to smile more often than men and men generally perform better in economic negotiation contexts. Among nonverbal behaviors, smiling can serve various social functions, from rewarding or appeasing others to conveying dominance, and could therefore be extremely useful in economic negotiations. However, smiling has hardly been studied in negotiation contexts. Here we examine links between smiling, gender, and negotiation outcomes. We analyze a corpus of video recordings of participant dyads during mock salary negotiations and test whether women smile more than men and if the amount of smiling can predict economic negotiation outcomes. Consistent with existing literature, women smiled more than men. There was no significant relationship between smiling and negotiation outcomes and gender did not predict negotiation performance. Exploratory analyses showed that expected negotiation outcomes, strongly correlated with actual outcomes, tended to be higher for men than for women. Implications for the gender pay gap and future research are discussed.
A Measure of the Smiling Synchrony in the Conversational Face-to-face Interaction Corpus PACO-CHEESE
Authors: Stéphane Rauzy, Mary Amoyal, Béatrice Priego-Valverde
Date: 2022-06-01 00:00:00
Proceeding: SmiLa (LREC) 2022 6

The smiling synchrony of the French audio-video conversational corpora “PACO” and “Cheese!” is investigated. The two corpora merged altogether last 6 hours and are made of 25 face-to-face dyadic interactions annotated following the 5 levels Smiling Intensity Scale proposed by Gironzetti et al. (2016). After introducing new indicators for characterizing synchrony phenomena, we find that almost all the 25 interactions of PACO-CHEESE show a strong and significant smiling synchrony behavior. We investigate in a second step the evolution of the synchrony parameters throughout the interaction. No effect is found and it appears rather that the smiling synchrony is present at the very start of the interaction and remains unchanged throughout the conversation.
Analysis of Co-Laughter Gesture Relationship on RGB Videos in Dyadic Conversation Context
Authors: Hugo Bohy, Ahmad Hammoudeh, Antoine Maiorca, Stéphane Dupont, Thierry Dutoit
Date: 2022-06-01 00:00:00
Proceeding: SmiLa (LREC) 2022 6
Tasks: Motion Synthesis

The development of virtual agents has enabled human-avatar interactions to become increasingly rich and varied. Moreover, an expressive virtual agent i.e. that mimics the natural expression of emotions, enhances social interaction between a user (human) and an agent (intelligent machine). The set of non-verbal behaviors of a virtual character is, therefore, an important component in the context of human-machine interaction. Laughter is not just an audio signal, but an intrinsic relationship of multimodal non-verbal communication, in addition to audio, it includes facial expressions and body movements. Motion analysis often relies on a relevant motion capture dataset, but the main issue is that the acquisition of such a dataset is expensive and time-consuming. This work studies the relationship between laughter and body movements in dyadic conversations between two interlocutors. The body movements were extracted from videos using deep learning based pose estimator model. We found that, in the explored NDC-ME dataset, a single statistical feature (i.e, the maximum value, or the maximum of Fourier transform) of a joint movement weakly correlates with laughter intensity by 30%. However, we did not find a direct correlation between audio features and body movements. We discuss about the challenges to use such dataset for the audio-driven co-laughter motion synthesis task.
Intergroup Bias in Smile Discrimination in Autism
Authors: Ruihan Wu, Antonia Hamilton, Sarah White
Date: 2022-06-01 00:00:00
Proceeding: SmiLa (LREC) 2022 6

Genuine and posed smiles are important social cues (Song, Over, & Carpenter, 2016). Autistic individuals struggle to reliably differentiate between them (Blampied, Johnston, Miles, & Liberty, 2010; Boraston, Corden, Miles, Skuse, & Blakemore, 2008), which may contribute to their difficulties in understanding others’ mental states. An intergroup bias has been found in non-autistic adults in identifying genuine from posed smiles (Young, 2017). This is the first study designed to investigate if autistic individuals would show a different pattern when differentiating smiles for in-groups and out-groups. Fifty-nine autistic adults were compared with forty non-autistic adults, matched on sex, age and nonverbal IQ. Roughly, half of each group were further randomly separated into two groups with a minimal group paradigm (adapted from Howard & Rothbart, 1980). There was no real difference between the groups, participants were primed to believe they were more similar to their in-groups. The ability to distinguish smiles was assessed on a 7-point Likert scale. We found both autism and non-autism groups rated genuine smiles more genuine than posed smiles and in-groups more genuine than out-groups. Even though both groups identified themselves more as in-group than out-group members, autistic individuals were less likely to than non-autistic individuals. However, autistic participants generally rated smiles as less genuine than non-autistic counterparts. These results indicate that autistic adults are capable of identifying genuine smiles from posed smiles, unlike previous findings; but they may be less convinced of the genuineness of others, which may affect their social communication thereafter. Importantly, autistic adults were equally influenced by social intergroup biases which has the potential to be used in interventions to alleviate their social difficulties in daily lives.
Inhalation Noises as Endings of Laughs in Conversational Speech
Authors: Jürgen Trouvain, Raphael Werner, Khiet Truong
Date: 2022-06-01 00:00:00
Proceeding: SmiLa (LREC) 2022 6

In this study we investigate the role of inhalation noises at the end of laughter events in two conversational corpora that provide relevant annotations. A re-annotation of the categories for laughter, silence and inbreath noises enabled us to see that inhalation noises terminate laughter events in the majority of all inspected laughs with a duration comparable to inbreath noises initiating speech phases. This type of corpus analysis helps to understand the mechanisms of audible respiratory activities in speaking vs. laughing in conversations.
Are There Any Body-movement Differences between Women and Men When They Laugh?
Authors: Ahmad Hammoudeh, Antoine Maiorca, Stéphane Dupont, Thierry Dutoit
Date: 2022-06-01 00:00:00
Proceeding: SmiLa (LREC) 2022 6
Tasks: Pose Estimation

Smiling differences between men and women have been studied in psychology. Women smile more than men although the expressiveness of women is not universally more across all facial actions. There are also body movement differences between women and men. For example, more open-body postures were reported for men, but are there any body-movement differences between men and women when they laugh? To investigate this question, we study body-movement signals extracted from recorded laughter videos using a deep learning pose estimation model. Initial results showed a higher Fourier Transform amplitude of thorax and shoulder movements for females while males had a higher Fourier transform amplitude of Elbow movement. The differences were not limited to a small frequency range but covered most of the frequency spectrum. However, further investigations are still needed.
Laughter During Cooperative and Competitive Games
Authors: Magdalena Rychlowska, Gary McKeown, Ian Sneddon, William Curran
Date: 2022-06-01 00:00:00
Proceeding: SmiLa (LREC) 2022 6
Tasks: General Knowledge

This exploratory study investigates the extent to which social context influences the frequency of laughter. In a within-subjects design, dyads of strangers played two simple laughter-inducing games in a cooperative and competitive setting, ostensibly to earn money individually and as a team. We examined the frequency of laughs produced in both settings. The analysis revealed that, the effects of cooperative versus competitive framing interacted with the game. Specifically, when playing a general knowledge quiz, participants tended to laugh more in the cooperative than in the competitive setting. However, the opposite was true when participants were asked to find a specific number of poker chips under time pressure. During this task participants laughed more in a competitive than in the cooperative setting. Further analyses revealed that familiarity with the task affected the amount of laughter differently for each of the two tasks. Playing the second round of the poker chips task was associated with a significant decreases in laughter frequency compared to the first round. This effect was less marked for the general knowledge quiz, where increased familiarity with the task in the second round led to more laughs in the cooperative, but not competitive setting. Together, the results highlight the flexibility of laughter as an interaction signal and illustrate the challenges of studying laughter in naturalistic settings.
Automatic Approach for Building Dataset of Citation Functions for COVID-19 Academic Papers
Authors: Setio Basuki, Masatoshi Tsuchiya
Date: 2022-06-01 00:00:00
Proceeding: LREC (LAW) 2022 6

This paper develops a new dataset of citation functions of COVID-19-related academic papers. Because the preparation of new labels of citation functions and building a new dataset requires much human effort and is time-consuming, this paper uses our previous citation functions that were built for the Computer Science (CS) domain, which consists of five coarse-grained labels and 21 fine-grained labels. This paper uses the COVID-19 Open Research Dataset (CORD-19) and extracts 99.6k random citing sentences from 10.1k papers. These citing sentences are categorized using the classification models built from the CS domain. The manually check on 475 random samples resulted accuracies of 76.6% and 70.2% on coarse-grained labels and fine-grained labels, respectively. The evaluation reveals three findings. First, two fine-grained labels experienced meaning shift while retaining the same idea. Second, the COVID-19 domain is dominated by statements highlighting the importance, cruciality, usefulness, benefit, consideration, etc. of certain topics for making sensible argumentation. Third, discussing State of The Arts (SOTA) in terms of their outperforming previous works in the COVID-19 domain is less popular compared to the CS domain. Our results will be used for further dataset development by classifying citing sentences in all papers from CORD-19.
Extending the SSJ Universal Dependencies Treebank for Slovenian: Was It Worth It?
Authors: Kaja Dobrovoljc, Nikola Ljubešić
Date: 2022-06-01 00:00:00
Proceeding: LREC (LAW) 2022 6
Tasks: Dependency Parsing

This paper presents the creation and evaluation of a new version of the reference SSJ Universal Dependencies Treebank for Slovenian, which has been substantially improved and extended to almost double the original size. The process was based on the initial revision and documentation of the language-specific UD annotation guidelines for Slovenian and the corresponding modification of the original SSJ annotations, followed by a two-stage annotation campaign, in which two new subsets have been added, the previously unreleased sentences from the ssj500k corpus and the Slovenian subset of the ELEXIS parallel corpus. The annotation campaign resulted in an extended version of the SSJ UD treebank with 5,435 newly added sentences comprising of 126,427 tokens. To evaluate the potential benefits of this data increase for Slovenian dependency parsing, we compared the performance of the classla-stanza dependency parser trained on the old and the new SSJ data when evaluated on the new SSJ test set and its subsets. Our results show an increase of LAS performance in general, especially for previously under-represented syntactic phenomena, such as lists, elliptical constructions and appositions, but also confirm the distinct nature of the two newly added subsets and the diversification of the SSJ treebank as a whole.
Converting the Sinica Treebank of Mandarin Chinese to Universal Dependencies
Authors: Yu-Ming Hsieh, Yueh-Yin Shih, Wei-Yun Ma
Date: 2022-06-01 00:00:00
Proceeding: LREC (LAW) 2022 6
Tasks: POS, TAG

This paper describes the conversion of the Sinica Treebank, one of the major Mandarin Chinese treebanks, to Universal Dependencies. The conversion is rule-based and the process involves POS tag mapping, head adjusting in line with the UD scheme and the dependency conversion. Linguistic insights into Mandarin Chinese alongwith the conversion are also discussed. The resulting corpus is the UD Chinese Sinica Treebank which contains more than fifty thousand tree structures according to the UD scheme. The dataset can be downloaded at https://github.com/ckiplab/ud.
Desiderata for the Annotation of Information Structure in Complex Sentences
Authors: Hannah Booth
Date: 2022-06-01 00:00:00
Proceeding: LREC (LAW) 2022 6

Many annotation schemes for information structure have been developed in recent years (Calhoun et al., 2005; Paggio, 2006; Goetze et al., 2007; Bohnet et al., 2013; Riester et al., 2018), in line with increased attention on the interaction between discourse and other linguistic dimensions (e.g. syntax, semantics, prosody). However, a crucial issue which existing schemes either gloss over, or propose only crude guidelines for, is how to annotate information structure in complex sentences. This unsatisfactory treatment is unsurprising given that theoretical work on information structure has traditionally neglected its status in dependent clauses. In this paper, I evaluate the status of pre-existing annotation schemes in relation to this vexed issue, and outline certain desiderata as a foundation for novel, more nuanced approaches, informed by state-of-the art theoretical insights (Erteschik-Shir, 2007; Bianchi and Frascarelli, 2010; Lahousse, 2010; Ebert et al., 2014; Matic et al., 2014; Lahousse, 2022). These desiderata relate both to annotation formats and the annotation process. The practical implications of these desiderata are illustrated via a test case using the Corpus of Historical Low German (Booth et al., 2020). The paper overall showcases the benefits which result from a free exchange between linguistic annotation models and theoretical research.
The Sensitivity of Annotator Bias to Task Definitions in Argument Mining
Authors: Terne Sasha Thorn Jakobsen, Maria Barrett, Anders Søgaard, David Lassen
Date: 2022-06-01 00:00:00
Proceeding: LREC (LAW) 2022 6
Tasks: Argument Mining, Sensitivity

NLP models are dependent on the data they are trained on, including how this data is annotated. NLP research increasingly examines the social biases of models, but often in the light of their training data and specific social biases that can be identified in the text itself. In this paper, we present an annotation experiment that is the first to examine the extent to which social bias is sensitive to how data is annotated. We do so by collecting annotations of arguments in the same documents following four different guidelines and from four different demographic annotator backgrounds. We show that annotations exhibit widely different levels of group disparity depending on which guidelines annotators follow. The differences are not explained by task complexity, but rather by characteristics of these demographic groups, as previously identified by sociological studies. We release a dataset that is small in the number of instances but large in the number of annotations with demographic information, and our results encourage an increased awareness of annotator bias.
NLP in Human Rights Research: Extracting Knowledge Graphs about Police and Army Units and Their Commanders
Authors: Daniel Bauer, Tom Longley, Yueen Ma, Tony Wilson
Date: 2022-06-01 00:00:00
Proceeding: LREC (LAW) 2022 6
Tasks: Knowledge Graphs

In this paper we explore the use of an NLP system to assist the work of Security Force Monitor (SFM). SFM creates data about the organizational structure, command personnel and operations of police, army and other security forces, which assists human rights researchers, journalists and litigators in their work to help identify and bring to account specific units and personnel alleged to have committed abuses of human rights and international criminal law. This paper presents an NLP system that extracts from English language news reports the names of security force units and the biographical details of their personnel, and infers the formal relationship between them. Published alongside this paper are the system’s code and training dataset. We find that the experimental NLP system performs the task at a fair to good level. Its performance is sufficient to justify further development into a live workflow that will give insight into whether its performance translates into savings in time and resource that would make it an effective technical intervention.