pwc-archive/papers-with-abstracts huggingface dataset:
Time-dependent non-homogeneous stochastic epidemic model of SIR type
Authors: Mireia Besalú, Giulia Binotto
Date: 2022-09-24 00:00:00
Tasks: Vocal Bursts Type Prediction

To better describe the spread of a disease, we extend a discrete time stochastic SIR-type epidemic model of Tuckwell and Williams. We assume the dependence on time of the number of daily encounters and include a parameter to represent a possible quarantine of the infectious individuals. We provide an analytic description of this Markovian model and investigate its dynamics. Both a diffusion approximation and the basic reproduction number are derived. Through several simulations, we show how the evolution of a disease is affected by the distribution of the number of daily encounters and its dependence on time.
Asymptotic decoupling of population growth rate and cell size distribution
Authors: Yaïr Hein, Farshid Jafarpour
Date: 2022-09-29 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

The rate at which individual bacterial cells grow depends on the concentrations of cellular components such as ribosomes and proteins. These concentrations continuously fluctuate over time and are inherited from mother to daughter cells, leading to correlations between the growth rates of cells across generations. Division sizes of cells are also stochastic and correlated across generations due to a phenomenon known as cell size regulation. Fluctuations and correlations from both growth and division processes affect the population dynamics of an exponentially growing culture. Here, we provide analytic solutions for the population dynamics of cells with continuously fluctuating growth rates coupled with a generic model of cell-size regulation. We show that in balanced growth, the effects of growth and division processes decouple; the population growth rate only depends on the single-cell growth rate process, and the population cell size distribution only depends on the model of division and cell size regulation. The population growth rate is always higher than the average single-cell growth rate, and the difference increases with growth rate variability and its correlation time. This difference also sets the timescale for the population to reach its steady state. We provide analytical solutions for oscillations in population growth rate and traveling waves in size distribution during this approach to the steady state.
Working With Convex Responses: Antifragility From Finance to Oncology
Authors: Nassim Nicholas Taleb, Jeffrey West
Date: 2022-09-29 00:00:00
Tasks: Management

We extend techniques and learnings about the stochastic properties of nonlinear responses from finance to medicine, particularly oncology where it can inform dosing and intervention. We define antifragility. We propose uses of risk analysis to medical problems, through the properties of nonlinear responses (convex or concave). We 1) link the convexity/concavity of the dose-response function to the statistical properties of the results; 2) define "antifragility" as a mathematical property for local beneficial convex responses and the generalization of "fragility" as its opposite, locally concave in the tails of the statistical distribution; 3) propose mathematically tractable relations between dosage, severity of conditions, and iatrogenics. In short we propose a framework to integrate the necessary consequences of nonlinearities in evidence-based oncology and more general clinical risk management.
Modifications to a classic BFGS library for use with SIMD-equipped hardware and an AAD library
Authors: Evgeny Goncharov, Alexandre Rodrigues
Date: 2022-09-29 00:00:00

We introduce certain modifications of the BFGS method for functions that are not parallelizable by nature (having consecutive operations only) taking advantage of SIMD. We also provide a modified LBFGS\texttt{++} library that takes advantage of these modifications, and the use of AAD, and give an interface for AAD users that takes advantage of the modified library automatically. We give two examples to illustrate the performance. The modified library is up to 3.8 times faster for European Swaption curve calibration in ORE (not parallelizable) and 1.4 times faster for calibrating the LMM model by a set of European options.
Multilevel Monte Carlo and its Applications in Financial Engineering
Authors: Devang Sinha, Siddhartha P. Chakrabarty
Date: 2022-09-29 00:00:00
Tasks: Management

In this article, we present a review of the recent developments on the topic of Multilevel Monte Carlo (MLMC) algorithm, in the paradigm of applications in financial engineering. We specifically focus on the recent studies conducted in two subareas, namely, option pricing and financial risk management. For the former, the discussion involves incorporation of the importance sampling algorithm, in conjunction with the MLMC estimator, thereby constructing a hybrid algorithm in order to achieve reduction for the overall variance of the estimator. In case of the latter, we discuss the studies carried out in order to construct an efficient algorithm in order to estimate the risk measures of Value-at-Risk (VaR) and Conditional Var (CVaR), in an efficient manner. In this regard, we briefly discuss the motivation and the construction of an adaptive sampling algorithm with an aim to efficiently estimate the nested expectation, which, in general is computationally expensive.
A First Attempt at Unreliable News Detection in Swedish
Authors: Ricardo Muñoz Sánchez, Eric Johansson, Shakila Tayefeh, Shreyash Kad
Date: 2022-06-01 00:00:00
Proceeding: ResTUP (LREC) 2022 6
Tasks: Articles, Data Augmentation, Misinformation

Throughout the COVID-19 pandemic, a parallel infodemic has also been going on such that the information has been spreading faster than the virus itself. During this time, every individual needs to access accurate news in order to take corresponding protective measures, regardless of their country of origin or the language they speak, as misinformation can cause significant loss to not only individuals but also society. In this paper we train several machine learning models (ranging from traditional machine learning to deep learning) to try to determine whether news articles come from either a reliable or an unreliable source, using just the body of the article. Moreover, we use a previously introduced corpus of news in Swedish related to the COVID-19 pandemic for the classification task. Given that our dataset is both unbalanced and small, we use subsampling and easy data augmentation (EDA) to try to solve these issues. In the end, we realize that, due to the small size of our dataset, using traditional machine learning along with data augmentation yields results that rival those of transformer models such as BERT.
BanglaHateBERT: BERT for Abusive Language Detection in Bengali
Authors: Md Saroar Jahan, Mainul Haque, Nabil Arhab, Mourad Oussalah
Date: 2022-06-01 00:00:00
Proceeding: ResTUP (LREC) 2022 6
Tasks: Abusive Language, Language Modeling, Language Modelling

This paper introduces BanglaHateBERT, a retrained BERT model for abusive language detection in Bengali. The model was trained with a large-scale Bengali offensive, abusive, and hateful corpus that we have collected from different sources and made available to the public. Furthermore, we have collected and manually annotated 15K Bengali hate speech balanced dataset and made it publicly available for the research community. We used existing pre-trained BanglaBERT model and retrained it with 1.5 million offensive posts. We presented the results of a detailed comparison between generic pre-trained language model and retrained with the abuse-inclined version. In all datasets, BanglaHateBERT outperformed the corresponding available BERT model.
A Comparison of Machine Learning Techniques for Turkish Profanity Detection
Authors: Levent Soykan, Cihan Karsak, Ilknur Durgar ElKahlout, Burak Aytan
Date: 2022-06-01 00:00:00
Proceeding: ResTUP (LREC) 2022 6

Profanity detection became an important task with the increase of social media usage. Most of the users prefer a clean and profanity free environment to communicate with others. In order to provide a such environment for the users, service providers are using various profanity detection tools. In this paper, we researched on Turkish profanity detection in our search engine. We collected and labeled a dataset from search engine queries as one of the two classes: profane and not-profane. We experimented with several classical machine learning and deep learning methods and compared methods in means of speed and accuracy. We performed our best scores with transformer based Electra model with 0.93 F1 Score. We also compared our models with the state-of-the-art Turkish profanity detection tool and observed that we outperform it from all aspects.
Features and Categories of Hyperbole in Cyberbullying Discourse on Social Media
Authors: Simona Ignat, Carl Vogel
Date: 2022-06-01 00:00:00
Proceeding: ResTUP (LREC) 2022 6

Cyberbullying discourse is achieved with multiple linguistic conveyances. Hyperboles witnessed in a corpus of cyberbullying utterances are studied. Linguistic features of hyperbole using the traditional grammatical indications of exaggerations are analyzed. The method relies on data selected from a larger corpus of utterances identified and labelled as “bullying”, from Twitter, from October 2020 to March 2022. An outcome is a lexicon of 250 entries. A small number of lexical level features have been isolated, and chi-squared contingency tests applied to evaluating their information value in identifying hyperbole. Words or affixes indicating superlatives or extremes of scales, with positive but not negative valency items, interact with hyperbole classification in this data set. All utterances extracted has been considered exaggerations and the stylistic status of “hyperbole” has been commented within the frame of new meanings in the context of social media.
What Is Going through Your Mind? Metacognitive Events Classification in Human-Agent Interactions
Authors: Hafiza Erum Manzoor, Volha Petukhova
Date: 2022-06-01 00:00:00
Proceeding: ISA (LREC) 2022 6
Tasks: Decision Making, valid

For an agent, either human or artificial, to show intelligent interactive behaviour implies assessments of the reliability of own and others’ thoughts, feelings and beliefs. Agents capable of these robust evaluations are able to adequately interpret their own and others’ cognitive and emotional processes, anticipate future actions, and improve their decision-making and interactive performances across domains and contexts. Reliable instruments to assess interlocutors’ mindful capacities for monitoring and regulation - metacognition - in human-agent interaction in real-time and continuously are of crucial importance however challenging to design. The presented study reports Concurrent Think Aloud (CTA) experiments in order to access and evaluate metacognitive dispositions and attitudes of participants in human-agent interactions. A typology of metacognitive events related to the ‘verbalized’ monitoring, interpretation, reflection and regulation activities observed in a multimodal dialogue has been designed, and serves as a valid tool to identify relation between participants’ behaviour analysed in terms of ISO 24617-2 compliant dialogue acts and the corresponding metacognitive indicators.
Assessment of Sales Negotiation Strategies with ISO 24617-2 Dialogue Act Annotations
Authors: Jutta Stock, Volha Petukhova, Dietrich Klakow
Date: 2022-06-01 00:00:00
Proceeding: ISA (LREC) 2022 6

Call centres endeavour to achieve the highest possible level of transparency with regard to the factors influencing sales success. Existing approaches to the quality assessment of customer-agent sales negotiations do not enable in-depths analysis of sales behaviour. This study addresses this gap and presents a conceptual and operational framework applying the ISO 24617-2 dialogue act annotation scheme, a multidimensional taxonomy of interoperable semantic concepts. We hypothesise that the ISO 24617-2 dialogue act annotation framework adequately supports sales negotiation assessment in the domain of call centre conversations. Authentic call centre conversations are annotated and a range of extensions/modifications are proposed making the annotation scheme better fit this new domain. We concluded that ISO 24617-2 serves as a powerful instrument for the analysis and assessment of sales negotiation and strategies applied by a call centre agent.
Levels of Non-Fictionality in Fictional Texts
Authors: Florian Barth, Hanna Varachkina, Tillmann Dönicke, Luisa Gödeke
Date: 2022-06-01 00:00:00
Proceeding: ISA (LREC) 2022 6

The annotation and automatic recognition of non-fictional discourse within a text is an important, yet unresolved task in literary research. While non-fictional passages can consist of several clauses or sentences, we argue that 1) an entity-level classification of fictionality and 2) the linking of Wikidata identifiers can be used to automatically identify (non-)fictional discourse. We query Wikidata and DBpedia for relevant information about a requested entity as well as the corresponding literary text to determine the entity’s fictionality status and assign a Wikidata identifier, if unequivocally possible. We evaluate our methods on an exemplary text from our diachronic literary corpus, where our methods classify 97% of persons and 62% of locations correctly as fictional or real. Furthermore, 75% of the resolved persons and 43% of the resolved locations are resolved correctly. In a quantitative experiment, we apply the entity-level fictionality tagger to our corpus and conclude that more non-fictional passages can be identified when information about real entities is available.
Event Sequencing Annotation with TIE-ML
Authors: Damir Cavar, Ali Aljubailan, Ludovic Mompelat, Yuna Won, Billy Dickson, Matthew Fort, Andrew Davis, Soyoung Kim
Date: 2022-06-01 00:00:00
Proceeding: ISA (LREC) 2022 6
Tasks: Sentence

TIE-ML (Temporal Information Event Markup Language) first proposed by Cavar et al. (2021) provides a radically simplified temporal annotation schema for event sequencing and clause level temporal properties even in complex sentences. TIE-ML facilitates rapid annotation of essential tense features at the clause level by labeling simple or periphrastic tense properties, as well as scope relations between clauses, and temporal interpretation at the sentence level. This paper presents the first annotation samples and empirical results. The application of the TIE-ML strategy on the sentences in the Penn Treebank (Marcus et al., 1993) and other non-English language data is discussed in detail. The motivation, insights, and future directions for TIE-ML are discussed, too. The aim is to develop a more efficient annotation strategy and a formalism for clause-level tense and aspect labeling, event sequencing, and tense scope relations that boosts the productivity of tense and event-level corpus annotation. The central goal is to facilitate the production of large data sets for machine learning and quantitative linguistic studies of intra- and cross-linguistic semantic properties of temporal and event logic.
Measuring Similarity by Linguistic Features rather than Frequency
Authors: Rodolfo Delmonte, Nicolò Busetto
Date: 2022-06-01 00:00:00
Proceeding: ISA (LREC) 2022 6
Tasks: Word Embeddings

In the use and creation of current Deep Learning Models the only number that is used for the overall computation is the frequency value associated with the current word form in the corpus, which is used to substitute it. Frequency values come in two forms: absolute and relative. Absolute frequency is used indirectly when selecting the vocabulary against which the word embeddings are created: the cutoff threshold is usually fixed at 30/50K entries of the most frequent words. Relative frequency comes in directly when computing word embeddings based on co-occurrence values of the tokens included in a window size 2/5 adjacent tokens. The latter values are then used to compute similarity, mostly based on cosine distance. In this paper we will evaluate the impact of these two frequency parameters on a small corpus of Italian sentences whose main features are two: presence of very rare words and of non-canonical structures. Rather than basing our evaluation on cosine measure alone, we propose a graded scale of scores which are linguistically motivated. The results computed on the basis of a perusal of BERT’s raw embeddings shows that the two parameters conspire to decide the level of predictability.
Testing the Annotation Consistency of Hallidayan Transitivity Processes: A Multi-variable Structural Approach
Authors: Min Dong, Xiaoyan Liu, Alex Chengyu Fang
Date: 2022-06-01 00:00:00
Proceeding: ISA (LREC) 2022 6

SFL seeks to explain identifiable, observable phenomena of language use in context through the application of a theoretical framework which models language as a functional, meaning making system (Halliday & Matthiessen 2004). Due to the lack of explicit annotation criteria and the divide between conceptual vs. syntactic criteria in practice, it has been a tough job to achieve consistency in the annotation of Hallidayn transitivity processes. The present study proposed that explicit structural and syntactic criteria should be adopted as a basis. Drawing on syntactic and grammatical features as judgement cues, we applied structurally oriented criteria for the annotation of the process categories and participant roles combining a set of interrelated syntactic variables and established the annotation criteria for contextualised circumstantial categories in structural as well as semantic terms. An experiment was carried out to test the usefulness of these annotation criteria, applying percent agreement and Cohen’s kappa as measurements of interrater reliability between the two annotators in each of the five pairs. The results verified our assumptions, albeit rather mildly, and, more significantly, offered some first empirical indications about the practical consistency of transitivity analysis in SFL. In the future work, the research team expect to draw on the insights and experience from some of the ISO standards devoted to semantic annotation such as dialogue acts (Bunt et al. 2012) and semantic roles (ISO-24617-4, 2014).
The place of ISO-Space in Text2Story multilayer annotation scheme
Authors: António Leal, Purificação Silvano, Evelin Amorim, Inês Cantante, Fátima Silva, Alípio Mario Jorge, Ricardo Campos
Date: 2022-06-01 00:00:00
Proceeding: ISA (LREC) 2022 6
Tasks: Articles

Reasoning about spatial information is fundamental in natural language to fully understand relationships between entities and/or between events. However, the complexity underlying such reasoning makes it hard to represent formally spatial information. Despite the growing interest on this topic, and the development of some frameworks, many problems persist regarding, for instance, the coverage of a wide variety of linguistic constructions and of languages. In this paper, we present a proposal of integrating ISO-Space into a ISO-based multilayer annotation scheme, designed to annotate news in European Portuguese. This scheme already enables annotation at three levels, temporal, referential and thematic, by combining postulates from ISO 24617-1, 4 and 9. Since the corpus comprises news articles, and spatial information is relevant within this kind of texts, a more detailed account of space was required. The main objective of this paper is to discuss the process of integrating ISO-Space with the existing layers of our annotation scheme, assessing the compatibility of the aforementioned parts of ISO 24617, and the problems posed by the harmonization of the four layers and by some specifications of ISO-Space.
Do machines dream of artificial agreement?
Authors: Anna Lindahl
Date: 2022-06-01 00:00:00
Proceeding: ISA (LREC) 2022 6

In this paper the (assumed) inconsistency between F1-scores and annotator agreement measures is discussed. This is exemplified in five corpora from the field of argumentation mining. High agreement is important in most annotation tasks and also often deemed important for an annotated dataset to be useful for machine learning. However, depending on the annotation task, achieving high agreement is not always easy. This is especially true in the field of argumentation mining, because argumentation can be complex as well as implicit. There are also many different models of argumentation, which can be seen in the increasing number of argumentation annotated corpora. Many of these reach moderate agreement but are still used in machine learning tasks, reaching high F1-score. In this paper we describe five corpora, in particular how they have been created and used, to see how they have handled disagreement. We find that agreement can be raised post-production, but that more discussion regarding evaluating and calculating agreement is needed. We conclude that standardisation of the models and the evaluation methods could help such discussions.
CroaTPAS: A Survey-based Evaluation
Authors: Costanza Marini
Date: 2022-06-01 00:00:00
Proceeding: ISA (LREC) 2022 6
Tasks: Multiple-choice, Survey

The Croatian Typed Predicate Argument Structures resource is a Croatian/English bilingual digital dictionary of corpus-derived verb valency structures, whose argument slots have been annotated with Semantic Types labels following the CPA methodology. CroaTPAS is tailor-made to represent verb polysemy and currently contains 180 Croatian verbs for a total of 683 different verbs senses. In order to evaluate the resource both in terms of identified Croatian verb senses, as well as of the English descriptions explaining them, an online survey based on a multiple-choice sense disambiguation task was devised, pilot tested and distributed among respondents following a snowball sampling methodology. Answers from 30 respondents were collected and compared against a yardstick set of answers in line with CroaTPAS’s sense distinctions. Jaccard similarity index was used as a measure of agreement. Since the multiple-choice items respondents answered to were based on a representative selection of CroaTPAS verbs, they allowed for a generalization of the results to the whole of the resource.
Multilingual Reference Annotation: A Case between English and Mandarin Chinese
Authors: Ut Seong Sio, Luís Morgado da Costa
Date: 2022-06-01 00:00:00
Proceeding: ISA (LREC) 2022 6
Tasks: Translation

This paper presents the on-going effort to annotate a cross-lingual corpus on nominal referring expressions in English and Mandarin Chinese. The annotation includes referential forms and referential (information) statuses. We adopt the RefLex annotation scheme (Baumann and Riester, 2012) for the classification of referential statuses. The data focus of this paper is restricted to [the-X] phrases in English (where X stands for any nominal) and their translation equivalents in Mandarin Chinese. The original English and translated Mandarin versions of ‘The Adventure of the Dancing Men’ and ‘The Adventure of Speckled Band’ from the Sherlock Holmes series were annotated. It contains 1090 instances of [the-X] phrases in English. Our study uncovers the following: (i) bare nouns are the most common Mandarin translation for [the-X] phrases in English, followed by demonstrative phrases, with the exception that when the noun phrase refers to locations/places, in such cases, demonstrative phrases are almost never used; (ii) [the-X] phrases in English are more likely to be translated as demonstrative phrases in Mandarin if they have the referential status of ‘given’ (previously mentioned) or ‘given-displaced’(antecedent of an expression occurs earlier than the previous five clauses). In these Mandarin demonstrative phrases, the proximal demonstrative is more often used and it is almost exclusively used for ‘given’ while the distal demonstrative can be used for both ‘given’ and ‘given-displaced’.