1,191 research outputs found
Essays on Corporate Disclosure of Value Creation
Information on a firm’s business model helps investors understand an entity’s resource requirements, priorities for action, and prospects (FASB, 2001, pp. 14-15; IASB, 2010, p. 12). Disclosures of strategy and business model (SBM) are therefore considered a central element of effective annual report commentary (Guillaume, 2018; IIRC, 2011). By applying natural language processing techniques, I explore what SBM disclosures look like when management are pressed to say something, analyse determinants of cross-sectional variation in SBM reporting properties, and assess whether and how managers respond to regulatory interventions seeking to promote SBM annual report commentary. This dissertation contains three main chapters. Chapter 2 presents a systematic review of the academic literature on non-financial reporting and the emerging literature on SBM reporting. Here, I also introduce my institutional setting. Chapter 3 and Chapter 4 form the empirical sections of this thesis. In Chapter 3, I construct the first large sample corpus of SBM annual report commentary and provide the first systematic analysis of the properties of such disclosures. My topic modelling analysis rejects the hypothesis that such disclosure is merely padding; instead finding themes align with popular strategy frameworks and management tailor the mix of SBM topics to reflect their unique approach to value creation. However, SBM commentary is less specific, less precise about time horizon (short- and long-term), and less balanced (more positive) in tone relative to general management commentary. My findings suggest symbolic compliance and legitimisation characterize the typical annual report discussion of SBM. Further analysis identifies proprietary cost considerations and obfuscation incentives as key determinants of symbolic reporting. In Chapter 4, I seek evidence on how managers respond to regulatory mandates by adapting the properties of disclosure and investigate whether the form of the mandate matters. Using a differences-in-differences research design, my results suggest a modest incremental response by treatment firms to the introduction of a comply or explain provision to provide disclosure on strategy and business model. In contrast, I find a substantial response to enacting the same requirements in law. My analysis provides clear and consistent evidence that treatment firms incrementally increase the volume of SBM disclosure, improve coverage across a broad range of topics as well as providing commentary with greater focus on the long term. My results point to substantial changes in SBM reporting properties following regulatory mandates, but the form of the mandate does matter. Overall, this dissertation contributes to the accounting literature by examining how firms discuss a central topic to economic decision making in annual reports and how firms respond to different forms of disclosure mandate. Furthermore, the results of my analysis are likely to be of value for regulators and policymakers currently reviewing or considering mandating disclosure requirements. By examining how companies adapt their reporting to different types of regulations, this study provides an empirical basis for recalibrating SBM disclosure mandates, thereby enhancing the information set of capital market participants and promoting stakeholder engagement in a landscape increasingly shaped by non-financial information
Patterns and Variation in English Language Discourse
The publication is reviewed post-conference proceedings from the international 9th Brno Conference on Linguistics Studies in English, held on 16–17 September 2021 and organised by the Faculty of Education, Masaryk University in Brno. The papers revolve around the themes of patterns and variation in specialised discourses (namely the media, academic, business, tourism, educational and learner discourses), effective interaction between the addressor and addressees and the current trends and development in specialised discourses. The principal methodological perspectives are the comparative approach involving discourses in English and another language, critical and corpus analysis, as well as identification of pragmatic strategies and appropriate rhetorical means. The authors of papers are researchers from the Czech Republic, Italy, Luxembourg, Serbia and Georgia
Emotion Recognition for Human-Centered Conversational Agents
This thesis proposes a study on Emotion Recognition in Conversation to address the challenges
of the task with a chatbot reference case study to enhance conversational agents’ ability to understand and respond appropriately to human emotion. The study consists of two phases. The
first one involves the use of several baselines and the implementation of EmoBERTa to explore
aspects of the task, such as preprocessing, balancing technique and context modelling tested
on ERC benchmark dataset. The results reveal that the punctuation provides key information
to the task, balancing techniques can provide marginal improvements if appropriately selected
and context can provide additional information and suggest that a non-static context construction could be beneficial.
In the second phase, the effectiveness of a Few-Shot learning method, SetFit, is explored in
the context of ERC to face the scarce amount of real labelled data. An incompatibility with
the given context definition of the architecture employed by the mentioned method called for
an adaptation which proved to be ineffective. The performance of the SetFit method and finetuning are compared in a limited data regime. Finally, the study explores the capabilities of
a trained model on a specific ERC dataset to adapt to limited data from a different domain
using Transfer Learning and fine-tuning with inconclusive results. The findings and insight
from this can lay the groundwork for future developments and studies in the growing field of
emotional-aware conversational agents and the application of Few-Shot learning in this task
Construction Grammar and Artificial Intelligence
In this chapter, we argue that it is highly beneficial for the contemporary
construction grammarian to have a thorough understanding of the strong
relationship between the research fields of construction grammar and artificial
intelligence. We start by unravelling the historical links between the two
fields, showing that their relationship is rooted in a common attitude towards
human communication and language. We then discuss the first direction of
influence, focussing in particular on how insights and techniques from the
field of artificial intelligence play an important role in operationalising,
validating and scaling constructionist approaches to language. We then proceed
to the second direction of influence, highlighting the relevance of
construction grammar insights and analyses to the artificial intelligence
endeavour of building truly intelligent agents. We support our case with a
variety of illustrative examples and conclude that the further elaboration of
this relationship will play a key role in shaping the future of the field of
construction grammar.Comment: Peer-reviewed author's draft of a chapter to appear in the Cambridge
Handbook of Construction Grammar (2024 - edited by Mirjam Fried and Kiki
Nikiforidou
A Review of Deep Learning Models for Twitter Sentiment Analysis: Challenges and Opportunities
Microblogging site Twitter (re-branded to X since July 2023) is one of the most influential online social media websites, which offers a platform for the masses to communicate, expresses their opinions, and shares information on a wide range of subjects and products, resulting in the creation of a large amount of unstructured data. This has attracted significant attention from researchers who seek to understand and analyze the sentiments contained within this massive user-generated text. The task of sentiment analysis (SA) entails extracting and identifying user opinions from the text, and various lexicon-and machine learning-based methods have been developed over the years to accomplish this. However, deep learning (DL)-based approaches have recently become dominant due to their superior performance. This study briefs on standard preprocessing techniques and various word embeddings for data preparation. It then delves into a taxonomy to provide a comprehensive summary of DL-based approaches. In addition, the work compiles popular benchmark datasets and highlights evaluation metrics employed for performance measures and the resources available in the public domain to aid SA tasks. Furthermore, the survey discusses domain-specific practical applications of SA tasks. Finally, the study concludes with various research challenges and outlines future outlooks for further investigation
Referring to discourse participants in Ibero-Romance languages
Synopsis:
This volume brings together contributions by researchers focusing on personal pronouns in Ibero-Romance languages, going beyond the well-established variable of expressed vs. non-expressed subjects. While factors such as agreement morphology, topic shift and contrast or emphasis have been argued to account for variable subject expression, several corpus studies on Ibero-Romance languages have shown that the expression of subject pronouns goes beyond these traditionally established factors and is also subject to considerable dialectal variation. One of the factors affecting choice and expression of personal pronouns or other referential devices is whether the construction is used personally or impersonally. The use and emergence of new impersonal constructions, eventually also new (im)personal pronouns, as well as the variation found in the expression of human impersonality in different Ibero-Romance language varieties is another interesting research area that has gained ground in the recent years. In addition to variable subject expression, similar methods and theoretical approaches have been applied to study the expression of objects. Finally, the reference to the addressee(s) using different address pronouns and other address forms is an important field of study that is closely connected to the variable expression of pronouns. The present book sheds light on all these aspects of reference to discourse participants. The volume contains contributions with a strong empirical background and various methods and both written and spoken corpus data from Ibero-Romance languages. The focus on discourse participants highlights the special properties of first and second person referents and the factors affecting them that are often different from the anaphoric third person. The chapters are organized into three thematic sections: (i) Variable expression of subjects and objects, (ii) Between personal and impersonal, and (iii) Reference to the addressee
Comparing the production of a formula with the development of L2 competence
This pilot study investigates the production of a formula with the development of L2 competence over proficiency levels of a spoken learner corpus. The results show that the formula
in beginner production data is likely being recalled holistically from learners’ phonological
memory rather than generated online, identifiable by virtue of its fluent production in absence
of any other surface structure evidence of the formula’s syntactic properties. As learners’ L2
competence increases, the formula becomes sensitive to modifications which show structural
conformity at each proficiency level. The transparency between the formula’s modification
and learners’ corresponding L2 surface structure realisations suggest that it is the independent
development of L2 competence which integrates the formula into compositional language,
and ultimately drives the SLA process forward
Large language models and artificial intelligence, the end of (language) learning as we know it—or not quite?
PreprintThe rapid advancements in large language models (LLM) and artificial intelligence (AI) have been a subject of recent significant interest and debate. This paper explores the impact of these developments on language learning. I discuss the technology underlying AI-based tools and the natural language processing (NLP) tasks they were originally designed for. This will help us to identify opportunities and limitations regarding their use in the context of language learning. I then examine how such technology can be used efficiently and effectively in language teaching and learning. The availability of such tools will require language teaching to focus on the non-mechanical aspects of writing. Similarly, automatically produced personalized teaching and learning materials will not replace human teachers, but give space for and support human–human interaction
Evaluating Feature-Specific Similarity Metrics using Human Judgments for Norwegian News
Masteroppgave i informasjonsvitenskapINFO390MASV-INF
A Primer on Seq2Seq Models for Generative Chatbots
The recent spread of Deep Learning-based solutions for Artificial Intelligence and the development of Large Language Models has pushed forwards significantly the Natural Language Processing area. The approach has quickly evolved in the last ten years, deeply affecting NLP, from low-level text pre-processing tasks –such as tokenisation or POS tagging– to high-level, complex NLP applications like machine translation and chatbots. This paper examines recent trends in the development of open-domain data-driven generative chatbots, focusing on the Seq2Seq architectures. Such architectures are compatible with multiple learning approaches, ranging from supervised to reinforcement and, in the last years, allowed to realise very engaging open-domain chatbots. Not only do these architectures allow to directly output the next turn in a conversation but, to some extent, they also allow to control the style or content of the response. To offer a complete view on the subject, we examine possible architecture implementations as well as training and evaluation approaches. Additionally, we provide information about the openly available corpora to train and evaluate such models and about the current and past chatbot competitions. Finally, we present some insights on possible future directions, given the current research status
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