- Corpora - ELRA lists

Lectures on Language Technology and Society
by Christian Hardmeier 06 Jun '23

06 Jun '23

We are delighted to invite you to an afternoon of public lectures on language technology and its interaction with society. The lectures will take place in Auditorium 4 at the IT University of Copenhagen on 13 June 2023, from 14:30 to 16:45 (UTC+2). Attendance is free for anyone interested. The lectures will also be streamed on Zoom. For remote participation, please register here: https://itucph.zoom.us/meeting/register/u5Utf-Chrz0sHNYzd76DD9WjJpOU0CckVsMb Speakers and titles: - Luca Maria Aiello, IT University of Copenhagen: The Language of Coordination - Shashi Narayan, Google London: Introducing Text-blueprint: Conditional generation with question-answering plans - Anne Lauscher, University of Hamburg: Ethical conversational AI - Searching for the truth? Abstracts and further information can be found here: https://christianhardmeier.rax.ch/workshop/langtech-society-2023/ -- Christian Hardmeier, Associate Professor – https://christianhardmeier.rax.ch/ IT University of Copenhagen, Department of Computer Science

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CoCo4MT Shared Task: First Call for Participation
by Katharina Kann 06 Jun '23

06 Jun '23

CoCo4MT Shared Task: First Call for Participation We are excited to introduce a new shared task for this year’s CoCo4MT workshop! Our aim is to encourage and facilitate research on corpus construction for low-resource machine translation. Corpus creation for machine translation is typically constrained by the cost and availability of human translators. When a new dataset needs to be created for a low-resource language or a specialized domain, the annotation budget should be used efficiently and any sentences chosen for translation should be of high quality and as useful for machine translation system training as possible. In this shared task, we ask participants to come up with ways in which such examples can be identified for a target language without any existing data. Specifically, given a parallel corpus between high-resource languages, the goal is to choose a good subset of the high-resource corpus to be translated into the low-resource language, in order to obtain a good training set for a machine translation system. The shared task winner will be the team whose instances result in the best final system after training. Detailed information: https://sites.google.com/view/coco4mt/shared-task Registration: https://forms.gle/jfKSPQMKEmaaXFHy5 Important Dates - May 19 2023: Release of train, dev and test data - May 30 2023: Release of baselines - July 12, 2023: Deadline to submit results - July 20, 2023: System description papers due Organizers (listed alphabetically) - Ananya Ganesh, University of Colorado Boulder - Constantine Lignos, Brandeis University - John E. Ortega, Northeastern University - Jonne Sälevä, Brandeis University - Katharina Kann, University of Colorado Boulder - Marine Carpuat, University of Maryland - Rodolfo Zevallos, Universitat Pompeu Fabra - Shabnam Tafreshi, University of Maryland - William Chen, Carnegie Mellon University -- Dr. Katharina Kann Assistant Professor of Computer Science University of Colorado Boulder Personal page: https://kelina.github.io Group page: https://nala-cub.github.io

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CFP: ML/NLP Competition on Automatic Classification of Literary Epochs (CoLiE)
by Marina Litvak 05 Jun '23

05 Jun '23

================================================== *CFP: ML/NLP Competition on Automatic Classification of Literary Epochs (CoLiE)* To advance the field of implicit temporal information retrieval from a text, this competition aims to challenge participants to develop automatic methods to identify the literary epochs of a given text, which is considered here as an implicit temporal context of a book. The task on Automatic Classification of Literary Epochs (CoLiE) aims at automatic identification of the literary epoch of a given text from its writing style: (1) Romanticism (1798-1837), (2) Victorian Literature (1837-1901), (3) Modernism (1900-1945), (4) Postmodernism (1945-2000), and (5) our days (from 2000). The competition is held as a part of the IACT’23 <https://en.sce.ac.il/news/iact23> workshop, held on July 27, 2023, in conjunction with the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval This competition is open to anyone with a passion for information retrieval, machine learning, and natural language processing. Whether you are a seasoned expert or a newcomer to the field, we welcome you to participate and extend the boundaries of automated text analysis! Competition site: http://www.kaggle.com/competitions/colie Competition Timeline - May 28, 2023: The competition is open to participants. Training and validation sets together with their labels are available. - July 10, 2023: Test dataset available. - July 17, 2023, 23:59 UTC: Final submission deadline. - July 27, 2023: The winners are announced at the special session at the IACT'23 <https://en.sce.ac.il/news/iact23> workshop. *The organizing team* - Dr. Marina Litvak (marinal(a)ac.sce.ac.il), Software Engineering Department, Shamoon College of Engineering, Beer Sheva, 84100, Israel - Dr. Irina Rabaev (irinar(a)ac.sce.ac.il), Software Engineering Department, Shamoon College of Engineering, Beer Sheva, 84100, Israel - Prof. Ricardo Campos (ricardo.campos(a)ipt.pt), Ci2 - Smart Cities Research Center, Polytechnic Institute of Tomar INESC TEC, Porto Porto, Portugal - Prof. Alípio Mário Jorge (amjorge(a)fc.up.pt) University of Porto Porto, Portugal - Prof. Adam Jatowt (adam.jatowt(a)uibk.ac.at) University of Innsbruck, Innsbruck, Austria - Mr. Vladimir Younkin (vladiyo(a)ac.sce.ac.il), Software Engineering Department, Shamoon College of Engineering, Beer Sheva, 84100, Israel -- Best regards, Marina Litvak

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Ph.D. position in NLP at the School of Engineering (University of Edinburgh)
by Shay Cohen 05 Jun '23

05 Jun '23

On behalf of Dr. Elliot Crowley from the School of Engineering at the University of Edinburgh (queries at elliot.crowley(a)ed.ac.uk): Application link: Affordable Training of Large Language Models<https://www.eng.ed.ac.uk/studying/postgraduate/research/phd/affordable-trai…> Recent developments in large language models (LLMs) have caught the attention of the public. LLMs such as OpenAI's GPT-4 and Google's Bard are able to generate remarkably realistic, coherent text based on a user's input and have the potential to be general-purpose tools used throughout society e.g. for customer service, summarising text, answering questions, writing contracts or translating between languages. However, LLMs are prohibitively expensive to train. GPT-3 (which is significantly smaller than its successor, GPT-4) has an estimated training time of 355-GPU years and an estimated training cost of $4.6M [1]. Only large, wealthy institutions can train these models and thereby control how they are trained and who gets access to them. This is undemocratic. Very recent work provides hope however. In [2] the authors explore the promising idea of “cramming”: the training of a LLM on a single GPU in a day. In [3] the authors use synthetic data to train “small” language models that can produce consistent stories at little cost. There is a huge discrepancy in quality between these models and their expensive counterparts, however. In this PhD, the student will investigate affordable LLM training i.e. with limited compute and/or data, inspired by [2,3]. Avenues of research could include (i) generating training data that facilitates fast training e.g. through dataset distillation [4]; (ii) exploring neural architecture search to develop models that are "aware" of being resource-constrained while being trained; (iii) developing novel cost-effective training algorithms, (iv) leveraging and tuning open-source LLMs. The successful student will have opportunities for collaboration within and outside Edinburgh’s School of Engineering e.g. with colleagues in the Institute for Digital Communications<https://www.eng.ed.ac.uk/research/institutes/idcom/>, The Bayesian and Neural Systems Group<https://www.bayeswatch.com/>, and Edinburgh NLP<https://edinburghnlp.inf.ed.ac.uk/>. [1] https://lambdalabs.com/blog/demystifying-gpt-3 [2] https://arxiv.org/abs/2212.14034 [3] https://arxiv.org/abs/2305.07759 [4] https://arxiv.org/abs/1811.10959 The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336. Is e buidheann carthannais a th’ ann an Oilthigh Dhùn Èideann, clàraichte an Alba, àireamh clàraidh SC005336.

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10th European Conference On Service-Oriented And Cloud Computing (ESOCC 2023): Final Call for Submissions
by georgeangelos60＠gmail.com 05 Jun '23

05 Jun '23

*** Final Call for Submissions *** 10th European Conference On Service-Oriented And Cloud Computing (ESOCC 2023) October 24-26, 2023, Golden Bay Beach Hotel, Larnaca, Cyprus https://cyprusconferences.org/esocc2023/ (Proceedings to be published in Springer LNCS; Journal Special Issue with Springer Computing) Submission Deadline: Abstracts by June 25, 2023; Full Papers by July 2, 2023 AIM AND SCOPE Nowadays, Service-Oriented and Cloud Computing are the primary approaches to build large-scale distributed systems and deliver software services to end users. Cloud-native software is pervading the delivery of enterprise applications, as they are composed of (micro)services that can be independently developed and deployed by exploiting multiple heterogeneous technologies. Resulting applications are polyglot service compositions that can then be shipped in serverful or serverless platforms (e.g., using virtualization technologies). These characteristics make Service-Oriented and Cloud Computing the natural answers for fulfilling the industry’s need for flexibly scalable and maintainable enterprise applications, to be delivered through state-of-the-art methodologies, like DevOps. To further support this, researchers and practitioners need to create methods, tools and techniques to support cost-effective and secure development as well as use of dependable devices, platforms, services and service-oriented applications in the Cloud, now also considering the Cloud-IoT computing continuum to exploit widespread adoption of smart connected things and the increasing growth of their computing capabilities. The European Conference on Service-Oriented and Cloud Computing (ESOCC) is the premier conference on advances in the state-of-the-art and practice of Service-Oriented Computing and Cloud Computing in Europe. ESOCC aims to facilitate the exchange between researchers and practitioners in the areas of Service-Oriented Computing and Cloud Computing, as well as to explore the new trends in those areas and foster future collaborations in Europe and beyond. TOPICS OF INTEREST ESOCC 2023 seeks original, high-quality contributions related to all aspects of Service-Oriented and Cloud computing. Specific topics of interest include, but are not limited to: • Applications for Service-Oriented and Cloud Computing, e.g., big data, commerce, energy, finance, health, scientific computing, smart cities • Blockchains for Service-Oriented and Cloud Computing • Business aspects of Service-Oriented and Cloud Computing, e.g., business models, brokerage, marketplaces, costs, pricing • Business processes, e.g., service-based workflow deployment and management • Cloud interoperability, service and Cloud standards, • Cloud-IoT computing continuum, e.g., edge computing, fog computing, mobility computing, next generation services/IoT • Cloud-native architectures and paradigms, e.g., microservices and DevOps • Cloud service models, e.g., IaaS, PaaS, SaaS, DBaaS, FaaS, etc. • Deployment, composition, and management of applications in Service-Oriented and Cloud Computing • Foundations and formal methods for Service-Oriented and Cloud Computing • Enablers for Service-Oriented and Cloud Computing, e.g., service discovery, orchestration, matchmaking, monitoring, and analytics • Model-Driven Engineering for Service-Oriented and Cloud Computing • Multi-Cloud, cross-Cloud, and federated Cloud solutions • Requirements engineering, design, development, and testing of applications in Service-Oriented and Cloud Computing • Semantic services and service mining • Service and Cloud middlewares and platforms • Software/service adaptation and evolution in Service-Oriented and Cloud Computing • Storage, computation and network Clouds • Sustainability and energy issues in Service-Oriented and Cloud Computing • Quality aspects (e.g., governance, privacy, security, and trust) of Service-Oriented and Cloud Computing • Quality of Service (QoS) and Service-Level Agreement (SLA) for Service-Oriented and Cloud Computing • Social aspects of Service-Oriented and Cloud Computing, e.g., crowdsourcing services, social and crowd-based Clouds • Virtualization for Service-Oriented and Cloud Computing, e.g., serverless, container-based virtualization, VMs IMPORTANT DATES • Submission of abstracts: June 25th, 2023 (AoE) • Submission of full papers: July 2nd, 2023 (AoE) • Notification to authors: August 4th, 2023 (AoE) • Camera-ready versions due: August 21st, 2023 (AoE) • Author registration due: August 21st, 2023 (AoE) TYPES OF SUBMISSIONS ESOCC 2023 invites submissions of the following kinds: • Regular Research Papers (15 pages including references) • PhD Symposium (12 pages including references) • Projects and Industry Reports (Projects and Industry Reports (1 to 6 pages including references, describing an ongoing EU or national project, or providing industrial perspectives on innovative applications, technologies, or methods in ESOCC’s scope) We only accept original papers, not submitted for publication elsewhere. The papers must be formatted according to the proceedings guidelines of Springer’s Lecture Notes in Computer Science (LNCS) series (http://www.springer.com/lncs). They must be submitted to the EasyChair site at:  https://easychair.org/conferences/?conf=esocc2023 by selecting the right track. Accepted papers from all tracks will be published in the main conference proceedings by Springer in the LNCS series. For publication to happen, at least one author of each accepted paper is expected to register and present the work at the conference. The best papers accepted will be invited to submit extended versions for a Journal Special Issue to be published by Springer Computing. ORGANISATION General Chair • George A. Papadopoulos, University of Cyprus, CY (george at ucy.ac.cy) Program Chairs • Florian Rademacher, University of Applied Sciences and Arts Dortmund, DE (florian.rademacher at fh-dortmund.de) • Jacopo Soldani, University of Pisa, IT (jacopo.soldani at unipi.it) Steering and Program Committee https://cyprusconferences.org/esocc2023/committees/

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summer school/BOOTCAMP announcement
by Anna Marchi 05 Jun '23

05 Jun '23

The University of Bologna is offering* a funded residential bootcamp*: *"Theories and methods for the corpus-assisted analysis of discourse: from language that denotes to language that expresses phenomena". * 20 participants *27-30 September (Bertinoro - Italy)* *Deadline for application 19/06/2023* #cadscamp All info: *https://centri.unibo.it/colitec/en/events/bootcamp-28-30-september-2023 <https://centri.unibo.it/colitec/en/events/bootcamp-28-30-september-2023> * Best, Anna

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Call for Participation: FinCausal 2023 (Financial Document Causality Detection)
by Doaa Samy 05 Jun '23

05 Jun '23

Call for Participation: *FinCausal-2023 Shared Task: “Financial Document Causality Detection” *is organised within the *5th Financial Narrative Processing Workshop (FNP 2023)* taking place in the 2023 IEEE International Conference on Big Data (IEEE BigData 2023) <http://bigdataieee.org/BigData2023/>, Sorrento, Italy, 15-18 December 2023. It is a *one-day event*. The exact date is to be announced. Important Dates: - Call for participation and registration: 3rd June 2023 - Registration deadline: 28 June - Training set release: 29 June 2023 - Test set release: 5 September 2023 - Systems submission deadline: 15 September 2023 - Release of results: 20 September 2023 - Paper submission deadline: 20 October 2023 - Notification of acceptance: November 12, 2023 - Camera-ready of accepted papers: November 20, 2023 - FNP Workshop: December 2023 Workshop URL: https://wp.lancs.ac.uk/cfie/fincausal2023/ Registration Form: https://forms.gle/29E161a8RmMosBLU8. After completing the registration form, the practice set will be sent to participants. *Shared Task Description:* Financial analysis needs factual data and an explanation of the variability of these data. Data state facts but need more knowledge regarding how these facts materialised. Furthermore, understanding causality is crucial in studying decision-making processes. The *Financial Document Causality Detection Task* (FinCausal) aims at identifying elements of cause and effect in causal sentences extracted from financial documents. Its goal is to evaluate which events or chain of events can cause a financial object to be modified or an event to occur, regarding a given context. In the financial landscape, identifying cause and effect from external documents and sources is crucial to explain why a transformation occurs. Two subtasks are organised this year. *English FinCausal subtask *and* Spanish FinCausal subtask*. This is the first year where we introduce a subtask in Spanish. *Objective*: For both tasks, participants are asked to identify, given a causal sentence, which elements of the sentence relate to the cause, and which relate to the effect. Participants can use any method they see fit (regex, corpus linguistics, entity relationship models, deep learning methods) to identify the causes and effects. *English FinCausal subtask* - *Data Description: *The dataset has been sourced from various 2019 financial news articles provided by Qwam, along with additional SEC data from the Edgar Database. Additionally, we have augmented the dataset from FinCausal 2022, adding 500 new segments. Participants will be provided with a sample of text blocks extracted from financial news and already labelled. - *Scope: *The* English FinCausal subtask* focuses on detecting causes and effects when the effects are quantified. The aim is to identify, in a causal sentence or text block, the causal elements and the consequential ones. Only one causal element and one effect are expected in each segment. - *Length of Data fragments: *The* English FinCausal subtask* segments are made up of up to three sentences. - *Data format: *CSV files. Datasets for both the English and the Spanish subtasks will be presented in the same format. This shared task focuses on determining causality associated with a quantified fact. An event is defined as the arising or emergence of a new object or context regarding a previous situation. So, the task will emphasise the detection of causality associated with the transformation of financial objects embedded in quantified facts. *Spanish FinCausal subtask* - *Data Description: *The dataset has been sourced from a corpus of Spanish financial annual reports from 2014 to 2018. Participants will be provided with a sample of text blocks extracted from financial news, labelled through inter-annotator agreement. - *Scope: *The *Spanish FinCausal subtask* aims to detect all types of causes and effects, not necessarily limited to quantified effects. The aim is to identify, in a paragraph, the causal elements and the consequential ones. Only one causal element and one effect are expected in each paragraph. - *Length of Data fragments: *The *Spanish FinCausal subtask* involves complete paragraphs. - *Data format: *CSV files. Datasets for both the English and the Spanish subtasks will be presented in the same format. This shared task focuses on determining causality associated with both events or quantified facts. For this task, a cause can be the justification for a statement or the reason that explains a result. This task is also a relation detection task. *FinCausal Shared Task Organisers:* - Antonio Moreno-Sandoval (UAM, Spain) - Blanca Carbajo Coronado (UAM, Spain) - Doaa Samy (UCM, Spain) - Jordi Porta (UAM, Spain) - Dominique Mariko (Yseop, France) For any questions, please contact the organisers at *fincausal.2023(a)gmail.com <fincausal.2023(a)gmail.com>*

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LxGr2023: Programme and Call for Participation
by Costas Gabrielatos 03 Jun '23

03 Jun '23

8th Symposium on Corpus Approaches to Lexicogrammar (LxGr2022) The symposium will take place online on 6-8 July 2023. The programme and registration details are here: https://sites.edgehill.ac.uk/lxgr/lxgr2023 For more information, contact lxgr(a)edgehill.ac.uk<mailto:lxgr@edgehill.ac.uk>. ________________________________ Edge Hill University<http://ehu.ac.uk/home/emailfooter> Modern University of the Year, The Times and Sunday Times Good University Guide 2022<http://ehu.ac.uk/tef/emailfooter> University of the Year, Educate North 2021/21 ________________________________ This message is private and confidential. If you have received this message in error, please notify the sender and remove it from your system. Any views or opinions presented are solely those of the author and do not necessarily represent those of Edge Hill or associated companies. Edge Hill University may monitor email traffic data and also the content of email for the purposes of security and business communications during staff absence.<http://ehu.ac.uk/itspolicies/emailfooter>

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Call for participation - Arabic NER Shared Task 2023
by nagham ghanim 02 Jun '23

02 Jun '23

Dear colleagues, We are happy to invite you to join the *Arabic NER SharedTask 2023* <https://dlnlp.ai/st/wojood/> which will be organized as part of the WANLP 2023. We will provide you with a large corpus and Google Colab notebooks to help you reproduce the baseline results. دعوة للمشاركة في مسابقة استخراج الكيونات المسماه من النصوص العربية. سنزود المشاركين بمدونة وبرمجيات للحصول على نتائج مرجعية يمكنهم البناء عليها. *INTRODUCTION* Named Entity Recognition (NER) is integral to many NLP applications. It is the task of identifying named entity mentions in unstructured text and classifying them to predefined classes such as person, organization, location, or date. Due to the scarcity of Arabic resources, most of the research on Arabic NER focuses on flat entities and addresses a limited number of entity types (person, organization, and location). The goal of this shared task is to alleviate this bottleneck by providing Wojood, a large and rich Arabic NER corpus. Wojood consists of about 550K tokens (MSA and dialect, in multiple domains) that are manually annotated with 21 entity types. *REGISTRATION* Participants need to register via this form ( *https://forms.gle/UCCrVNZ2LaPviCZS6* <https://forms.gle/UCCrVNZ2LaPviCZS6>). Participating teams will be provided with common training development datasets. No external manually labelled datasets are allowed. Blind test data set will be used to evaluate the output of the participating teams. Each team is allowed a maximum of 3 submissions. All teams are required to report on the development and test sets (after results are announced) in their write-ups. *FAQ* For any questions related to this task, please check our *Frequently Asked Questions* <https://docs.google.com/document/d/1XE2n89mFLic2P9DO_sAD51vy734BOt0kgtZ6bFf…> *IMPORTANT DATES* - March 03, 2023: Registration available - May 25, 2023: Data-sharing and evaluation on development set Avaliable - June 10, 2023: Registration deadline - July 20, 2023: Test set made available - July 30, 2023: Evaluation on test set (TEST) deadline - Augest 29, 2023: Shared task system paper submissions due - October 12, 2023: Notification of acceptance - October 30, 2023: Camera-ready version - TBA: WANLP 2023 Conference. ** All deadlines are 11:59 PM UTC-12:00 (Anywhere On Earth).* *CONTACT* For any questions related to this task, please contact the organizers directly using the following email address: *NERShare...(a)gmail.com <https://groups.google.com/>* or join the google group: *https://groups.google.com/g/ner_sharedtask2023* <https://groups.google.com/g/ner_sharedtask2023>. *SHARED TASK* As described, this shared task targets both flat and nested Arabic NER. The subtasks are: *Subtask 1:* *Flat NER* In this subtask, we provide the Wojood-Flat train (70%) and development (10%) datasets. The final evaluation will be on the test set (20%). The flat NER dataset is the same as the nested NER dataset in terms of train/test/dev split and each split contains the same content. The only difference in the flat NER is each token is assigned one tag, which is the first high-level tag assigned to each token in the nested NER dataset. *Subtask 2:* *Nestd NER* In this subtask, we provide the Wojood-Nested train (70%) and development (10%) datasets. The final evaluation will be on the test set (20%). *METRICS* The evaluation metrics will include precision, recall, F1-score. However, our official metric will be the micro F1-score. The evaluation of shared tasks will be hosted through CODALAB. Teams will be provided with a CODALAB link for each shared task. -*CODALAB link for NER Shared Task Subtask 1 (Flat NER)* <https://codalab.lisn.upsaclay.fr/competitions/11594> -*CODALAB link for NER Shared Task Subtask 2 (Nestd NER)* <https://dlnlp.ai/st/wojood/> *BASELINES* Two baseline models trained on Wojood (flat and nested) are provided: *Nested NER baseline:* is presented in this *article* <https://aclanthology.org/2022.lrec-1.387/>, and code is available in *GitHub* <https://github.com/SinaLab/ArabicNER>. The model achieves a micro F1-score of 0.9059 (note that this baseline does not handle nested entities of the same type). *Flat NER baseline:* same code repository for nested NER (*GitHub* <https://github.com/SinaLab/ArabicNER>) can also be used to train flat NER task. Our flat NER baseline achieved a micro F1-score of 0.8785. *GOOGLE COLAB NOTEBOOKS* To allow you to experiment with the baseline, we authored four Google Colab notebooks that demonstrate how to train and evaluate our baseline models. [1] *Train Flat NER* <https://gist.github.com/mohammedkhalilia/72c3261734d7715094089bdf4de74b4a>: This notebook can be used to train our ArabicNER model on the flat NER task using the sample Wojood data found in our repository. [2] *Evaluate Flat NER* <https://gist.github.com/mohammedkhalilia/c807eb1ccb15416b187c32a362001665>: this notebook will use the trained model saved from the notebook above to perform evaluation on unseen dataset. [3] *Train Nested NER* <https://gist.github.com/mohammedkhalilia/a4d83d4e43682d1efcdf299d41beb3da>: This notebook can be used to train our ArabicNER model on the nested NER task using the sample Wojood data found in our repository. [4] *Evaluate Nested NER* <https://gist.github.com/mohammedkhalilia/9134510aa2684464f57de7934c97138b>: this notebook will use the trained model saved from the notebook above to perform evaluation on unseen dataset. *ORGANIZERS* - Mustafa Jarrar, Birzeit University - Muhammad Abdul-Mageed, University of British Columbia & MBZUAI - Mohammed Khalilia, Birzeit University - Bashar Talafha, University of British Columbia - AbdelRahim Elmadany, University of British Columbia - Nagham Hamad, Birzeit University - Alaa Omer, Birzeit University

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The 5th Financial Narrative Processing Workshop (FNP 2023)
by Marina Litvak 02 Jun '23

02 Jun '23

*The 5th Financial Narrative Processing Workshop (FNP 2023)* To be held at the 2023 IEEE International Conference on Big Data (IEEE BigData 2023), Sorrento, Italy, from 15 to 18 December 2023. FNP 2023: http://wp.lancs.ac.uk/cfie/fnp2023/ *Submission page:* https://wi-lab.com/cyberchair/2023/bigdata23/scripts/submit.php?subarea=S14… *Important Dates:* 1st Call for workshop papers: June 1, 2023 2nd Call for workshop papers: August 15, 2023 Final Call for workshop papers: October 1, 2023 Due date for workshop papers submission: October 30, 2023 (anywhere in the world) Notification of paper acceptance to authors: November 12, 2023 Camera-ready of accepted papers: November 20, 2023 Workshop date: 1 day event: December 15-18, 2023 (exact date to be announced) Other dates for shared tasks will be advertised separately *Workshop Description:*Financial narrative processing is an emerging field that combines natural language processing (NLP) and machine learning (ML) techniques to extract, summarise, and analyse both qualitative and quantitative financial data. As the amount of financial data continues to grow exponentially, this data is increasingly considered as big data, which presents challenges and opportunities for data scientists. The 5th Financial Narrative Processing Workshop (FNP 2023) aims to bring together researchers and industry practitioners to share their latest research results and practical experiences in financial narrative processing, which is a key aspect of big data. In particular, the workshop will focus on three shared tasks: Financial Narrative Summarization, Financial Table of Content Extraction, and Financial Causality Detection. These tasks will challenge participants to apply state-of-the-art techniques in NLP and ML to extract meaningful insights from financial documents. The workshop will provide an informal and vibrant forum for discussion and collaboration, with the goal of advancing the field of financial narrative processing within the context of big data. We welcome submissions from researchers and practitioners in academia and industry. FNP 2023 workshop is organised by a team of experts who have been at the forefront of financial NLP research for the past five years. We have organised more than 7 international events, introduced NLP and AI shared tasks, and provided big datasets and methodologies needed to push forward the emerging field of financial NLP. Our workshop series has contributed significantly to the field of financial NLP, as evidenced by our proceedings on ACL anthology and citations in Google Scholar. *Previous Proceedings:*All FNP proceedings across the years are on ACL Anthology: https://aclanthology.org/venues/fnp/. The 1st FNP was associated with LREC 2018 http://lrec-conf.org/workshops/lrec2018/W27/pdf/book_of_proceedings.pdf FNP Google Scholar: https://scholar.google.com/citations?hl=en&user=8Qn7yJ8AAAAJ *Motivation:*Financial narrative disclosures represent a significant portion of firms’ overall financial communications with investors. Textual commentaries help to clarify issues that may be obscured by complex accounting methods and footnote disclosures. In addition, narratives summarise corporate strategy, contextualise results, explain governance arrangements, describe corporate social responsibility policy, and provide forward-looking information for investors. However, financial narratives may also provide management with an opportunity to obfuscate accounting results and manipulate readers’ perceptions of underlying economic performance. In a previous FNP workshop, we organised a panel of experts to discuss the future of Financial NLP and data leaders from AI firms in France and London. The consensus was that financial data has increased exponentially in recent years due to the increase in regulations. This has led to an increase in the number of financial news surrounding the events of releasing such disclosures. Therefore, state-of-the-art methodologies are necessary to understand and analyse huge and sensitive financial data in a short amount of time. We believe that the FNP 2023 workshop will continue to contribute to the field of Financial NLP by providing a platform for researchers and industry practitioners to share their research results and practical development experiences in Big Data research, development, and practice. In addition, our workshop will help participants gain a better understanding of the challenges posed by big data and its 5 V’s (velocity, volume, value, variety, and veracity) in financial text analysis. *Topics of Interest in relation to Financial NLP:*We encourage research on topics related to analysing financial narratives using state-of-the-art NLP techniques, including but not limited to morphological analysis, disambiguation, tokenization, part-of-speech tagging, named entity recognition, chunking, parsing, semantic role labelling, sentiment analysis, document quality, and advanced readability metrics. The use of NLP and machine learning in the financial domain has encouraged studies around gender and ethnicities imbalance, as well as mental health and well-being research. Given the focus of the IEEE Big Data 2023 conference, we also encourage research on under-resourced languages and under-represented financial markets. In recent years, FNP has included research on Arabic, Spanish, and Portuguese financial markets. Our collaboration with the MultiLing workshop ( http://multiling.iit.demokritos.gr) has highlighted the importance of summarization across domains and sources that are related to finance (e.g., company blogs, product reviews, market briefs, etc.). This includes financial multilingual and cross-lingual summarization using single-document summarization, multi-document summarization, summarization evaluation, headline generation, and cross-domain/cross-topic summarization. Given the international nature of the event, we particularly welcome FNP papers reporting non-English and multilingual research, describing the different regulatory regimes within which companies operate internationally. *The FNP2023 shared tasks* will be announced separately and are expected to be: Financial Narrative Summarisation (FNS 2023) Financial Table of Content Extraction (FinTOC 2023) Financial Causality Detection (FinCausal 2023) For the latest details about the shared tasks please visit: http://wp.lancs.ac.uk/cfie/shared-tasks/ *Call For Papers for the Main Workshop:* We invite papers describing original, completed or ongoing, unpublished research in Financial Natural Language Processing and Financial Text Analysis. As financial data is increasingly considered as big data, we encourage submissions that address the five main and innate characteristics of big data (velocity, volume, value, variety, and veracity) in the context of financial narrative processing. We encourage submissions on topics that include, but are not limited to, the following: - Applying core technologies on financial narratives within the context of big data: morphological analysis, disambiguation, tokenization, part-of-speech tagging, named entity recognition, chunking, parsing, semantic role labelling, sentiment analysis, document quality and advanced readability metrics, etc. - Using NLP to detect misreporting in relation to diversity and wellbeing on issues related to gender, ethnicity, women at work as well as employee mental health and stability, in the context of big data. - Financial narrative resources and tools for managing and analysing large-scale financial data. - Summarization techniques across domains and sources that are related to finance (e.g. company blogs, product reviews, market briefs, etc.), this includes financial multilingual and cross-lingual summarization using single-document summarization, multi-document summarization, summarization evaluation, headline generation, cross-domain/cross-topic summarization. - Analysis of Online Social Networks for detection of public opinions towards financial events. - Multilingual analysis, describing the different regulatory regimes within which companies operate internationally. - Ongoing research and preliminary results that explore the intersection of financial narrative processing and big data. - Negative results, for example techniques and methodologies that work for certain languages but not on others. Other venues could be showing that state-of-the-art technologies such as BERT could fail on certain tasks or languages. All papers accepted will be included in the conference proceedings published by the IEEE Computer Society Press. We follow IEEE submission format. Please submit a full paper (up to 10 page IEEE 2-column format) or short paper (up to 4 page IEEE 2-column format) through the online submission system. *Organising Committee:* Dr Mo El-Haj, Lancaster University, UK (General Chair) Dr Houda Bouamor, CMU, Qatar (FNP Program Chair) Prof Paul Rayson, Lancaster University, UK (FNP Program Chair) Blanca Carbajo Coronado, UAM, Madrid, Spain (FNP coordinator, Publication Chair) Nikiforos Pittaras, NCSR Demokritos (Publicity Chair) Dr George Giannakopoulos, NCSR Demokritos (FNS Shared Task Organiser) Dr Marina Litvak, Shamoon Academic College of Engineering (FNS Shared Task Organiser) Prof Antonio Moreno Sandoval, UAM, Madrid, Spain (FinCausal Shared Task Organizer) Dr Doaa Samy, UAM, Madrid, Spain (FinCausal Shared Task Organizer) Dr Juyeon KANG, Fortia Financial Solution (FinTOC Shared Task Organiser) Dr Ismail El Maarouf, Imprevicible (FinTOC Shared Task Organiser) -- Best regards, Marina Litvak

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