AIISCA full-day workshop at LREC-COLING 2024 (Torino, Italia), 20-25 May, 2024
The emergence of very large language models (VLLMs) has dramatically altered the trajectory of progress in AI and its applications. With the release of ChatGPT, that excitement has now transcended boundaries from AI researchers to the common man; indeed it asserts we are living in an exciting time of scientific proliferation. However, we see two divergent community views on VLLMs. Believers in the magical powers of VLLMs claim that VLLMs are autodidactic in learning a wide gamut of new capabilities – referred to as "emerging capabilities" in the community – an effect that gets pronounced with model size and dataset scale. On the other hand, critics are not yet prepared to acknowledge the self-learning power of VLLMs; they criticize it as only a statistical learner and out several flaws – hallucination being the most prominent one. In this forum, we want to bring together both communities – the believers and the critics, to explore exciting tasks together: (i) CT2 - Counter Turing Test: AI-Generated Text Detection, and (ii) HILT - HallucinatIon eLiciaTion through automatic detec- tion and mitigation. Expect this to be an exciting forum to discuss, debate, and explore exciting scientific pathways for the future.
The aim of this workshop is to better understand and mitigate factors that cause the research-to-practice gap for data-driven approaches to transportation applications. We will gather people from the intelligent transportation community, including both researchers of multiple disciplines and practitioners from transportation agencies and local communities, to conduct talks, poster sessions, and discussions that will help fulfill the aim.
Knowledge Graphs (KGs) have emerged as a compelling abstraction for organizing and interconnecting the world’s structured knowledge in order to integrate information extracted from multiple data sources. Such knowledge graphs, and knowledge networks, are beginning to play a central role in representing information extracted by AI systems and improving the predictions of AI systems by providing knowledge expressed in KGs as inputs. On February 23-25, 2022, the NSF and White House Office of Science and Technology Policy (OSTP) sponsored a workshop to kick of a 4-month “OKN Innovation Sprint”. The use cases and applications identified in that meeting were from a very broad range of domains including climate change, disaster response and management, supply chain management, identifying bias in research, decarceration, homelessness, and nutrition security. Sprint teams will investigate construction and use of open knowledge networks within the context of specific applications in these domains. All teams will reassemble at a workshop in June to assess progress and develop a common strategy for an OKN, going forward. This workshop aims to invite researchers, practitioners, and the general public to brainstorm the ideas related to OKN, collaboratively build KGs for different domains or applications, develop AI algorithms to provide intelligent services based on OKN, and discuss the social and economic implications related to OKN.
Knowledge Graphs are getting traction in both academia and in the industry as one of the key elements of AI applications. They are being recognized as an important and essential resource in many downstream tasks such as question answering, recommendation, personal assistants, business analytics, business automation, etc. Even though there are large knowledge graphs built with crowdsourcing such as Wikidata or using semi-structured data such as DBpedia or Yago or from structured data such as relational databases, building knowledge graphs from text corpora still remains an open challenge. This workshop aims to bring together researchers from multiple focus areas such as Natural Language Processing (NLP), Entity Linking (EL), Relation Extraction (RE), Knowledge Representation and Reasoning (KRR), Deep Learning (DL), Knowledge Base Construction (KBC), Semantic Web, Linked Data, and other related fields to foster a discussion and enhance the state-of-the-art in knowledge graph generation from text.
Combating fake news is one of the burning societal crisis. It is difficult to expose false claims before they create a lot of damage. Automatic fact/claim verification has recently become a topic of interest among diverse research communities. Research efforts and datasets on text fact verification could be found, but there is not much attention towards multi-modal or cross-modal fact-verification. This workshop will encourage researchers from interdisciplinary domains working on multi-modality and/or fact-checking to come together and work on multimodal (images, memes, videos) fact-checking. At the same time, multimodal hate speech detection is an important problem but has not received much attention. Lastly, learning joint modalities his of interest to both Natural Language Processing (NLP) and Computer Vision (CV) forums. Rationale: During the last decade, both the field of studies - NLP and CV have made significant progress due to the success strories of neural network. Mutimodal tasks like visual question-answering (VQA), image captioning, video captioning, caption based image retrieval, etc. started getting into the main spotlight either in NLP/CV forums. Mutimodality is the next big leap for the AI community. De-Factify is a specified forum to discuss on multimodal fake news, and hate speech related challenges. We also encourage discussion on multi-modal tasks in general.
The role of online platforms as a prime, daily communication tool is coincident with a sharp rise in its misuse, threatening our society in large. These platforms have been implicated for promoting hate speech, radicalization, harassment, cyberbullying, fake news, human trafficking, drug dealing, gender-based stereotyping and violence among other ills, with a significant impact on individual and community well-being. Such content and behaviors are inherently multi-faceted, making the recognition of their narratives challenging for researchers as well as social media companies. The implications to individuals and communities require reliable models and algorithms for detecting, understanding and countering the malevolent behavior in such communications. These challenges have led to a rising prominence of analysis of online communications in academia, politics, homeland security, and industry using computational techniques from natural language processing, statistics, network science, data mining, machine learning, computational linguistics, human-computer interaction, and cognitive science. To meet these challenges, this workshop aims to stimulate research on social, cultural, emotional, communicative, and linguistic aspects of harmful conversations on online platforms and developing novel approaches to analyze, interpret and understand them.
The workshop welcomes for consideration papers that employ quantitative and/or qualitative, analytical, theoretical approaches examining a diverse range of issues related to online harmful communications. Papers on resources/data and tools will also be welcome either for demos or for short/regular talks.
Pictographs, commonly referred to as "emoji", have become a popular way to enhance electronic communication. With their introduction in the late 1990's, emoji have been widely used to enhance the sentiment, emotion, and sarcasm expressed in social media messages. They often play distinct social and communicative roles compared to other forms of written language while taking over language constructs such as slang terms and emoticons. The ability to automatically process, derive meaning and interpret text fused with emoji will be essential as society embraces emoji as a standard form of online communication. Yet the pictorial nature of emoji, the fact that (the same) emoji may be used in different contexts to express different meanings, and that emoji are used in different cultures and communities over the world who interpret emoji differently, make it especially difficult to apply traditional Natural Language Processing (NLP) techniques to analyze them. To meet these challenges, Emoji aims to stimulate research on understanding social, cultural, communicative, and linguistic roles of emoji and developing novel computational approaches to analyze, interpret and understand emoji and their usage in social media applications. It will provide a forum to bring together researchers and practitioners from both academia and industry in the areas of computer science, social network analysis and mining, natural language processing, computational linguistics, human-computer interaction, and computational social sciences to discuss high-quality research and emerging applications, to exchange ideas and experience, and to identify new opportunities for collaboration.
Emoji is the 4th of a series of emoji understanding workshops conducted by the organizers. The 1st emoji understanding workshop, Emoji, was the best-attended workshop at ICWSM 2018 (with 35 attendees) that was extended from a half-day to a full-day due to the quality and quantity of submissions received and the expressed interest. It attracted 18 submissions, including 14 long papers and 4 short papers. A total of 8 papers (6 long papers and 2 short papers) were accepted for the final workshop program and it also included a keynote presentation, a tutorial, and a highly interdisciplinary panel on "The Challenges in Emoji Understanding" that provided an animated and engaging forum to the attendees to discuss the open emoji research problems with leading researchers and practitioners. The workshop program was covered in a WIRED.com article which can be accessed online at https://www.wired.com/story/academic-emoji-conference/. The second emoji understanding workshop, Emoji, was co-located with The Web Conference 2019 (formerly, The World Wide Web Conference). It attracted 11 submissions, including 7 full papers and 4 short papers, out of which, 6 papers were accepted for presentation. The workshop program also consisted of a keynote presentation and three invited talks. The workshop ran for a half-day and attracted more than 40 participants. The third emoji understanding workshop, Emoji, was co-located with ICWSM 2020. It attracted 63 registered participants where Emojination and Adobe Inc. sponsored twenty of them with their workshop registration fees. The workshop program consisted of a keynote speech, an invited talk, and five research paper presentations. To minimize the effect of not having an opportunity to network in-person due to the online nature of this workshop, an hour long virtual networking event was conducted at the end of the workshop which attracted lively participation.
This workshop will bring researchers in academia, industry, humanitarian organizations, and healthcare practioners ar the confluence of knowledge representation, natural language understanding, and deep learning. Prior exposure to the basic concepts in NLP and DL is desirable, however, there are no prerequisites for attending the tutorial. We will cover basics and advanced techniques with sufficient use cases and demonstrations. Newcomers in the area will learn the basic principles of data science and the fundamentals of knowledge-infused learning. Expert attendees will appreciate promising, reliable, and practical approaches to overcoming familiar technical obstacles in social good domains.
CASY 2020 is a hybrid physical/virtual conference on the theme of collaborative assistants for the society. The conference will take place on October 16, 2020 virtually over Zoom and physically, in a limited socially distant manner, on the campus on University of South Carolina, Columbia, SC, USA. The event will be free-to-attend and is intended to promote the ethical usage of digital assistants in society for daily life activities.
Research in artificial intelligence and data science is accelerating rapidly due to an unprecedented explosion in the amount of information on the web. In parallel, we noticed immense growth in the construction and utility of the knowledge network from Google, Netflix, NSF, and NIH.
However, current methods risk an unsatisfactory ceiling of applicability due to shortcomings in bringing homogeneity between knowledge graphs, data mining, and deep learning. In this changing world, retrospective studies for building state-of-the-art AI and Data science systems have raised concerns on trust, traceability, and interactivity for prospective applications in healthcare, finance, and crisis response.
We believe the paradigm of knowledge-infused mining and learning would account for both pieces of knowledge that accrue from domain expertise and guidance from physical models. Further, it will allow the community to design new evaluation strategies that assess robustness and fairness across all comparable state-of-the-art algorithms.
Pictographs, commonly referred to as "emoji", have become a popular way to enhance electronic communication. With their introduction in the late 1990's, emoji have been widely used to enhance the sentiment, emotion, and sarcasm expressed in social media messages. They often play distinct social and communicative roles compared to other forms of written language while taking over language constructs such as slang terms and emoticons. The ability to automatically process, derive meaning and interpret text fused with emoji will be essential as society embraces emoji as a standard form of online communication. Yet the pictorial nature of emoji, the fact that (the same) emoji may be used in different contexts to express different meanings, and that emoji are used in different cultures and communities over the world who interpret emoji differently, make it especially difficult to apply traditional Natural Language Processing (NLP) techniques to analyze them. To meet these challenges, Emoji aims to stimulate research on understanding social, cultural, communicative, and linguistic roles of emoji and developing novel computational approaches to analyze, interpret and understand emoji and their usage in social media applications. It will provide a forum to bring together researchers and practitioners from both academia and industry in the areas of computer science, social network analysis and mining, natural language processing, computational linguistics, human-computer interaction, and computational social sciences to discuss high-quality research and emerging applications, to exchange ideas and experience, and to identify new opportunities for collaboration.
Emoji is a follow up to one of the best-attended workshops at ICWSM 2018 that was extended from a half-day to a full day due to the quality and quantity of submissions received and the expressed interest. Emoji, which was the the first workshop in this series involved a truly interdisciplinary (computer science, linguistics, computational social science, anthropology, law, and marketing) and highly engaging participation covering a balanced representation from academia and industry. It attracted 18 submissions, including 14 long papers and 4 short papers. A total of 8 papers (6 long papers and 2 short papers) were accepted for the final workshop program and it also included a keynote presentation, a tutorial, and a highly interdisciplinary panel on "The Challenges in Emoji Understanding" that provided an animated and engaging forum to the attendees to discuss the open emoji research problems with leading researchers and practitioners. The workshop program was covered in a WIRED.com which can be accessed online at https://www.wired.com/story/academic-emoji-conference/. The second emoji understanding workshop, Emoji, was co-located with The Web Conference 2019 (formerly, The World Wide Web Conference). It attracted 11 submissions, including 7 full papers and 4 short papers, out of which, 6 papers were accepted for presentation. The workshop program also consisted of a keynote presentation and three invited talsks. The workshop ran for a half-day and attracted more than 40 participants.
The symposium will bring together the full range of stakeholders contributing to the practice and science of disaster management, who will be able to reflect on the past research efforts funded by NSF and other agencies as well as understand and assess the state of the art.