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检索条件"任意字段=2nd Workshop on Bridging Human-Computer Interaction and Natural Language Processing, HCI NLP 2022"

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hci+nlp 2022 - 2nd workshop on bridging human-computer interaction and natural language processing, Proceedings of the workshop

HCI+NLP 2022 - 2nd Workshop on Bridging Human-Computer Inter...

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2nd workshop on bridging human-computer interaction and natural language processing, hci+nlp 2022

ISBN: (纸本)9781955917902

The proceedings contain 8 papers. The topics discussed include: taxonomy builder: a data-driven and user-centric tool for streamlining taxonomy construction;an interactive exploratory tool for the task of hate speech detection;human-centered computing in legal nlp - an application to refugee status determination;let’s chat: understanding user expectations in socialbot interactions;teaching interactively to learn emotions in natural language;narrative datasets through the lenses of nlp and hci;and towards a deep multi-layered dialectal language analysis: a case study of African-American English.

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Let's Chat: Understanding User Expectations in Socialbot interactions 2

Let's Chat: Understanding User Expectations in Socialbot Int...

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2nd workshop on bridging human-computer interaction and natural language processing, hci+nlp 2022

作者： Soper, Elizabeth Pacquetet, Erin Saha, Sougata Das, Souvik Srihari, Rohini SUNY Department of Linguistics Buffalo United States Computer Science

ISBN: (纸本)9781955917902

This paper analyzes data from the 2021 Amazon Alexa Prize Socialbot Grand Challenge 4, in order to better understand the differences between human-computer interactions (hci) in a socialbot setting and conventional human-to-human interactions. We find that because socialbots are a new genre of hci, we are still negotiating norms to guide interactions in this setting. We present several notable patterns in user behavior toward socialbots, which have important implications for guiding future work in the development of conversational agents. © 2022 Association for Computational Linguistics.

关键词： Behavioral research

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Narrative Datasets through the Lenses of nlp and hci 2

Narrative Datasets through the Lenses of NLP and HCI

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2nd workshop on bridging human-computer interaction and natural language processing, hci+nlp 2022

作者： Sultana, Sharifa Zhang, Renwen Lim, Hajin Antoniak, Maria Cornell University United States National University of Singapore Singapore Seoul National University Korea Republic of

ISBN: (纸本)9781955917902

In this short paper, we compare existing value systems and approaches in nlp and hci for collecting narrative data. Building on these parallel discussions, we shed light on the challenges facing some popular nlp dataset types, which we discuss these in relation to widely-used narrative-based hci research methods;and we highlight points where nlp methods can broaden qualitative narrative studies. In particular, we point towards contextuality, positionality, dataset size, and open research design as central points of difference and windows for collaboration when studying narratives. Through the use case of narratives, this work contributes to a larger conversation regarding the possibilities for bridging nlp and hci through speculative mixed-methods. © 2022 Association for Computational Linguistics.

关键词： natural language processing systems

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Towards a Deep Multi-layered Dialectal language Analysis: A Case Study of African-American English 2

Towards a Deep Multi-layered Dialectal Language Analysis: A ...

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2nd workshop on bridging human-computer interaction and natural language processing, hci+nlp 2022

作者： Dacon, Jamell Michigan State University East LansingMI United States

ISBN: (纸本)9781955917902

Currently, natural language processing (nlp) models proliferate language discrimination leading to potentially harmful societal impacts as a result of biased outcomes. For example, part-of-speech taggers trained on Mainstream American English (MAE) produce non-interpretable results when applied to African American English (AAE) as a result of language features not seen during training. In this work, we incorporate a human-in-the-loop paradigm to gain a better understanding of AAE speakers' behavior and their language use, and highlight the need for dialectal language inclusivity so that native AAE speakers can extensively interact with nlp systems while reducing feelings of disenfranchisement. © 2022 Association for Computational Linguistics.

关键词： natural language processing systems

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human-Centered Computing in Legal nlp An Application to Refugee Status Determination 2

Human-Centered Computing in Legal NLP An Application to Refu...

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2nd workshop on bridging human-computer interaction and natural language processing, hci+nlp 2022

作者： Barale, Claire The University of Edinburgh School of Informatics Edinburgh United Kingdom

ISBN: (纸本)9781955917902

This paper proposes an approach to the design of an ethical human-AI reasoning support system for decision makers in refugee law. In the context of refugee status determination, practitioners mostly rely on text data. We therefore investigate human-AI cooperation in legal natural language processing. Specifically, we want to determine which design methods can be transposed to legal text analytics. Although little work has been done so far on human-centered design methods applicable to the legal domain, we assume that introducing iterative cooperation and user engagement in the design process is (1) a method to reduce technical limitations of an nlp system and (2) that it will help design more ethical and effective applications by taking users' preferences and feedback into account. The proposed methodology is based on three main design steps: cognitive process formalization in models understandable by both humans and computers, speculative design of prototypes, and semi-directed interviews with a sample of potential users. © 2022 Association for Computational Linguistics.

关键词： natural language processing systems

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Design Considerations for an nlp-Driven Empathy and Emotion Interface for Clinician Training via Telemedicine 2

Design Considerations for an NLP-Driven Empathy and Emotion ...

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2nd workshop on bridging human-computer interaction and natural language processing, hci+nlp 2022

作者： Girju, Roxana Girju, Marina Department of Linguistics & Computer Science University of Illinois Urbana-Champaign United States Jabs School of Business California Baptist University United States

ISBN: (纸本)9781955917902

As digital social platforms and mobile technologies become more prevalent and robust, the use of Artificial Intelligence (AI) in facilitating human communication will grow. This, in turn, will encourage development of intuitive, adaptive, and effective empathic AI interfaces that better address the needs of socially and culturally diverse communities. In this paper, we present several design considerations of an intelligent digital interface intended to guide the clinicians toward more empathetic communication. This approach allows various communities of practice to investigate how AI, on one side, and human communication and healthcare needs, on the other, can contribute to each other's development. © 2022 Association for Computational Linguistics.

关键词： Design

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Teaching Interactively to Learn Emotions in natural language 2

Teaching Interactively to Learn Emotions in Natural Language

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2nd workshop on bridging human-computer interaction and natural language processing, hci+nlp 2022

作者： Titung, Rajesh Alm, Cecilia O. Rochester Institute of Technology New York United States

ISBN: (纸本)9781955917902

Motivated by prior literature, we provide a proof of concept simulation study for an understudied interactive machine learning method, machine teaching (MT), for the text-based emotion prediction task. We compare this method experimentally against a more well-studied technique, active learning (AL). Results show the strengths of both approaches over more resource-intensive offline supervised learning. Additionally, applying AL and MT to fine-tune a pre-trained model offers further efficiency gain. We end by recommending research directions which aim to empower users in the learning process. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

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MUWS'2023: The 2nd International workshop on Multimodal human Understanding for the Web and Social Media 23

MUWS'2023: The 2nd International Workshop on Multimodal Huma...

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32nd ACM International Conference on Information and Knowledge Management (CIKM)

作者： Cheema, Gullal S. Hakimov, Sherzod Kastner, Marc A. Garcia, Noa Leibniz Univ Hannover L3S Res Ctr Hannover Germany Univ Potsdam Potsdam Germany Kyoto Univ Kyoto Japan Osaka Univ Osaka Japan

ISBN: (纸本)9798400701245

Multimodal human understanding and analysis is an emerging research area that cuts through several disciplines like computer Vision (CV), natural language processing (nlp), Speech processing, human-computer interaction (hci), and Multimedia. Several multimodal learning techniques have recently shown the benefit of combining multiple modalities in image-text, audio-visual and video representation learning and various downstream multimodal tasks. At the core, these methods focus on modelling the modalities and their complex interactions by using large amounts of data, different loss functions and deep neural network architectures. However, for many Web and Social media applications, there is the need to model the human, including the understanding of human behaviour and perception. For this, it becomes important to consider interdisciplinary approaches, including social sciences, semiotics and psychology. The core is understanding various cross-modal relations, quantifying bias such as social biases, and the applicability of models to real-world problems. Interdisciplinary theories such as semiotics or gestalt psychology can provide additional insights and analysis on perceptual understanding through signs and symbols via multiple modalities. In general, these theories provide a compelling view of multimodality and perception that can further expand computational research and multimedia applications on the Web and Social media. The theme of the MUWS workshop, multimodal human understanding, includes various interdisciplinary challenges related to social bias analyses, multimodal representation learning, detection of human impressions or sentiment, hate speech, sarcasm in multimodal data, multimodal rhetoric and semantics, and related topics. The MUWS workshop will be an interactive event and include keynotes by relevant experts, poster and demo sessions, research presentations and discussion.

关键词： multimodality machine learning image-text relations social media web human understanding semiotics

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Fostering human-AI interaction: development of a Clinical Decision Support System enhanced by eXplainable AI and natural language processing 2

Fostering Human-AI interaction: development of a Clinical De...

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Joint of the 2nd World Conference on eXplainable Artificial Intelligence Late-Breaking Work, Demos and Doctoral Consortium, xAI-2024:LB/D/DC

作者： Bergomi, Laura Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy

Artificial Intelligence (AI) is increasingly integrated into Decision Support Systems (DSS). The explainability of AI-based systems becomes crucial in sensitive and critical domains, such as healthcare, where ethical considerations and reliability are paramount concerns. In the clinical setting, it is important to evaluate how humans and AI can collaborate on cognitive tasks. Collaboration protocols (HAI-CP) allow for the investigation of the usefulness of AI models and their impact on users (both positive and negative). Although research on the application of these methods is blooming, there is little understanding of the impact on clinical decision-making, especially for eXplainable AI (XAI) systems, due to the lack of user studies. Therefore, the goal of this proposal is to develop a clinical DSS enhanced by XAI and natural language processing (nlp): their synergy can add value to the interaction between users and AI, fostering a more linguistically natural, comprehensible, trustworthy, and supporting interfacing, that blends into the existing workflows. This proposal explores potential solutions to tailor natural language explanations and data visualizations to the end-user, improving the comprehensibility of the reasons behind a decision, and increasing the user’s confidence in the decision;investigates and tests possible strategies to "get the patient-in-the-loop";explores uncertainty quantification and counterfactual approaches, and finally assesses the impact on naturalistic (i.e., real-world) decision-making and long-term effects and biases. © 2024 Copyright for this paper by its authors.

关键词： Decision support systems

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