Report on the future conversations workshop at CHIIR 2021

Spina, Damiano; Trippas, Johanne R.; Thomas, Paul; Joho, Hideo; Byström, Katriina; Clark, Leigh; Craswell, Nick; Czerwinski, Mary; Elsweiler, David; Frummet, Alexander; Ghosh, Souvick; Kiesel, Johannes; Lopatovska, Irene; McDuff, Daniel; Meyer, Selina; Mourad, Ahmed; Owoicho, Paul; Cherumanal, Sachin Pathiyan; Russell, Daniel M.; Sitbon, Laurianne

doi:10.1145/3476415.3476421

Cited by 7 publications

(4 citation statements)

References 50 publications

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“…Furthermore, these systems can deal with sensitive patient data and thus need to be safeguarded. Voice-only CIS systems may also encounter issues with speech recognition, especially when people are distressed or are in noisy environments (Spina et al, 2021).…”

Section: Conversational Information Seeking In Healthmentioning

confidence: 99%

Conversational Information Seeking

Zamani¹,

Trippas²,

Dalton³

et al. 2022

Preprint

Self Cite

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Conversational information seeking (CIS) is concerned with a sequence of interactions between one or more users and an information system. Interactions in CIS are primarily based on natural language dialogue, while they may include other types of interactions, such as click, touch, and body gestures. This monograph provides a thorough overview of CIS definitions, applications, interactions, interfaces, design, implementation, and evaluation. This monograph views CIS applications as including conversational search, conversational question answering, and conversational recommendation. Our aim is to provide an overview of past research related to CIS, introduce the current state-of-the-art in CIS, highlight the challenges still being faced in the community. and suggest future directions.

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Section: Conversational Information Seeking In Healthmentioning

confidence: 99%

Conversational Information Seeking

Zamani¹,

Trippas²,

Dalton³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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“…These themed blocks have the main presentation to kickstart the session, followed by the discussion of the participants' position statements and roundtable 3 discussion. Workshop participants contribute to a written technical report similar to the Future Conversations workshop at CHIIR'21 [9].…”

Section: Workhop Formatmentioning

confidence: 99%

First Early Career Researchers’ Roundtable for Information Access Research

Trippas

Maxwell

2022

ACM SIGIR Conference on Human Information Interaction and Retrieval

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The COVID-19 pandemic has changed the way we work, study, and conduct research. Ongoing stresses and uncertainties of the pandemic have impacted research activities and collaborations, especially for graduate researchers 1 and Early Career Researchers (ECRs) 2 . It has also changed the way we connect with the broader research communities. For example, in the last year, conferences were either postponed or held online. Even though many conferences implemented social activities, connecting online with peers is hard. Thus, serendipity and forming new bonds or research connections at conferences have been more complex. Indeed, graduate researchers and ECRs have increased challenges connecting and establishing new research connections in online driven environments. This workshops aims to empower graduate and ECRs, make new research connections, and foster a sense of belonging.

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“…While speaking with and listening to computers has a long history within the field of human-computer interaction (HCI), these recent industrial and commercial trends have reinvigorated interest in the voice of the machine [28,95]. In SIGCHI and adjacent spaces, this can be seen in recent workshops (e.g., [15,79,99,104]) and conferences (notably CUI 1 and ACL 2 ).…”

Section: Introductionmentioning

confidence: 99%

Transcending the “Male Code”: Implicit Masculine Biases in NLP Contexts

Seaborn

Chandra

Fabre

2023

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

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Critical scholarship has elevated the problem of gender bias in data sets used to train virtual assistants (VAs). Most work has focused on explicit biases in language, especially against women, girls, femme-identifying people, and genderqueer folk; implicit associations through word embeddings; and limited models of gender and masculinities, especially toxic masculinities, conflation of sex and gender, and a sex/gender binary framing of the masculine as diametric to the feminine. Yet, we must also interrogate how masculinities are "coded" into language and the assumption of "male" as the linguistic default: implicit masculine biases. To this end, we examined two natural language processing (NLP) data sets. We found that when gendered language was present, so were gender biases and especially masculine biases. Moreover, these biases related in nuanced ways to the NLP context. We offer a new dictionary called AVA that covers ambiguous associations between gendered language and the language of VAs.

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Report on the future conversations workshop at CHIIR 2021

Cited by 7 publications

References 50 publications

Conversational Information Seeking

Conversational Information Seeking

First Early Career Researchers’ Roundtable for Information Access Research

Transcending the “Male Code”: Implicit Masculine Biases in NLP Contexts

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