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Clarifying questions are an integral component of modern information retrieval systems, directly impacting user satisfaction and overall system performance.
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Convai3: Generating clarifying questions for open-domain dialogue systems (clariq)
Mohammad Aliannejadi, Julia Kiseleva, Aleksandr Chuklin, Jeff Dalton, and Mikhail Burtsev. 2020 · 2009
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When does relevance mean usefulness and user satisfaction in web search?
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Sudha Rao and Hal Daumé III. 2018 · 2018
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Asking clarifying questions in open-domain information-seeking conversations
Mohammad Aliannejadi, Hamed Zamani, Fabio Crestani, and W. Bruce Croft. 2019 · 2019
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Answer-based adversarial training for generating clarification questions
Sudha Rao and Hal Daumé III. 2019 · 2019
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Guided transformer: Leveraging multiple external sources for representation learning in conversational search
Helia Hashemi, Hamed Zamani, and W. Bruce Croft. 2020 · 2020
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Vera Provatorova, Samarth Bhargav, Svitlana Vakulenko, and Evangelos Kanoulas. 2021 · 2021
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Towards facet-driven generation of clarifying questions for conversational search
Ivan Sekulić, Mohammad Aliannejadi, and Fabio Crestani. 2021a · 2021
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User engagement prediction for clarification in search
Ivan Sekulić, Mohammad Aliannejadi, and Fabio Crestani. 2021b · 2021
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Paul Owoicho, Jeffrey Dalton, Mohammad Aliannejadi, Leif Azzopardi, Johanne R. Trippas, and Svitlana Vakulenko. 2022 · 2022
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Mimics-duo: Offline & online evaluation of search clarification
Leila Tavakoli, Johanne R Trippas, Hamed Zamani, Falk Scholer, and Mark Sanderson. 2022 · 2022
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Analysing the effect of clarifying questions on document ranking in conversational search
Antonios Minas Krasakis, Mohammad Aliannejadi, Nikos Voskarides, and Evangelos Kanoulas. 2020 · 2020
Cited alongside, same era.
ALBERT: A lite BERT for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2020 · 2020
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Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al. 2020 · 2020
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Generating clarifying questions for information retrieval
Hamed Zamani, Susan Dumais, Nick Craswell, Paul Bennett, and Gord Lueck. 2020a · 2020
Cited alongside, same era.
Building and evaluating open-domain dialogue corpora with clarifying questions
Mohammad Aliannejadi, Julia Kiseleva, Aleksandr Chuklin, Jeff Dalton, and Mikhail Burtsev. 2021b · 2021
Cited alongside, same era.
Analysing mixed initiatives and search strategies during conversational search
Mohammad Aliannejadi, Leif Azzopardi, Hamed Zamani, Evangelos Kanoulas, Paul Thomas, and Nick Craswell. 2021a
Cited in the paper.
Generating clarifying questions with web search results
Ziliang Zhao, Zhicheng Dou, Jiaxin Mao, and Ji-Rong Wen. 2022 · 2022
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When and what to ask through world states and text instructions: Iglu nlp challenge solution
Zhengxiang Shi, Jerome Ramos, To Eun Kim, Xi Wang, Hossein A Rahmani, and Aldo Lipani. 2023 · 2023
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Understanding user satisfaction with task-oriented dialogue systems
Clemencia Siro, Mohammad Aliannejadi, and Maarten de Rijke. 2022 · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023 · 2023
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Learning to execute actions or ask clarification questions
Zhengxiang Shi, Yue Feng, and Aldo Lipani. 2022 · 2070
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Ranking clarification questions via natural language inference
Vaibhav Kumar, Vikas Raunak, and Jamie Callan. 2020 · 2096
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