@inproceedings{barlacchi-etal-2022-focusqa,
title = "{F}ocus{QA}: Open-Domain Question Answering with a Context in Focus",
author = "Barlacchi, Gianni and
Lauriola, Ivano and
Moschitti, Alessandro and
Del Tredici, Marco and
Shen, Xiaoyu and
Vu, Thuy and
Byrne, Bill and
de Gispert, Adri{\`a}",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.381",
doi = "10.18653/v1/2022.findings-emnlp.381",
pages = "5195--5208",
abstract = "We introduce question answering with a cotext in focus, a task that simulates a free interaction with a QA system. The user reads on a screen some information about a topic, and they can follow-up with questions that can be either related or not to the topic; and the answer can be found in the document containing the screen content or from other pages. We call such information context. To study the task, we construct FocusQA, a dataset for answer sentence selection (AS2) with 12,165011unique question/context pairs, and a total of 109,940 answers. To build the dataset, we developed a novel methodology that takes existing questions and pairs them with relevant contexts. To show the benefits of this approach, we present a comparative analysis with a set of questions written by humans after reading the context, showing that our approach greatly helps in eliciting more realistic question/context pairs. Finally, we show that the task poses several challenges for incorporating contextual information. In this respect, we introduce strong baselines for answer sentence selection that outperform the precision of state-of-the-art models for AS2 up to 21.3{\%} absolute points.",
}
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<abstract>We introduce question answering with a cotext in focus, a task that simulates a free interaction with a QA system. The user reads on a screen some information about a topic, and they can follow-up with questions that can be either related or not to the topic; and the answer can be found in the document containing the screen content or from other pages. We call such information context. To study the task, we construct FocusQA, a dataset for answer sentence selection (AS2) with 12,165011unique question/context pairs, and a total of 109,940 answers. To build the dataset, we developed a novel methodology that takes existing questions and pairs them with relevant contexts. To show the benefits of this approach, we present a comparative analysis with a set of questions written by humans after reading the context, showing that our approach greatly helps in eliciting more realistic question/context pairs. Finally, we show that the task poses several challenges for incorporating contextual information. In this respect, we introduce strong baselines for answer sentence selection that outperform the precision of state-of-the-art models for AS2 up to 21.3% absolute points.</abstract>
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%0 Conference Proceedings
%T FocusQA: Open-Domain Question Answering with a Context in Focus
%A Barlacchi, Gianni
%A Lauriola, Ivano
%A Moschitti, Alessandro
%A Del Tredici, Marco
%A Shen, Xiaoyu
%A Vu, Thuy
%A Byrne, Bill
%A de Gispert, Adrià
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F barlacchi-etal-2022-focusqa
%X We introduce question answering with a cotext in focus, a task that simulates a free interaction with a QA system. The user reads on a screen some information about a topic, and they can follow-up with questions that can be either related or not to the topic; and the answer can be found in the document containing the screen content or from other pages. We call such information context. To study the task, we construct FocusQA, a dataset for answer sentence selection (AS2) with 12,165011unique question/context pairs, and a total of 109,940 answers. To build the dataset, we developed a novel methodology that takes existing questions and pairs them with relevant contexts. To show the benefits of this approach, we present a comparative analysis with a set of questions written by humans after reading the context, showing that our approach greatly helps in eliciting more realistic question/context pairs. Finally, we show that the task poses several challenges for incorporating contextual information. In this respect, we introduce strong baselines for answer sentence selection that outperform the precision of state-of-the-art models for AS2 up to 21.3% absolute points.
%R 10.18653/v1/2022.findings-emnlp.381
%U https://aclanthology.org/2022.findings-emnlp.381
%U https://doi.org/10.18653/v1/2022.findings-emnlp.381
%P 5195-5208
Markdown (Informal)
[FocusQA: Open-Domain Question Answering with a Context in Focus](https://aclanthology.org/2022.findings-emnlp.381) (Barlacchi et al., Findings 2022)
ACL
- Gianni Barlacchi, Ivano Lauriola, Alessandro Moschitti, Marco Del Tredici, Xiaoyu Shen, Thuy Vu, Bill Byrne, and Adrià de Gispert. 2022. FocusQA: Open-Domain Question Answering with a Context in Focus. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 5195–5208, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.