R3: Reinforced Ranker-Reader for open-domain Question Answering
In recent years researchers have achieved considerable success applying neural network methods to question answering (QA). These approaches have achieved state of the art results in simplified closed-domain settings such as the SQuAD (Rajpurkar et al. 2016) dataset, which provides a pre-selected pas...
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sg-smu-ink.sis_research-52402025-02-10T08:14:24Z R3: Reinforced Ranker-Reader for open-domain Question Answering WANG, Shuohang YU, Mo GUO, Xiaoxiao WANG, Zhiguo KLINGER, Tim ZHANG, Wei CHANG, Shiyu TESAURO, Gerald ZHOU, Bowen JIANG, Jing In recent years researchers have achieved considerable success applying neural network methods to question answering (QA). These approaches have achieved state of the art results in simplified closed-domain settings such as the SQuAD (Rajpurkar et al. 2016) dataset, which provides a pre-selected passage, from which the answer to a given question may be extracted. More recently, researchers have begun to tackle open-domain QA, in which the model is given a question and access to a large corpus (e.g., wikipedia) instead of a pre-selected passage (Chen et al. 2017a). This setting is more complex as it requires large-scale search for relevant passages by an information retrieval component, combined with a reading comprehension model that “reads” the passages to generate an answer to the question. Performance in this setting lags well behind closed-domain performance. In this paper, we present a novel open-domain QA system called Reinforced Ranker-Reader (R3), based on two algorithmic innovations. First, we propose a new pipeline for open-domain QA with a Ranker component, which learns to rank retrieved passages in terms of likelihood of extracting the ground-truth answer to a given question. Second, we propose a novel method that jointly trains the Ranker along with an answer-extraction Reader model, based on reinforcement learning. We report extensive experimental results showing that our method significantly improves on the state of the art for multiple open-domain QA datasets. 2018-02-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4237 info:doi/10.1609/aaai.v32i1.12053 https://ink.library.smu.edu.sg/context/sis_research/article/5240/viewcontent/Reinforced_Reader_Ranker_2018_AAAI_afv.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Answer extraction Ground truth Neural network method Open domain question answering Question Answering Reading comprehension Databases and Information Systems Theory and Algorithms |
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Answer extraction Ground truth Neural network method Open domain question answering Question Answering Reading comprehension Databases and Information Systems Theory and Algorithms WANG, Shuohang YU, Mo GUO, Xiaoxiao WANG, Zhiguo KLINGER, Tim ZHANG, Wei CHANG, Shiyu TESAURO, Gerald ZHOU, Bowen JIANG, Jing R3: Reinforced Ranker-Reader for open-domain Question Answering |
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In recent years researchers have achieved considerable success applying neural network methods to question answering (QA). These approaches have achieved state of the art results in simplified closed-domain settings such as the SQuAD (Rajpurkar et al. 2016) dataset, which provides a pre-selected passage, from which the answer to a given question may be extracted. More recently, researchers have begun to tackle open-domain QA, in which the model is given a question and access to a large corpus (e.g., wikipedia) instead of a pre-selected passage (Chen et al. 2017a). This setting is more complex as it requires large-scale search for relevant passages by an information retrieval component, combined with a reading comprehension model that “reads” the passages to generate an answer to the question. Performance in this setting lags well behind closed-domain performance. In this paper, we present a novel open-domain QA system called Reinforced Ranker-Reader (R3), based on two algorithmic innovations. First, we propose a new pipeline for open-domain QA with a Ranker component, which learns to rank retrieved passages in terms of likelihood of extracting the ground-truth answer to a given question. Second, we propose a novel method that jointly trains the Ranker along with an answer-extraction Reader model, based on reinforcement learning. We report extensive experimental results showing that our method significantly improves on the state of the art for multiple open-domain QA datasets. |
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WANG, Shuohang YU, Mo GUO, Xiaoxiao WANG, Zhiguo KLINGER, Tim ZHANG, Wei CHANG, Shiyu TESAURO, Gerald ZHOU, Bowen JIANG, Jing |
author_facet |
WANG, Shuohang YU, Mo GUO, Xiaoxiao WANG, Zhiguo KLINGER, Tim ZHANG, Wei CHANG, Shiyu TESAURO, Gerald ZHOU, Bowen JIANG, Jing |
author_sort |
WANG, Shuohang |
title |
R3: Reinforced Ranker-Reader for open-domain Question Answering |
title_short |
R3: Reinforced Ranker-Reader for open-domain Question Answering |
title_full |
R3: Reinforced Ranker-Reader for open-domain Question Answering |
title_fullStr |
R3: Reinforced Ranker-Reader for open-domain Question Answering |
title_full_unstemmed |
R3: Reinforced Ranker-Reader for open-domain Question Answering |
title_sort |
r3: reinforced ranker-reader for open-domain question answering |
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Institutional Knowledge at Singapore Management University |
publishDate |
2018 |
url |
https://ink.library.smu.edu.sg/sis_research/4237 https://ink.library.smu.edu.sg/context/sis_research/article/5240/viewcontent/Reinforced_Reader_Ranker_2018_AAAI_afv.pdf |
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