Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
2-2018
Abstract
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.
Keywords
Answer extraction, Ground truth, Neural network method, Open domain question answering, Question Answering, Reading comprehension
Discipline
Databases and Information Systems | Theory and Algorithms
Research Areas
Data Science and Engineering
Publication
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, Palo Alto, CA, 2018 February 2-7
First Page
5981
Last Page
5998
ISBN
9781577358008
Identifier
10.1609/aaai.v32i1.12053
Publisher
AAAI Press
City or Country
Palo Alto, CA
Citation
WANG, Shuohang; YU, Mo; GUO, Xiaoxiao; WANG, Zhiguo; KLINGER, Tim; ZHANG, Wei; CHANG, Shiyu; TESAURO, Gerald; ZHOU, Bowen; and JIANG, Jing.
R3: Reinforced Ranker-Reader for open-domain Question Answering. (2018). Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, Palo Alto, CA, 2018 February 2-7. 5981-5998.
Available at: https://ink.library.smu.edu.sg/sis_research/4237
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1609/aaai.v32i1.12053
