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Contextualized Sparse Representations for Real-Time Open-Domain Question Answering
[article]
2020
arXiv
pre-print
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Open-domain question answering can be formulated as a phrase retrieval problem, in which we can expect huge scalability and speed benefit but often suffer from low accuracy due to the limitation of existing ...
In this paper, we aim to improve the quality of each phrase embedding by augmenting it with a contextualized sparse representation (Sparc). ...
We thank the members of Korea University, University of Washington, NAVER Clova AI, and the anonymous reviewers for their insightful comments. ...
arXiv:1911.02896v2
fatcat:o4mojj5tjnhtjffqacpdpa72ey
Contextualized Sparse Representations for Real-Time Open-Domain Question Answering
2020
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
unpublished
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Open-domain question answering can be formulated as a phrase retrieval problem, in which we can expect huge scalability and speed benefit but often suffer from low accuracy due to the limitation of existing ...
In this paper, we aim to improve the quality of each phrase embedding by augmenting it with a contextualized sparse representation (SPARC). ...
We thank the members of Korea University, University of Washington, NAVER Clova AI, and the anonymous reviewers for their insightful comments. ...
doi:10.18653/v1/2020.acl-main.85
fatcat:2xbfiyjiuzg2tkawpllnwrliha
Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index
2019
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
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Existing open-domain question answering (QA) models are not suitable for real-time usage because they need to process several long documents on-demand for every input query, which is computationally prohibitive ...
In this paper, we introduce query-agnostic indexable representations of document phrases that can drastically speed up open-domain QA. ...
We thank the members of UW NLP, Google AI, and the anonymous reviewers for their insightful comments. ...
doi:10.18653/v1/p19-1436
dblp:conf/acl/SeoLKPFH19
fatcat:khayf34qmzhfjebghkopaurpmi
SPARTA: Efficient Open-Domain Question Answering via Sparse Transformer Matching Retrieval
[article]
2020
arXiv
pre-print
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We introduce SPARTA, a novel neural retrieval method that shows great promise in performance, generalization, and interpretability for open-domain question answering. ...
We validated our approaches on 4 open-domain question answering (OpenQA) tasks and 11 retrieval question answering (ReQA) tasks. ...
Conclusion In short, we propose SPARTA, a novel ranking method, that learns sparse representation for better open-domain QA. ...
arXiv:2009.13013v1
fatcat:qxsacmzhvrd3vjmsbf2tfu237a
NeuralQA: A Usable Library for Question Answering (Contextual Query Expansion + BERT) on Large Datasets
[article]
2020
arXiv
pre-print
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Existing tools for Question Answering (QA) have challenges that limit their use in practice. ...
Code and documentation for NeuralQA is available as open source on Github (https://github.com/victordibia/neuralqaGithub). ...
Acknowledgments The author thanks Melanie Beck, Andrew Reed, Chris Wallace, Grant Custer, Danielle Thorpe and other members of the Cloudera Fast Forward team for their valuable feedback. ...
arXiv:2007.15211v2
fatcat:q5wnqc6awzdw7nsxoyl4ehlb4u
Semantic Models for the First-stage Retrieval: A Comprehensive Review
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2103.04831v2 fatcat:7dminrg6cvefbcrggzf5dtfezaOther Versions
We believe it is the right time to survey current status, learn from existing methods, and gain some insights for future development. ...
Moreover, we identify some open challenges and envision some future directions, with the hope of inspiring more researches on these important yet less investigated topics. ...
approaches for open-domain question answering. ...
arXiv:2103.04831v3
fatcat:6qa7hvc3jve3pcmo2mo4qsiefq
Dynamic Adaptive Network Intelligence
[article]
2015
arXiv
pre-print
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We report state-of-the-art results for DANI over question answering tasks in the bAbI dataset that have proved difficult for contemporary approaches to learning representation (Weston et al., 2015). ...
Accurate representational learning of both the explicit and implicit relationships within data is critical to the ability of machines to perform more complex and abstract reasoning tasks. ...
Although we report the application of DANI as an independent framework for learning representation, we recognize that our system could be employed to condition the input and intermediate layers of neural ...
arXiv:1511.06379v1
fatcat:2obpgubyjnfqhl7hwpw2tfh6qy
Adaptable Closed-Domain Question Answering Using Contextualized CNN-Attention Models and Question Expansion
2022
IEEE Access
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In closed-domain Question Answering (QA), the goal is to retrieve answers to questions within a specific domain. ...
Moreover, we include candidate answer identification and question expansion techniques for context reduction and rewriting ambiguous questions. ...
[16] introduced Dense-Sparse Phrase Index (DENSPI), an indexable query-agnostic phrase representation model for real-time open-domain QA on SQuAD. ...
doi:10.1109/access.2022.3170466
fatcat:64rbm4tiqfb3hi4law253ioina
Adaptive Information Seeking for Open-Domain Question Answering
[article]
2021
arXiv
pre-print
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Information seeking is an essential step for open-domain question answering to efficiently gather evidence from a large corpus. ...
In this paper, we propose a novel adaptive information-seeking strategy for open-domain question answering, namely AISO. ...
Introduction Open-domain question answering (QA) (Voorhees et al., 1999) is a task of answering questions using a large collection of texts (e.g., Wikipedia). ...
arXiv:2109.06747v1
fatcat:n52qdsuzqjf3lpeexuosfuilh4
Dense-to-Question and Sparse-to-Answer: Hybrid Retriever System for Industrial Frequently Asked Questions
2022
Mathematics
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be continuously input for the FAQ system's maintenance. ...
The term "Frequently asked questions" (FAQ) refers to a query that is asked repeatedly and produces a manually constructed response. ...
Acknowledgments: Many thanks to KU NMT Group for taking the time to proofread this article.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/math10081335
fatcat:i2jlowhdgrd53hgegetw6uaozu
Accelerating Real-Time Question Answering via Question Generation
[article]
2021
arXiv
pre-print
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To reduce the computational cost and accelerate real-time question answering (RTQA) for practical usage, we propose to remove all the neural networks from online QA systems, and present Ocean-Q (an Ocean ...
Although deep neural networks have achieved tremendous success for question answering (QA), they are still suffering from heavy computational and energy cost for real product deployment. ...
(i) We set up the first benchmark under the setting of answering open-domain questions without neural networks in real time. ...
arXiv:2009.05167v2
fatcat:sqy6s5f2jvbyfpltataeayoozm
Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering
[article]
2021
arXiv
pre-print
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Open-domain Question Answering (OpenQA) is an important task in Natural Language Processing (NLP), which aims to answer a question in the form of natural language based on large-scale unstructured documents ...
We hope our work would enable researchers to be informed of the recent advancement and also the open challenges in OpenQA research, so as to stimulate further progress in this field. ...
Specifically, textual QA is studied under two task settings based on the availability of contextual information, i.e. Machine Reading Comprehension (MRC) and Open-domain QA (OpenQA). ...
arXiv:2101.00774v3
fatcat:6evkg5cikjdp5fsi3ou3iqqkyq
Answering Questions on COVID-19 in Real-Time
[article]
2020
arXiv
pre-print
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2020 pre-print arXiv:2006.15830v1 fatcat:ye5we7sczzaqvpzujmp6l6fam4
answers to questions in real-time. ...
We hope our system will be able to aid researchers in their search for knowledge and information not only for COVID-19, but for future pandemics as well. ...
We thank the members of Korea University for the helpful comments and Kyle Lo (Allen Institute for Artificial Intelligence) for the insightful discussion. ...
arXiv:2006.15830v2
fatcat:36sbtup3rrgdtkqrqnw2i67qhi
Differentiable Reasoning over a Virtual Knowledge Base
[article]
2020
arXiv
pre-print
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We also describe a pretraining scheme for the contextual representation encoder by generating hard negative examples using existing knowledge bases. ...
At each step the module uses a combination of sparse-matrix TFIDF indices and a maximum inner product search (MIPS) on a special index of contextual representations of the mentions. ...
End-to-end open-domain question answering with bertserini. arXiv preprint arXiv:1902.01718, 2019. Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W. ...
arXiv:2002.10640v1
fatcat:2fcnpovwjjfdvfl36nvhoj3u54
A Survey on Machine Reading Comprehension Systems
[article]
2020
arXiv
pre-print
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Its goal is to develop systems to answer the questions regarding a given context. ...
Our investigations demonstrate that the focus of research has changed in recent years from answer extraction to answer generation, from single to multi-document reading comprehension, and from learning ...
(A: Answer, P: passage, Q: Question)
DATASET
OPEN/CLOSE
DOMAIN
LANGUAGE
QUESTION
TYPE
CONTEXT
TYPE
ANSWER
TYPE
#QUESTION
#CONTEXT
COLLECT DATA
QUESTION
CLASSIFICATION
LINK ADDRESS ...
arXiv:2001.01582v2
fatcat:hb54svswpvgl5hpp5tesx2koca
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