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  • Association for the Advancement of Artificial Intelligence (AAAI)  (2)
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  • Association for the Advancement of Artificial Intelligence (AAAI)  (2)
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  • 1
    Online Resource
    Online Resource
    Association for the Advancement of Artificial Intelligence (AAAI) ; 2016
    In:  Proceedings of the AAAI Conference on Artificial Intelligence Vol. 30, No. 1 ( 2016-03-05)
    In: Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 30, No. 1 ( 2016-03-05)
    Abstract: As the amount of knowledge bases (KBs) grows rapidly, the problem of question answering (QA) over multiple KBs has drawn more attention. The most significant distinction between multiple KB-QA and single KB-QA is that the former must consider the alignments between KBs. The pipeline strategy first constructs the alignments independently, and then uses the obtained alignments to construct queries. However, alignment construction is not a trivial task, and the introduced noises would be passed on to query construction. By contrast, we notice that alignment construction and query construction are interactive steps, and jointly considering them would be beneficial. To this end, we present a novel joint model based on integer linear programming (ILP), uniting these two procedures into a uniform framework. The experimental results demonstrate that the proposed approach outperforms state-of-the-art systems, and is able to improve the performance of both alignment construction and query construction.
    Type of Medium: Online Resource
    ISSN: 2374-3468 , 2159-5399
    Language: Unknown
    Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
    Publication Date: 2016
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  • 2
    Online Resource
    Online Resource
    Association for the Advancement of Artificial Intelligence (AAAI) ; 2020
    In:  Proceedings of the AAAI Conference on Artificial Intelligence Vol. 34, No. 05 ( 2020-04-03), p. 9032-9039
    In: Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 34, No. 05 ( 2020-04-03), p. 9032-9039
    Abstract: This paper focuses on the answer sentence selection task. Unlike previous work, which only models the relation between the question and each candidate sentence, we propose Multi-Perspective Graph Encoder (MPGE) to take the relations among the candidate sentences into account and capture the relations from multiple perspectives. By utilizing MPGE as a module, we construct two answer sentence selection models which are based on traditional representation and pre-trained representation, respectively. We conduct extensive experiments on two datasets, WikiQA and SQuAD. The results show that the proposed MPGE is effective for both types of representation. Moreover, the overall performance of our proposed model surpasses the state-of-the-art on both datasets. Additionally, we further validate the robustness of our method by the adversarial examples of AddSent and AddOneSent.
    Type of Medium: Online Resource
    ISSN: 2374-3468 , 2159-5399
    Language: Unknown
    Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
    Publication Date: 2020
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
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