{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,11,8]],"date-time":"2022-11-08T05:29:43Z","timestamp":1667885383398},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"01","license":[{"start":{"date-parts":[[2019,7,17]],"date-time":"2019-07-17T00:00:00Z","timestamp":1563321600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.aaai.org"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Query-document semantic interactions are essential for the success of many cloze-style question answering models. Recently, researchers have proposed several attention-based methods to predict the answer by focusing on appropriate subparts of the context document. In this paper, we design a novel module to produce the query-aware context vector, named Multi-Space based Context Fusion (MSCF), with the following considerations: (1) interactions are applied across multiple latent semantic spaces; (2) attention is measured at bit level, not at token level. Moreover, we extend MSCF to the multi-hop architecture. This unified model is called Enhanced Attentive Reader (EA Reader). During the iterative inference process, the reader is equipped with a novel memory update rule and maintains the understanding of documents through read, update and write operations. We conduct extensive experiments on four real-world datasets. Our results demonstrate that EA Reader outperforms state-of-the-art models.<\/jats:p>","DOI":"10.1609\/aaai.v33i01.33016375","type":"journal-article","created":{"date-parts":[[2019,8,27]],"date-time":"2019-08-27T07:36:21Z","timestamp":1566891381000},"page":"6375-6382","source":"Crossref","is-referenced-by-count":1,"title":["EA Reader: Enhance Attentive Reader for Cloze-Style Question Answering via Multi-Space Context Fusion"],"prefix":"10.1609","volume":"33","author":[{"given":"Chengzhen","family":"Fu","sequence":"first","affiliation":[]},{"given":"Yan","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2019,7,17]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/4600\/4478","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/4600\/4478","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T07:16:38Z","timestamp":1667805398000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/4600"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,17]]},"references-count":0,"journal-issue":{"issue":"01","published-online":{"date-parts":[[2019,7,23]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v33i01.33016375","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2019,7,17]]}}}