Reassessing combinatorial productivity exhibited by simple recurrent networks in language acquisition
Refereed conference paper presented and published in conference proceedings


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摘要it has long been criticized that connectionist models are inappropriate models for language acquisition since one of the important properties, the property of generalization beyond the training space, cannot be exhibited by the networks. Recently van der Velde et al. have discussed the issue of the combinatorial productivity, arguing that simple recurrent networks (SRNs) fail in this regard. They have attempted to show that performance of SRNs on generalization is limited to word-word association. In this paper, we report our follow-up study with two simulations demonstrating that (i) bi-gram does not play the dominant role as claimed (ii) SRNs are indeed able to exhibit combinatorial productivity when appropriately trained.
著者Wong FCK, Minett JW, Wang WSY
會議名稱IEEE International Joint Conference on Neural Network
會議開始日16.07.2006
會議完結日21.07.2006
會議地點Vancouver
會議國家/地區加拿大
詳細描述IEEE
出版年份2006
月份1
日期1
出版社IEEE
頁次1596 - 1603
國際標準書號978-0-7803-9490-2
國際標準期刊號1098-7576
語言英式英語
Web of Science 學科類別Computer Science; Computer Science, Artificial Intelligence

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