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Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2021,11,30]]},"abstract":"<jats:p>\n            The task of\n            <jats:bold>semantic role labeling<\/jats:bold>\n            (\n            <jats:bold>SRL<\/jats:bold>\n            ) is dedicated to finding the predicate-argument structure. Previous works on SRL are mostly supervised and do not consider the difficulty in labeling each example which can be very expensive and time-consuming. In this article, we present the first neural unsupervised model for SRL. To decompose the task as two argument related subtasks, identification and clustering, we propose a pipeline that correspondingly consists of two neural modules. First, we train a neural model on two syntax-aware statistically developed rules. The neural model gets the relevance signal for each token in a sentence, to feed into a BiLSTM, and then an adversarial layer for noise-adding and classifying simultaneously, thus enabling the model to learn the semantic structure of a sentence. Then we propose another neural model for argument role clustering, which is done through clustering the learned argument embeddings biased toward their dependency relations. Experiments on the CoNLL-2009 English dataset demonstrate that our model outperforms the previous state-of-the-art baseline in terms of non-neural models for argument identification and classification.\n          <\/jats:p>","DOI":"10.1145\/3461613","type":"journal-article","created":{"date-parts":[[2021,8,12]],"date-time":"2021-08-12T16:57:58Z","timestamp":1628787478000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Neural Unsupervised Semantic Role Labeling"],"prefix":"10.1145","volume":"20","author":[{"given":"Kashif","family":"Munir","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, 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