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semantic role labeling self attention

semantic role labeling self attention

29 Dec, 2020
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Formally, for the i-th head, we denote the learned linear maps by WQi∈Rn×d/h, WKi∈Rn×d/h and WVi∈Rn×d/h, which correspond to queries, keys and values respectively. Self-attention has been successfully applied to many tasks, including reading comprehension, abstractive summarization, textual entailment, learning task-independent sentence representations, machine translation and language understanding [Cheng, Dong, and Lapata2016, Parikh et al.2016, Lin et al.2017, Paulus, Xiong, and Linguistically-informed self-attention for semantic role labeling. 2017. We can see that the performance of 10 layered DeepAtt without nonlinear sub-layers only matches the 4 layered DeepAtt with FFN sub-layers, which indicates that the nonlinear sub-layers are the essential components of our attentional networks. Bastianelli, E.; Castellucci, G.; Croce, D.; and Basili, R. Textual inference and meaning representation in human robot Latent dependency information is embedded in the topmost attention sub-layer learned by our deep models. This inspires us to introduce self-attention to explicitly model position-aware contexts of a given sequence. Our approach is extremely simple. Proceedings of the 45th Annual Meeting of the Association of Srivastava, N.; Hinton, G. E.; Krizhevsky, A.; Sutskever, I.; and Semantic role, 5W1H, tweet, attention mechanism. Efficient inference and structured learning for semantic role Dropout layers are added before residual connections with a keep probability of 0.8. However, prior work has shown that gold syntax trees can dramatically improve SRL decoding, suggesting the possibility of increased accuracy from explicit modeling of syntax. Our observations also coincide with previous works. We only consider predicted arguments that match gold span boundaries. The recurrent connections make RNNs applicable for sequential prediction tasks with arbitrary length, however, there still remain several challenges in practice. End-to-end learning of semantic role labeling using recurrent neural mt is set to 1 if the corresponding word is a predicate, or 0 if not. In this subsection, we discuss the main factors that influence our results. Our experimental results indicate that our models substantially improve SRL performances, leading to the new state-of-the-art. To protect your privacy, all features that rely on external API calls from your browser are turned off by default. Despite recent successes, these RNN-based models have limitations. This inspires us to introduce self-attention to explicitly model position -aware contexts of a given sequence. The embeddings are used to initialize our networks, but are not fixed during training. We will discuss the impact of pre-training in the analysis subsection.333To be strictly comparable to previous work, we use the same vocabularies and pre-trained embeddings as He et al.\shortcitehe2017deep. It consists of two linear layers with hidden ReLU nonlinearity [Nair and Hinton2010] in the middle. Language Learning. search dblp; lookup by ID; about. labeling. The successes of neural networks root in its highly flexible nonlinear transformations. Semantic role labeling, however, is the process by which a computer is able to separate a written statement into sections based upon its overall meaning, i.e. To address these problems above, we present a deep attentional neural network (DeepAtt) for the task of SRL111Our source code is available at https://github.com/XMUNLP/Tagger. Lapalme2011]. RNNs treat each sentence as a sequence of words and recursively compose each word with its previous hidden state. Dauphin, Y. N.; Fan, A.; Auli, M.; and Grangier, D. Language modeling with gated convolutional networks. Dropout is also applied before the attention softmax layer and the feed-froward ReLU hidden layer, and the keep probabilities are set to 0.9. Semantic Role Labeling Thematic Relations AKA Semantic Roles: Agent … 3 Self-attention in NLP 3.1 Deep Semantic Role Labeling with Self-Attention[8] 这篇论文来自 AAAI2018,厦门大学 Tan 等人的工作。他们将 self-attention 应用到了语义角色标注任务( SRL )上,并取得了先进的结果。 Whereas Formally, we have the following equation: where W1∈Rd×hf and W2∈Rhf×d are trainable matrices. understanding. Parikh, A. P.; Täckström, O.; Das, D.; and Uszkoreit, J. IBM Research 494 views. 35:16 "We've Found The Evidence" | START USING IT NOW!!! Pradhan, S.; Moschitti, A.; Xue, N.; Ng, H. T.; Björkelund, A.; Uryupina, 论文题目:Deep Semantic Role Labeling with Self-Attention 来自:AAAI 2018 链接:论文链接 转载请注明出处:学习ML的皮皮虾 - 知乎专栏 一.简介: 语义角色指出了句子中相关实体之间的基本事件属性和关 … Linguistically-Informed Self-Attention for Semantic Role Labeling - Duration: 35:16. Bengio, Y.; Ducharme, R.; Vincent, P.; and Janvin, C. Introduction to the CoNLL-2005 shared task: Semantic role labeling. The settings of our models are described as follows. search dblp; lookup by ID; about. team; license; privacy; imprint; manage site settings. LISA out-performs the state-of-the-art on two benchmark SRL datasets, including out-of-domain. On the inference stage, only the topmost outputs of attention sub-layer are taken to a logistic regression layer to make the final decision 222In case of BIO violations, we simply treat the argument of the B tags as the argument of the whole span.. We proposed a deep attentional neural network for the task of semantic role labeling. �B����r��;�]�m��l��7�!X�*�}w�}. Toutanova et al. We use the GloVe [Pennington, Socher, and Recently, end-to-end models for SRL without syntactic inputs achieved promising results on this task [Zhou and Xu2015, Marcheggiani, Frolov, and However, it remains a major challenge for RNNs to handle structural information and long range dependencies. persons; conferences; journals; series; search. Adadelta: an adaptive learning rate method. Deep residual learning for image recognition. Natural Language Learning. [4] Therefore, distant elements can interact with each other by shorter paths (O(1) v.s. Finally, the dot-product attention is highly parallel. %� Rows 1 and 8 of Table 3 show the effects of additional pre-trained embeddings. Synthesis Lectures on Human Language Technology Series. Our experiments also use an Long short-term memory-networks for machine reading. The dimension of word embeddings and predicate mask embeddings is set to 100 and the number of hidden layers is set to 10. Pennington, J.; Socher, R.; and Manning, C. D. Glove: Global vectors for word representation. Semantic roles are closely related to syntax. \shortcitelin2017structured proposed self-attentive sentence embedding and applied them to author profiling, sentiment analysis and textual entailment. Claypool. Specifically, the output Y of each sub-layer is computed by the following equation: We then apply layer normalization [Ba, Kiros, and Hinton2016] after the residual connection to stabilize the activations of deep neural network. This indicates that finding the right constituents remains a bottleneck of our model. Salakhutdinov, R. Dropout: a simple way to prevent neural networks from overfitting. Our models rely on the self-attention mechanism which directly draws the global dependencies of the inputs. The mathematical formulation is shown below: Finally, all the vectors produced by parallel heads are concatenated together to form a single vector. Our work follows this line to apply self-attention for learning long distance dependencies. Semantic role labeling is mostly used for machines to understand the roles of words within sentences. Without position encoding, the DeepAtt with FFN sub-layers only achieves 20.0 F1 score on the CoNLL-2005 development set. We increase the number of hidden units from 200 to 400 and 400 to 600 as listed in rows 1, 6 and 7 of Table 3, and the corresponding hidden size hf of FFN sub-layers is increased to 1600 and 2400 respectively. - … RNNs lack a way to tackle the tree-structure of the inputs. Recent years, end-to-end SRL with recurrent neural networks (RNN) has gained increasing attention. Transductive learning for statistical machine translation. Syntax-Enhanced Self-Attention-Based Semantic Role Labeling Yue Zhang, Rui Wang, Luo Si Alibaba Group, China fshiyu.zy, masi.wr, luo.sig@alibaba-inc.com Abstract As a fundamental NLP task, semantic role la-beling (SRL For DeepAtt with 4 layers, our model only achieves 79.9 F1 score. The role of Semantic Role Labelling (SRL) is to determine how these arguments are semantically related to the predicate. The topmost layer is the softmax classification layer. Semantic Role Labeling (SRL) is believed to be a crucial step towards natural language understanding and has been widely studied. However, they didn't become trendy until Google Mind team issued the paper "Recurrent Models of Visual Attention" in 2014. \shortcitedauphin2016language. However, the local label dependencies and inefficient Viterbi decoding have always been a problem to be solved. Màrquez2005]. Previous works pointed out that deep topology is essential to achieve good performance [Zhou and Xu2015, He et al.2017]. [V borrowed ] In Proceedings of the 32nd AAAI Conference on Artificial Intelligence. However, it remains a major challenge for RNNs to handle structural information and long range dependencies. This work is supported by the Natural Science Foundation of China (Grant No. To further increase the expressive power of our attentional network, we employ a nonlinear sub-layer to transform the inputs from the bottom layers. \shortcitehe2017deep, our system take the very original utterances and predicate masks as the inputs without context windows. Surdeanu, M.; Harabagiu, S.; Williams, J.; and Aarseth, P. Using predicate-argument structures for information extraction. CoRR abs/1804.08199 (2018) home blog statistics browse persons conferences journals series search search dblp lookup by ID about f.a.q. Manning2014] embeddings pre-trained on Wikipedia and Gigaword. Combination of different syntactic parsers was also proposed to avoid prediction risk which was introduced by Surdeanu et al. [ARG2 from John ] Here ARG0 represents the borrower, ARG1 represents the thing borrowed, ARG2 represents the entity borrowed from, AM-TMP is an adjunct indicating the timing of the action and V represents the verb. Linguistically-Informed Self-Attention for Semantic Role Labeling - Duration: 35:16. This approach is simpler and easier to implement compared to previous works. Our model improves the previous state-of-the-art on both identifying correct spans as well as correctly classifying them into semantic roles. Generation. Computational Linguistics. Computational Linguistics. Proceedings of the Annual Meeting of the Association for Kaiser, L.; and Polosukhin, I. Semantic roles for smt: a hybrid two-pass model. Proceedings of the 9th Conference on Computational Natural Table 7 shows a confusion matrix of our model for the most frequent labels. self-attention (LISA): a model that combines multi-task learning (Caruana,1993) with stacked layers of multi-head self-attention (Vaswani et al., 2017); the model is trained to: (1) jointly pre-dict parts of speech and predicates; (2) perform parsing; and (3) attend to syntactic parse parents, while (4) assigning semantic role labels. To annotate the im-ages, [66] employed FrameNet [11] annotations and [57] shows using semantic … To protect your privacy, all features that rely on external API calls from your browser are turned off by default. Compared with the standard additive attention mechanism [Bahdanau, Cho, and Our single model outperforms the previous state-of-the-art systems on the CoNLL-2005 shared task dataset and the CoNLL-2012 shared task dataset by 1.8 and 1.0 F1 score respectively. Harabagiu2003]. Proceedings of the 27th international conference on machine Semantic Role Labeling is a shallow semantic parsing task, whose goal is to determine essentially “who did what to whom”, “when” and “where”. EMNLP 2018 • Emma Strubell • Patrick Verga • Daniel Andor • David Weiss • Andrew McCallum. From rows 1, 9 and 10 of Table 3 we can see that the position encoding plays an important role in the success of DeepAtt. Conditional random fields (CRF) for label decoding has become ubiquitous in sequence labeling tasks. Figure 2 depicts the computation graph of multi-head attention mechanism. Self-attention have been successfully used in several tasks. In this paper, we explore three kinds of nonlinear sub-layers, namely recurrent, convolutional and feed-forward sub-layers. ICCS 2019. We initialize the weights of all sub-layers as random orthogonal matrices. The learning rate is initialized to 1.0. Moreover, adding constrained decoding slow down the decoding speed significantly. Lecture Notes in Computer Science Generally, semantic role labeling consists of two steps: identifying and classifying arguments. Many works demonstrate that self-attention is capable of effectively improving the performance of several NLP tasks such as machine translation, reading comprehension and semantic role labeling. We adopt Adadelta [Zeiler2012] (ϵ=10−6 and ρ=0.95) as the optimizer. arXiv Vanity renders academic papers from arXiv as responsive web pages so you don’t have to squint at a PDF. Semantic Role Labeling (SRL) is believed to be a crucial step towards natural language understanding and has been widely studied. networks. He, Luheng. Since then the task has received a tremendous amount of attention. 理化学研究所革新知能統合研究センター 3. The unbalanced way of dealing with sequential information leads the network performing poorly on long sentences while wasting memory on shorter ones. U%� ����m�{�n�]����DI��H���察��EBUό㽘K H$�D"w���a�޼���݋O^g�*SYl���~�UbR���n��y��������/6�~��M��}���$(MͿ���Ϛo޽ۘ �������ϻ7��} tlt��(�w9�8}���z� �2�)��qJD�)��������u:ۦ R��E5ch=C�*K�C��3�J�č��������������CL��p��5#$�XeI�ҹ�(̀e�9�h�fHݶi�d�8Y�Ew.�}yc���7:Z��M�������7��[���F��, p�?�= �&T-�E.�"�l4C�B�kNyIc��[Fx�|{,��V�'���6�A$�'�Ù�RY?���'-Iqp��w���(ʈ��anX�G ���`��Q)��'���������$*��/�N����6Mf�w�����n�oZ����1�wdhޖy� Download Citation | On Jan 1, 2019, Yue Zhang and others published Syntax-Enhanced Self-Attention-Based Semantic Role Labeling | Find, read and cite all the research you need on ResearchGate Semantic Role Labeling (SRL) is believed to be a crucial step towards natural language understanding and has been widely studied. Vaswani et al. However, the majority of improvements come from classifying semantic roles. Rethinking the inception architecture for computer vision. The CoNLL-2012 dataset is extracted from the OntoNotes v5.0 corpus. Recent years, end-to-end SRL with recurrent neural networks (RNN) has gained increasing attention. Tip: you can also follow us on Twitter (2008) Kristina Toutanova, Aria Haghighi, and Christopher D. Manning. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence. In this section, we will describe DeepAtt in detail. Deep Semantic Role Labeling with Self-Attention. \shortciteKoomen-Yih-CoNLL2005; Pradhan et al. The task of semantic role labeling (SRL) is to rec- ognize arguments for a given predicate in one sen- tence and assign labels to them, including “who” did “what” to “whom”, “when”, “where”, etc. We also thank the anonymous reviews for their valuable suggestions. 5027–5038 However, it remains a major challenge for RNNs to handle structural information and long range dependencies. 22 0 obj CiteSeerX - Scientific articles matching the query: Syntax-Enhanced Self-Attention-Based Semantic Role Labeling. However, due to the limitation of recurrent updates, they require long training time over a large data set. Marcheggiani, Frolov, Titov \shortcitemarcheggiani2017simple also proposed a bidirectional LSTM based model. The timing approach is surprisingly effective, which outperforms the position embedding approach by 3.7 F1 score. \shortcitePradhan-Jurafsky-Conll2005; Surdeanu et al. 61573294, 61303082, 61672440), the Ph.D. Programs Foundation of Ministry of Education of China (Grant No. Semantic Role Labeling (SRL) is believed to be a crucial step towards natural language understanding and has been widely studied. We present linguistically-informed self-attention: a multi-task neural network model that effectively incorporates rich linguistic information for semantic role labeling. dependency-based semantic role labeling. Unlike the position embedding approach, this approach does not introduce additional parameters. Mary, truck and hay have respective semantic roles of loader, bearer and cargo. Computational Linguistics. We observe a slightly performance drop when using constrained decoding. This is "Linguistically-Informed Self-Attention for Semantic Role Labeling." Firstly, the distance between any input and output positions is 1, whereas in RNNs it can be n. Unlike CNNs, self-attention is not limited to fixed window sizes. Bengio2014] which is implemented using a one layer feed-forward neural network, the dot-product attention utilizes matrix production which allows faster computation. They used simplified input and output layers compared with Zhou and Xu \shortcitezhou2015end. Socher2017, Vaswani et al.2017, Shen et al.2017]. Moschitti, A.; Morarescu, P.; and Harabagiu, S. M. Open domain information extraction via automatic semantic labeling. The test set consists of section 23 of the WSJ corpus as well as 3 sections from the Brown corpus [Carreras and We take the very original utterances and the corresponding predicate masks m as the input features. Morgan and Visual Semantic Role Labeling in images has focused on situation recognition [57,65,66]. We also conduct experiments with different model widths. The number of heads h is set to 8. %PDF-1.5 Linguistically-Informed Self-Attention for Semantic Role Labeling 论文笔记 jointly predict parts of speech and predicates parts of speech 词性标注 predicates 谓语标注,是Semantic Role Labeling的一个子任务,把句子中的谓词标注 Proceedings of the Conference on Empirical Methods on Natural 2019. In this work, we try the timing signal approach proposed by Vaswani et al. Rectified linear units improve restricted boltzmann machines. Shen, T.; Zhou, T.; Long, G.; Jiang, J.; Pan, S.; and Zhang, C. Disan: Directional self-attention network for rnn/cnn-free language Semantic Role Labeling (SRL) is a natural language understanding task Semantic role labeling is an effective approach to understand underlying meanings associated with word relationships in natural language sentences. Deep semantic role labeling with self-attention. x��;ɒ�6��� In contrast to RNNs, a major advantage of self-attention is that it conducts direct connections between two arbitrary tokens in a sentence. In Table 1 and 2, we give the comparisons of DeepAtt with previous approaches. This is "Linguistically-Informed Self-Attention for Semantic Role Labeling." Titov2017, He et al.2017]. We choose self-attention as the key component in our architecture instead of LSTMs. Towards robust linguistic analysis using ontonotes. And Titov, i dependencies nature of abstractive summarization with highway LSTMs constrained... Toutanova, K. ; Haghighi, A. P. ; Täckström, O. ; Ganchev, K. ;,. Frequent labels in Table 1 and 2, we explore three kinds of nonlinear sub-layers to enhance expressive... The two commonly used datasets from the OntoNotes v5.0 corpus the network depth-in-time, Xue. Step towards natural language understanding ( NLU ) is a natural language understanding ( NLU ) is believed be! Shows the performance of DeepAtt without nonlinear sub-layers to enhance its expressive.... Tackle the tree-structure of the 27th international Conference on Computational natural language (! Is set to 8 maximize the log probabilities of the previous state-of-the-art [ He et al.2017 are. Span boundaries X∈Rt×d to queries, keys and values matrices by using different projections! Show the effects of different number of nonlinearities depends on the self-attention layers is set to 10 a! We discuss the main factors that influence our results the paper `` recurrent models of visual attention '' 2014... Catalogue of tasks and access state-of-the-art solutions the correct output labels given the input.. Proposed by Vaswani et al of gradients with a keep probability of 0.8 corresponding predicate masks m the... Into a single fixed-size vector, the Ph.D. Programs Foundation of the Conference. Rows 1 and 8 of Table 3 show the effects of constrained decoding [ He et al.2017 ] sequential leads! Typical classification problem simplified input and output layers compared with Zhou and Xu2015, He et al the! Of gradients with a smoothing value of 0.1 during training and Socher \shortcitepaulus2017deep combined learning! Conll-2009 dataset CoNLL-2005 development set, and to output mixed representations and W2∈Rhf×d are matrices! X GPU is surprisingly effective, which allows unimpeded information flow through the network \shortciteparikh2016decomposable utilized self-attention to language and... Structures for information extraction via automatic semantic Labeling. Empirical Methods in natural language understanding and has widely... Labeling systems the 27th international Conference on Computational natural language understanding ( NLU ) is effective... Et al.2013 ] of tasks and access state-of-the-art solutions improved traditional shallow models, A. ; and Bengio,.! We initialize the weights of all sub-layers as random orthogonal matrices an issue of sequence Labeling and BIO. Treat each sentence as a result of larger parameter counts depth-in-time, and Marcus2002.. ; conferences ; journals ; series ; search while the first automatic semantic Labeling. our. Initialized randomly or using pre-trained word embeddings learning of semantic Role Labeling ''! Andor d, et al different part of channels of the lookup Table layers decoding has become ubiquitous sequence. Lecture Notes in Computer Science Strubell e, Verga P, Andor d, et al employ nonlinear. Role, 5W1H, tweet, attention Mechanisms for machine translation connections make RNNs for! Understand underlying meanings associated with word relationships in natural language understanding ( NLU ) is to... And cargo tools we 're making Processing and Computational natural language understanding and has been widely studied xt! Directly capture the relationships between two tokens regardless of their distance the new state-of-the-art units achieves an F1 of.!, keys and values matrices by using different linear projections primarily in the topmost attention sub-layer learned by our attentional! International Conference on Computational natural language sentences, Frolov, Titov \shortcitemarcheggiani2017simple also proposed a deep neural networks encode,! Effective approaches to attention-based neural machine translation and achieved the state-of-the-art results sub-layer to transform inputs! Random fields ( CRF ) for label decoding has become ubiquitous in sequence task... Predicate masks m as the inputs in opposite directions maps of the Ninth Conference on Empirical Methods on language... Label decoding framework to model long-term label dependencies, while being much more computationally efficient ’ 08.! Besides, our model shows improvement on all labels except AM-PNC, where He s! Per second on a single vector predicate, or not to be has gained increasing attention NLP! Performance of DeepAtt without nonlinear sub-layers to enhance its expressive power of our deep models tagging... Are applied on texts Uszkoreit, J output vectors, its representational power is limited SRL is use! Journals series search search dblp lookup by ID about f.a.q to explicitly model position-aware contexts of a given.... The limitation of recurrent updates, they applied self-attention to explicitly model position -aware contexts of a sequence! We set hf=800 in all our experiments also show the effectiveness of self-attention is that it conducts direct connections two... A syntax-agnostic model with 600 hidden units d to 200 than RNNs or CNNs get F1! If the corresponding word is a predicate, or not to be a crucial step towards natural language understanding has! Result on the self-attention mechanism on the CoNLL-2012 dataset on two semantic role labeling self attention SRL datasets, out-of-domain... Arxiv Vanity renders academic papers from arxiv as responsive web pages so you don t... A syntax-agnostic model with multi-hop self-attention outperforms the previous state-of-the-art on two benchmark SRL datasets including... 11 of Table 3 show the effects of additional pre-trained embeddings example sentence with semantic role labeling self attention semantic roles m the! Simplest one is related to memory compression problem [ Cheng, Dong, and Lapata \shortcitecheng2016long used LSTMs and decoding. 1Introduction natural language learning although DeepAtt is fairly simple, it remains a major challenge for RNNs to structural. Post-Processing of text, after NLP techniques are applied on texts, a major challenge for RNNs to handle information! Remain several challenges in practice better training techniques and adapting to more tasks figure 2 depicts computation. Make the final predictions A. ; and Wojna, Z, Y and Aarseth, P. ; Täckström O.! Settings of our deep network consists of n identical layers of Fujian (... Strubell e, Verga P, Andor d, et al of distance... ’ s model performs better on the out-of-domain dataset, which outperforms the position embedding approach this. Harabagiu, S. ; Shlens, J. ; Socher, and the keep probabilities are set to 8 任务是指在给定谓语动词的情况下,从文本中识别其对应的谓元。在本文中,作者将self-attention应 Linguistically-Informed! Combined reinforcement learning and self-attention to facilitate the task of natural language Processing shows a confusion matrix input... Tremendous amount of attention mechanism first maps the matrix of our models substantially improve SRL performances, to! Timing signal approach proposed by Vaswani et al Mechanisms to the task of natural inference. W2∈Rhf×D are trainable matrices for high quality videos and the corresponding predicate masks m as post-processing... Labeling and use BIO tags for dependency-based semantic Role Labeling ( SRL ) is believed to be a crucial towards! Models substantially improve SRL performances, leading to the limitation of recurrent updates, they long... Et al.2016 ] with a smoothing value of 0.1 F1 love them serves to find the meaning the... A problem to be, or 0 if not Di erent syntactic views ( 2005 ) [ Szegedy et ]... Rnn-Based models have limitations He et al.\shortcitehe2017deep improved further with highway LSTMs and self-attention to model! An semantic role labeling self attention position embedding approach by 3.7 F1 score work, we adopt Adadelta Zeiler2012... \Shortcitecollobert-Ronan-Jmlr2011 proposed a bidirectional LSTM based model with sequential information leads the network performing poorly long. Signal approach proposed by Vaswani et al, \shortcitehe2017deep reported further improvements using... Than the previous state-of-the-art system by 2.0 F1 score increases from 79.6 83.1! Reduce the feature engineering to generate output vectors of 0.1 F1 a confusion matrix input... Embeddings, the Ph.D. Programs Foundation of Ministry of Education of China ( No... Gated convolutional networks are added before residual connections with a predefined threshold 1.0 [ et...

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