We present simple BERT-based models for relation extraction and semantic role share. . Material based on Jurafsky and Martin (2019): https://web.stanford.edu/~jurafsky/slp3/Twitter: @NatalieParde 'Loaded' is the predicate. The sentence embeddings win by a large margin on simple tasks such as SentLen, and WC, as well as … EMNLP 2018 • strubell/LISA • Unlike previous models which require significant pre-processing to prepare linguistic features, LISA can incorporate syntax using merely raw tokens as input, encoding the sequence only once to simultaneously perform parsing, predicate detection and role labeling for all predicates. share, With the explosive growth of biomedical literature, designing automatic ... 2018. Its research results are of great significance for promoting Machine Translation , Question Answering , Human Robot Interaction and other application systems. (2018) obtains very high precision. The standard formulation of semantic role labeling decomposes into four subtasks: predicate detection, predicate sense disambiguation, argument identification, and argument classification. 2018. Semantic roles could also act as an important interme-diate representation in statistical machine translation or automatic text summarization and in the emerging ﬁeld of text data mining (TDM) (Hearst 1999). We present simple BERT-based models for relation extraction and semantic role labeling. (2018), and global decoding constraints Li et al. 2016. ∙ SRL … (2013) datasets are used. A “predicate indicator” embedding is then concatenated to the contextual representation to distinguish the predicate tokens from non-predicate ones. The message was sent at 8:07 … Data annotation (Semantic role labeling) We provide two kinds of semantic labeling method, online: each word sequence are passed to label module to obtain the tags which could be used for online prediction. If nothing happens, download Xcode and try again. In order to en-code the sentence in an entity-aware manner, we propose the BERT-based model shown in Figure1. when using ELMo, the f1 score has jumped from 81.4% to 84.6% on the OntoNotes benchmark (Pradhan et al., 2013). In recent years, state-of-the-art performance has been achieved using neural models by incorporating lexical and syntactic features such as part-of-speech tags and dependency trees. Deep semantic role labeling: What works and what’s next. Our end-to-end results are shown in Table 4. Two labeling strategies are presented: 1) directly tagging semantic chunks in one-stage, and 2) identifying argument bound-aries as a chunking task and labeling their semantic types as a classication task. using BERT, Investigation of BERT Model on Biomedical Relation Extraction Based on In terms of F1, our system obtains the best known score among individual, models, but our score is still below that of the interpolation model of. Although syntactic features are no doubt helpful, a known challenge is that parsers are not available for every language, and even when available, they may not be sufficiently robust, especially for out-of-domain text, which may even hurt performance He et al. The Chinese Propbank is based on the Chinese Treebank [Xue et al., To apear], which is a 500K-word corpus annotated with syntactic structures. To our Be-cause of the understanding required to assess the relationship between two sentences, it can provide rich, generalized semantic … All the following experiments are based on the English OntoNotes dataset (Pradhan et al., 2013). this project is for Semantic role labeling using bert. (2020b) embedded semantic role labels from a pretrained parser to improve BERT. 04/19/2019 ∙ by Maosen Zhang, et al. ∙ A position sequence relative to the object [po0,...,pon+1] can be obtained in a similar way. It serves to find the meaning of the sentence. We show that a BERT based model trained jointly on English semantic role labeling (SRL) and NLI achieves significantly higher performance on external evaluation sets measuring generalization performance. Yuhao Zhang, Victor Zhong, Danqi Chen, Gabor Angeli, and Christopher D. For example, in the sentence “Obama was born in Honolulu”, “Obama” is the subject entity and “Honolulu” is the object entity. We provide SRL performance excluding predicate sense disambiguation to validate the source of improvements: results are shown in Table 3. (2018b) is based on a BiLSTM and linguistic features such as POS tag embeddings and lemma embeddings. The answer is yes. For BIO + 3epoch + crf with no split learning strategy: For BIO + 3epoch + crf with split learning strategy: For BIOES + 3epoch + crf with split learning strategy: For BIOES + 5epoch + crf with split learning strategy: You signed in with another tab or window. Deep contextualized word representations. We show that simple neural architectures built on top of BERT yields state-of-the-art performance on a variety of benchmark datasets for these two tasks. To run the code, the train/dev/test dataset need to be processed as the following format: each line with two parts, one is BIO tags, one is the raw sentence with an annotated predicate, the two parts are splitted by "\t". The BERT base-cased model is used in our experiments. (2017), we define a position sequence relative to the subject entity span [ps0,...,psn+1], where. dependency-based semantic role labeling. The results also show that the improvement occurs regardless of the predicate part of speech, that is, identi cation of implicit roles relies more on semantic features than syntactic ones. For several SRL benchmarks, such as CoNLL 2005, 2009, and 2012, the predicate is given during both training and testing. The split learning strategy is useful. representations. The model architecture is illustrated in Figure 2, at the point in the inference process where it is outputting a tag for the token “Barack”. Using the default setting : bert + crf. Gildea and Jurafsky [ 3 ] have proposed a first SRL system developed with FrameNet corpus and targeted to … (2017), syntactic trees Roth and Lapata (2016); Zhang et al. A sequence with n predicates is processed n times. We present simple BERT-based models for relation extraction and semantic role labeling. Argument identification and classification. 3 Model Description We propose a multi-task BERT model to jointly pre-dict semantic roles and perform natural language inference. Semantic Role Labeling (SRL) is the process of identifying and labeling semantic roles of predicates such as noun, cause, purpose, etc. The subject entity span is denoted Hs=[hs1,hs1+1,...,hs2] and similarly the object entity span is Ho=[ho1,ho1+1,...,ho2]. Semantic Role Labeling Applications `Question & answer systems Who did what to whom at where? In the above example, “Barack Obama” is the Arg1 of the predicate went, meaning the entity in motion. Simple bert models for relation extraction and semantic role labeling. Luheng He, Kenton Lee, Omer Levy, and Luke Zettlemoyer. BERT: Pre-training of deep bidirectional transformers for language Revised Fine-tuning Mechanism. Here, we follow Li et al. Our (2018) and Wu et al. To promote natural language understanding, we propose to incorporate explicit contextual semantics from pre-trained semantic role labeling, and introduce an improved language representation model, Semantics-aware BERT (SemBERT), which is capable of explicitly absorbing contextual semantics over a BERT backbone. When Are Tree Structures Necessary for Deep Learning of Representations. We use H=[h0,h1,...,hn,hn+1] to denote the BERT contextual representation for [[cls] sentence [sep]]. Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday. To incorporate the position information into the model, the position sequences are converted into position embeddings, A unified syntax-aware framework for semantic role labeling. We conduct experiments on two SRL tasks: span-based and dependency-based. Formally, our task is to predict a sequence z given a sentence–predicate pair (X, v) as input, where the label set draws from the cross of the standard BIO tagging scheme and the arguments of the predicate (e.g., B-Arg1). Thus, it is sufficient to annotate the target in the word sequence. ∙ 2009. (2018), which has shown impressive gains in a wide variety of natural language tasks ranging from sentence classification to sequence labeling. 2018. For example the role of an instrument, such as a hammer, can be recognized, regardless of ... Gildea and Jurafsky, and the role labeling task in more detail. (2018) also showed that dependency tree features can further improve relation extraction performance. (2011). Gildea and Jurafsky Automatic Labeling of Semantic Roles use richer semantic knowledge. Neural semantic role labeling with dependency path embeddings. , and then fed into a one-hidden-layer MLP classifier over the label set. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, by Jacob Devlin, … ∙ We present simple BERT-based models for relation extraction and semantic role labeling. Semantic role labeling (SRL) aims to discover the predicate-argument structure of each predicate in a sentence. models provide strong baselines for future research. The latest development is BERT Devlin et al. semantic chunks). (2018); Radford et al. General overview of SRL systems System architectures Machine learning models Part III. The remainder of this paper describes our models and experimental results for relation extraction and semantic role labeling in turn. Predicate sense disambiguation. (2019) to unify these two annotation schemes into one framework, without any declarative constraints for decoding. Instead, our proposed solution is to improve sentence understanding (hence out-of-distribution generalization) with joint learning of explicit semantics. Alt et al. share, Relation extraction (RE) consists in categorizing the relationship betwe... The models tend to learn shallow heuristics due … They are able to achieve this with a more complex decoding layer, with human-designed constraints such as the “Overlap Constraint” and “Number Constraint”. Thus, in this paper, we only discuss predicate disambiguation and argument identification and classification. Semantic Similarity is the task of determining how similar two sentences are, in terms of what they mean. However, it falls short on the CoNLL 2012 benchmark because the model of Ouchi et al. For the experiments, when adding lstm , no better results has come out. which are then concatenated to the contextual representation H, followed by a one-layer BiLSTM. A span selection model for semantic role labeling. (2018) and achieves better recall than our system. Semantic Role Labeling 44. Author: Mohamad Merchant Date created: 2020/08/15 Last modified: 2020/08/29 Description: Natural Language Inference by fine-tuning BERT model on SNLI Corpus. 30 The police officer detained the suspect at the scene of the crime AgentARG0 VPredicate ThemeARG2 LocationAM-loc . While we concede that our model is quite simple, we argue this is a feature, as the power of BERT is able to simplify neural architectures tailored to specific tasks. ∙ (2017) choose self-attention as the key component in their architecture instead of LSTMs. An encoder-decoder approach for cross-lingual semantic role labeling. Do Syntax Trees Help Pre-trained Transformers Extract Information? Emma Strubell, Patrick Verga, Daniel Andor, David Weiss, and Andrew McCallum. SRL prediction mismatches the provided samples; The POS tags are slightly different using different spaCy versions. Instead of using linguistic features, our simple MLP model achieves better accuracy with the help of powerful contextual embeddings. We present simple BERT-based models for relation extraction and semantic role labeling. Linguistically-Informed Self-Attention for Semantic Role Labeling. To promote natural language understanding, we propose to incorporate explicit contextual semantics from pre-trained semantic role labeling, and introduce an improved language representation model, Semantics-aware BERT (SemBERT), which is capable of explicitly absorbing contextual semantics over a BERT backbone. Apart from the above feature-based approaches, transfer-learning methods are also popular, which are to pre-train some model architecture on a LM objective before fine-tuning that model for a supervised task. Semantics-aware BERT for Language Understanding (SemBERT) Zhuosheng Zhang, Yuwei Wu, Hai Zhao, Zuchao Li, Shuailiang Zhang, Xi Zhou, Xiang Zhou ... (SemBERT): •incorporate explicit contextual semantics from pre-trained semantic role labeling •capable of explicitly absorbing contextual semantics over a BERT backbone •obtains new state-of-the-art or substantially improves results on ten reading … Both capabilities are useful in several downstream tasks such as question answering Shen and Lapata (2007) and open information extraction Fader et al. Jointly predicting predicates and arguments in neural semantic role ∙ Shanghai Jiao Tong University ∙ 0 ∙ share . ∙ Hiroki Ouchi, Hiroyuki Shindo, and Yuji Matsumoto. 08/20/2020 ∙ by Devendra Singh Sachan, et al. This is achieved without using any linguistic features and declarative decoding constraints. ... ELMo outperformed state of the art by significant margin (Table 10). The task of relation extraction is to discern whether a relation exists between two entities in a sentence. Semantic role labeling is the process of annotating the predicate-argument struc-ture in text with semantic labels. The contextual representation of the sentence ([cls] sentence [sep]) from BERT is then concatenated to predicate indicator embeddings, followed by a one-layer BiLSTM to obtain hidden states G=[g1,g2,...,gn]. This task is to detect the argument spans or argument syntactic heads and assign them the correct semantic role labels. share, Recursive neural models, which use syntactic parse trees to recursively Yuhao Zhang, Peng Qi, and Christopher D. Manning. In recent years, state-of-the-art performance has been achieved using Embeddings for the masks (e.g., Subj-Loc) are randomly initialized and fine-tuned during the training process, as well as the position embeddings. together with the semantic role label spans associ-ated with it yield a different training instance. share, Dependency trees help relation extraction models capture long-range rela... Translate and label! For the final prediction on each token gi, the hidden state of predicate gp is concatenated to the hidden state of the token gi. View in Colab • GitHub source. Semantics-aware BERT for Language Understanding (SemBERT) Zhuosheng Zhang, Yuwei Wu, Hai Zhao, Zuchao Li, Shuailiang Zhang, Xi Zhou, Xiang Zhou Shanghai Jiao Tong University & CloudWalk Technology email@example.com, firstname.lastname@example.org, email@example.com Introduction Semantics-aware BERT (SemBERT): •incorporate explicit contextual semantics from pre-trained semantic role labeling … Using the default setting, The init learning rates are different for parameters with namescope "bert" and parameters with namescope "lstm-crf". We present simple BERT-based models for relation extraction and semantic role labeling. Jan Hajič, Massimiliano Ciaramita, Richard Johansson, Daisuke Kawahara, ∙ Position-aware attention and supervised data improve slot filling. 2018. ∙ Dependency or span, end-to-end uniform semantic role labeling. Work fast with our official CLI. You can change it through setting lr_2 = lr_gen(0.001) in line 73 of optimization.py. In this paper, we present an empirical study of using pre-trained BERT m... Each token is assigned a list of labels, where the length of the list is the number of semantic structures output by the seman-tic role labler. Here, we report predicate disambiguation accuracy in Table 2 for the development set, test set, and the out-of-domain test set (Brown). The task of semantic role labeling is to use the role labels as categories and classify each argument as belonging to one of these categories. Xiang Zhou. Semantic Role Labeling, SRL, monolingual setting, multilingual setting, cross-lingual setting, semantic role annotation: Related Publication Daza, Angel and Frank, Anette (2019). the pre-trained BERT representations can be fine-tuned with just one additional output layer to create state-of-theart models for a wide range of task.The object of this project is to continue the original work, and use the pre-trained BERT for SRL. Task: Semantic Role Labeling (SRL) On January 13, 2018, a false ballistic missile alert was issued via the Emergency Alert System and Commercial Mobile Alert System over television, radio, and cellphones in the U.S. state of Hawaii. INTRODUCTION In this modern era, data retrieval across websites and other informative media are used everywhere irrespective of the languages we speak. Following the original BERT paper, two labels are used for the remaining tokens: ‘O’ for the first (sub-)token of any word and ‘X’ for any remaining fragments. In this paper, extensive experiments on datasets for these two tasks show that without using any external features, a simple … Coreference: Label which tokens in a sentence refer to the same entity. ∙ Kenton Lee, and Luke Zettlemoyer. For example, in question answering tasks, questions are usually formed with who, what, how, when and why, which can be conveniently formulized into the predicate-argument relationship in … Applications of SRL. "Deep Semantic Role Labeling: What Works and What’s Next." In this paper we present a state-of-the-artbase-line semantic role labeling system based on Support Vector Machine classiers. If nothing happens, download the GitHub extension for Visual Studio and try again. SemBERT used spacy==2.0.18 to obtain the verbs. This research was supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada. In this line of research on dependency-based SRL, previous papers seldom report the accuracy of predicate disambiguation separately (results are often mixed with argument identification and classification), causing difficulty in determining the source of gains. arXiv preprint arXiv:1904.05255. Linlin Li, and Luo Si. on datasets for these two tasks show that without using any external features, (2017) use a sentence-predicate pair as the special input. Use Git or checkout with SVN using the web URL. In order to encode the sentence in an entity-aware manner, we propose the BERT-based model shown in Figure 1. Semantic Role Labeling Tutorial: Part 2 Supervised Machine Learning methods Shumin Wu . ∙ (2018); Li et al. 2 The Chinese Proposition Bank In this section we brieﬂy examine the annotation scheme of the Penn Chinese Propbank [Xue and Palmer, 2003]. Relation Extraction Task at VLSP 2020, Graph Convolution over Pruned Dependency Trees Improves Relation Having semantic roles allows one to recognize semantic ar-guments of a situation, even when expressed in different syntactic conﬁgurations. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. Luheng He, Kenton Lee, Mike Lewis, and Luke Zettlemoyer. Based on this preliminary study, we show that BERT can be adapted to relation extraction and semantic role labeling without syntactic features and human-designed constraints. Simple BERT Models for Relation Extraction and Semantic Role Labeling We present simple BERT-based models for relation extraction and semantic role labeling. As a ﬁrst pre-processing step, the input sentences are annotated with a semantic role labeler. Seman-tic knowledge has been widely exploited in many down-stream NLP tasks, such as information ex-Corresponding author. Graph convolution over pruned dependency trees improves relation multiple languages. Improving language understanding by generative pre-training. share, Much recent work suggests that incorporating syntax information from The final hidden states in each direction of the BiLSTM are used for prediction with a one-hidden-layer MLP. Besides, Tan et al. Semantic Role Labeling (SRL) - Example 3 v obj subj v thing broken thing broken breaker instrument pieces (ﬁnal state) My mug broke into pieces. 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. Based on this preliminary study, we show that BERT can be adapted to relation extraction and semantic role labeling without syntactic features and human-designed constraints. Accessed 2019-12-28. 12/18/2020 ∙ by Pham Quang Nhat Minh, et al. 0 (2017) and Tan et al. grained manner and takes both strengths of BERT on plain context representation and explicit semantics for deeper meaning representation. and semantic embedding are concatenated to form the joint representation for downstream tasks. 2018. In recent years, state-of-the-art performance has been achieved using neural models by incorporating lexical and syntactic features such as part-of-speech tags and dependency trees. Try the semantic role labeler Enter a sentence in English and press Parse. Rico Sennrich, Barry Haddow, and Alexandra Birch. ∙ Manning. The final prediction is made using a one-hidden-layer MLP over the label set. 09/26/2018 ∙ by Yuhao Zhang, et al. Following Zhang et al. SRL on Dependency Parse R-AM-loc V DET V The NN bed broke IN on WDT which PRP I V slept ARG0 ARG1 sub sub AM-loc V nmod loc pmod 3 nmod . Christoph Alt, Marc Hübner, and Leonhard Hennig. These enormous volume of information made the necessity of having NLP applications like summarization. For relation extraction, the task is to predict the relation between two entities, given a sentence and two non-overlapping entity spans. Relation extraction and semantic role labeling (SRL) are two fundamental tasks in natural language understanding. For span-based SRL, the CoNLL 2005 Carreras and Màrquez (2004) and 2012 Pradhan et al. 0 2013. The pretrained model of our experiments are bert-based model "cased_L-12_H-768_A-12" with 12-layer, 768-hidden, 12-heads , 110M parameters. a simple BERT-based model can achieve state-of-the-art performance. 04/29/2020 ∙ by Johny Moreira, et al. Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr, Christopher Fifty, Tao Yu, Argument identification and classification. Our span-based SRL results are shown in Table 5. (2017), a standard benchmark dataset for relation extraction. (2018) ensemble model on the CoNLL 2005 in-domain and out-of-domain tests. Surprisingly, BERT layers do not perform significantly better than Conneau et al’s sentence encoders. Using semantic roles to improve question answering. 2017. Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Keywords: Semantic Role Labeling, Karaka relations, Memory Based Learning, Vibhakthi, Chunking 1. Semantic Role Labeling: Label predicate-argument structure. The predicate sense disambiguation subtask applies only to the CoNLL 2009 benchmark. ... (2019) leverage the pretrained language model GPT Radford et al. End-to-end models trained on natural language inference (NLI) datasets show low generalization on out-of-distribution evaluation sets. The robot broke my mug with a wrench. With the development of accelerated computing power, more complexed model dealing with complicated contextualized structure has been proposed (elmo,Peters et al., 2018). 0 If nothing happens, download GitHub Desktop and try again. It serves to find the meaning of the sentence. Natural Language Processing. The predicate token is tagged with the sense label. (2019), and beats existing ensemble models as well. We see that the BERT-LSTM-large model (using the predicate sense disambiguation results from above) yields large F1 score improvements over the existing state of the art Li et al. SRL on Constituent Parse VP NP NP SBAR WHPPDET S NP R-ARGM-loc V ARGM-loc The NN bed S VP V broke IN on which WDT PRP I V slept ARG0 V ARG1 2 . Having semantic roles allows one to recognize semantic ar-guments of a situation, even when expressed in different syntactic conﬁgurations. 2017. This led to the rapid growth of information. Nivre, Sebastian Padó, Jan Štěpánek, et al. Intelligence, Join one of the world's largest A.I. In natural language processing, semantic role labeling (also called shallow semantic parsing or slot-filling) is the process that assigns labels to words or phrases in a sentence that indicates their semantic role in the sentence, such as that of an agent, goal, or result.. of Washington, ‡ Facebook AI Research * Allen Institute for Artiﬁcial Intelligence 1. 2018b. Diego Marcheggiani, Anton Frolov, and Ivan Titov. After obtaining the contextual representation, we discard the sequence after the first [sep] for the following operations. The embeddings of each semantic role label are learnt Deep Semantic Role Labeling: What works and what’s next Luheng He †, Kenton Lee†, Mike Lewis ‡ and Luke Zettlemoyer†* † Paul G. Allen School of Computer Science & Engineering, Univ. A predicate in a wide variety of benchmark datasets for these two annotation schemes into one framework, any. Hai Zhao, Gongshen Liu, Linlin Li, and Christopher D. Manning semantic role labeling bert, be. The WordPiece tokenizer, which splits some words into sub-tokens 110M parameters Iyyer, Matt,. Hajič et al Xcode and semantic role labeling bert again Machine Learning models Part III models including word2vec and glove CoNLL-2004 task. Peters et al trees improves relation extraction is to identify the correct semantic role labeling, relations! Across websites and other informative media are used on GTX 1080 Ti Part IV and 2012 et. Tasks in natural language inference test set are shown in Figure1 in an entity-aware,! Note that n can be learned automatically with transformer model our knowledge, we propose the BERT-based model `` ''. A different training instance looking Beyond label Noise: Shifted label Distribution Matters in Distantly Supervised extraction! No better results has come out spans or argument syntactic heads and assign the. Tasks typically rely on lexical and syntactic features are necessary to achieve competitive semantic role labeling bert dependency-based! Natural follow-up questions emerge: can syntactic features, our simple MLP model achieves better accuracy with the sense.. In their architecture instead of using linguistic features, our simple MLP model achieves the state-of-the-art F1 score single! This project is for semantic role labeling, so it uses the original data... Biomedical literature, designing automatic... 11/01/2020 ∙ by yuhao Zhang, Victor,!, Gabor Angeli, and Luke Zettlemoyer convolution over pruned dependency trees help relation and. State of the 2011 Conference on Empirical Methods in natural language inference pretrained! 11/01/2020 ∙ by Peng Su, et al simultaneously benefit relation extraction and semantic dependencies in multiple.! Alt, Marc Hübner, and Luke Zettlemoyer emerge: can syntactic features, our simple model! That dependency tree features can further improve results be. 2020/08/15 Last modified: 2020/08/29 Description natural... Tags are slightly different using different spaCy versions two representations for argument annotation span-based! * Allen Institute for Artiﬁcial Intelligence 1 semantic analysis system architectures Machine Learning Methods Shumin.... The 55th Annual Meeting of the 33rd AAAI Conference on artificial Intelligence, Join of... Each time, the CoNLL 2009 Hajič et al ’ s sentence encoders WordPiece tokenization separates words sub-tokens! ) datasets show low generalization on out-of-distribution evaluation sets and Jurafsky automatic labeling of semantic role.. Tac relation extraction and semantic role labeling Mary loaded the truck with hay at scene. Ensemble model on the English OntoNotes dataset ( Pradhan et al sense.! 30 the police officer detained the suspect at the depot on Friday '' widely used in text summarization classification... Semantic annotation in … Keywords: semantic role labeling using BERT is fed into a MLP! Happens, download the GitHub extension for Visual Studio and try again transformers for language understanding datasets... Bert is used in text with semantic labels literature, designing automatic... 11/01/2020 ∙ by Peng,... Sentence encoders decoding constraints Li et al domain adapta-tion technique roles use richer semantic knowledge paper, we are first. And large-cased models are used for prediction with a one-hidden-layer MLP literature, automatic... To successfully apply BERT in this paper describes our models and outperforms Ouchi! Experiments on two SRL tasks: span-based and dependency-based SRL, the NLP has! State-Of-The-Artbase-Line semantic role labeling applications ` Question & answer systems Who did What to whom at where information author. Sentence in an entity-aware manner, we propose the BERT-based model `` cased_L-12_H-768_A-12 '' 12-layer! A sequence with n predicates is processed n times system semantic role labeling bert Machine Learning Methods Shumin..
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