\section{Related work}
%(list some related works)
%In the following, the paper will be organized as the following: Section 2 gives an overview of fields of ORL, syntactic parsing application, and multi-task learning; Section 3 presents our model, which consists of a BiLSTM encoder, GCN encoder, ...; Section 4 describes the experiments we have performed, followed by results and analyses; and Lastly, Section 5 concludes the paper and points out the future direction to work on.

% \section{Related work}

% % ORL任务
% % traditional methods
% % SRL for ORL; MTL
% %前人有人用SRL信息的，但是SRL与ORL更像是类似并列或者父子集关系的任务。
% Due to the strong correlation between ORL and semantic
% role labeling (SRL), previous works mainly focus on exploit SRL\cite{zhang2019enhancing} extract the nerual features from the encoder of  a pre-trained SRL model, ,  to assist ORL; xxx use MTL to do SRL and ORL at the same time. Although being similar to each other, SRL and ORL are at the same level of abstraction in terms of NLU, i.e., at the semantic level. It is more natural for the syntax to do the favour. Some concerns about using syntax for ORL are the indirectness 

% % parser applications
% % parsing for SRL
% % parsing for relation extraction  

% % MTL


% ORL任务
%\textbf{FGOA, ORL}
%Fine-grained opinion analysis (FGOA) aims to identify user opinion in a text, including opinion expressions, the intensity of opinion, holders of the opinion, targets of the opinion and target-dependent attitude \cite{marasovic2017srl4orl}.
An opinion consists of several components, e.g., expressions, holders, and targets.
Some previous works focus on recognizing some components, whereas others try to recognize all components at the same time. 
%According to the commonly accepted benchmark corpus MPQA \cite{wiebe2005annotating} and previous works \cite{breck2007identifying,yang-cardie-2013-joint,katiyar2016investigating,marasovic2017srl4orl,zhang2019enhancing}, opinion entities is composed of expressions, holders and targets.
\citet{yang2014joint} and \citet{breck2007identifying} work entirely on labeling of the opinion expressions. \citet{kim2006extracting} and \citet{johansson2013relational} apply pipeline models to firstly predicting opinion expressions and then labeling holders and targets for each expression. Joint models simultaneously identify all opinion components, predicting which role is related to which opinion \cite{choi2006joint,yang-cardie-2013-joint,katiyar2016investigating}. In this work, we follow the opinion role labeling (ORL) task setting of \citet{marasovic2017srl4orl} and \citet{zhang2019enhancing}, and try to predict holders and targets for the given opinion expressions.

%\textbf{SRL for ORL}
%Previous works indicate that neural approaches do not outperform the feature-based CRF models for ORL \cite{yang-cardie-2013-joint}.
%Considering the limited labeled data for ORL, 
Previous works make use of SRL resources to address the issue of data scarcity for ORL, considering SRL is highly related to ORL and has a considerable amount of training data. Inspired by the similarity between ORL and SRL in task definition, \citet{kim2006extracting} and \citet{ruppenhofer2008finding} address ORL with a well-trained SRL model by treating opinion expressions as semantic predicates, and opinion roles as semantic roles. 
\citet{marasovic2017srl4orl} take SRL as an auxiliary task, and employ different MTL frameworks to learn the common grounds between ORL and SRL and distinguish task-specific knowledge. \citet{zhang2019enhancing} extract neural features from a well-trained SRL model as SRL-aware word representations, and then feed them into the input layer of ORL, aiming to alleviate the error propagation problem.
% Besides as a feature-based method,  ORL model can be build better with the guide of SRL neural features, and can better learn  the consistencies and inconsistencies between SRL and ORL. 

% ORL is a kind of Fine-Grained Opinion Analysis task and aims to detect important opinion arguments, expression, holders and targets. \cite{breck2007identifying,irsoy2013bidirectional,marasovic2017srl4orl,zhang2019enhancing}
% Some work focuses entirely on labeling of opinion expressions. \cite{} 

% Previous works investigate the feature-based approaches and neural approaches on the commonly accepted benchmark corpus MPQA \cite{wiebe2005annotating}, which show that the neural approaches do not outperform the feature-based CRF model.   

% \cite{marasovic2017srl4orl} and \cite{zhang2019enhancing} think  nerual approaches do not work because of the scarcity of ORL labeled training data. Meanwhile,  Semantic Role Labeling (SRL) task answers the question \textit{Who did what to whom, where and when}, which is strongly  correlative with ORL and oppose a considerable amount of training data. So previous works mainly focus on exploiting SRL to address the problem of lacking  labeled training data.
% traditional methods

% For using SRL to improve ORL performance, \cite{kim2006extracting} and \cite{ruppenhofer2008finding} take the opinion expression as the predicate to obtain semantic roles by a well-trained SRL model, and then map semantic roles into opinion roles. But this heuristic approach do not address the issue of  the inconsistency between the ORL and SRL.\cite{marasovic2017srl4orl}  use different multi-task learning (MTL) frameworks  with the auxiliary task SRL, which will better learn the common grounds by the shared parameters, and better distinguish the task-specific knowledge by the separate parameters. 

% SRL for ORL; MTL
%前人有人用SRL信息的，但是SRL与ORL更像是类似并列或者父子集关系的任务。

% \cite{zhang2019enhancing} extract neural features from the encoder of a well-trained SRL model as extra word representations, and then feed them into the  input layer of ORL, called SRL-SAWR approach, which can alleviate error propagation. Besides as a feature-based method,  ORL model can be build better with the guide of SRL neural features, and can better learn  the consistencies and inconsistencies between SRL and ORL. 

% xx view SRL as an  auxiliary task,
% and propose different multi-task learning  techniques to use the information of SRL. As reported, SRL is highly effective for ORL.

%\textbf{pull in parser}
%Although SRL is highly similar in nature to ORL, it cannot solve ORL for all cases \cite{ruppenhofer2008finding,marasovic2017srl4orl}. When opinion expression exists in complicated syntactic structures with opinion arguments, shallow semantic representation like SRL, is hard to recognize correct opinion arguments \cite{marasovic2017srl4orl}. %Besides, SRL and ORL are more like tasks of parent-child relationship.

Many previous works have shown that syntactic information is of great value for SRL and other NLP tasks \cite{he2018syntax,zhangyue2019syntax,strubell2018linguistically,xia2019mtl,miwa2016end,zhang2019syntaxMT}. \citet{xia2019syntax} use the relative position between predicate words and other words in a dependency tree to represent syntactic information, while \citet{roth2016neural} employ LSTM to obtain the embedding of a dependency path. \citet{tai2015improved} and \citet{kipf2016semi} propose TreeLSTM and graph convolution network (GCN) to encode the tree/graph-structural data respectively. 
Both TreeLSTM and GCN are commonly used techniques to encode parse trees \cite{miwa2016end,marcheggiani2017encoding,bastings2017graph}. \citet{zhang2019syntaxMT} and \citet{xia2019mtl} extract the hidden states from the LSTM encoder of the parser model as syntax-aware word representations, and feed them to downstream tasks as extra inputs.

In contrast, few works have proved that syntactic knowledge is useful in the neural ORL models. \citet{yang-cardie-2013-joint} integrate the shortest path features from dependency trees into a traditional CRF-based ORL model. To our best knowledge, this work is the first to investigate how to incorporate syntax into neural ORL models.

%Instead of the parse tree, researchers investigate to exploit the other information contained in parser models for addressing the issue of errors propagation and low parsing accuracy of out-domain. Some works simply view the dependency parsing as a auxiliary task under the framework of MTL, which learns the syntactic information by the shared parameters.

%\textbf{parser application}
%Limited to the technology of encoding dependency trees, the works of exploiting syntactic information for higher ORL performance are rare, except that \citet{yang-cardie-2013-joint} 
%As for other downstream tasks, there exist various of approaches to incorporate syntactic knowledge. % into downstream tasks with the development of dependency parsers and neural networks. %neural network models has met with different answers in the literature.

%In this work, we ????

% parser applications
% \textbf{parser application}
% \citet{yang-cardie-2013-joint}  employ syntactic patterns of dependency trees to improve the performance in traditional feature-based ORL model.
% with the development of dependency parsing and tree/graph-based encoder in recent years, syntax has been demonstrated highly effective in  a variety of NLP downstream tasks\cite{dozat2016deep, tai2015improved,Improved neural machine translation with a syntax-aware encoder and decoder, kipf2016semi}, such as SRL \cite{}, relation extraction \cite{}, machine translation \cite{Improved neural machine translation with a syntax-aware
% encoder and decoder}, and quesion answer \cite{Using semantic roles to improve question answering, Machine comprehension with syntax, frames, and semantics}.  %The question of how to effectively incorporate syntactic knowledge into neural network models has met with different answers in the literature. 

% With the recent advances in dependency parsing and the introduction of the Treelstm and GCN, more and more researcher investigate to apply dependency parsing to downstream tasks. 

% sub tree {head embedding, label embedding, end to end }
% whole tree
% latent possible tree
% feature-based implict tree
% multi task learning

% parsing for SRL

% For SRL task, previous researches investigate  whether extra syntactic knowledge are beneficial for neural SRL models.
% \cite{roth2016neural} model dependency path between the predicate word and each argument head word as sequences of words and dependencies,  and apply LSTM to obtain dependency path embedding.
% \cite{marcheggiani2017encoding} construct Graph Convolutional Networks to represent and encode the dependency structure.
% % He et al. (2018) emphasize the role of syntax in argument identification rather than role labeling. 
% \cite{he2018syntax} propose a k-th order argument pruning algorithm based on systematic dependency trees. 
% \cite{xia2019syntax} investigate several common approaches for encoding syntactic trees, such as TreeGRU, SDP, TPF, PE. 
% Besides above-mentioned researches based on LSTM encoder, \cite{zhangyue} incorporate syntactic information into self-attention encoder. 
% Strubell et al. (2018) incorporate syntactic information in a multi-task neural network model that simultaneously performs part-of-speech tagging, dependency parsing, predicate detection,and SRL. 

%  Specifically, they develop an argument pruning algorithm that operates over dependency structures and selects argument candidates subject to a parameter determining their distance from the predicate.The predicate and its arguments are then encoded with an LSTM similar to Marcheggiani and Titov (2017).

 
%  Previous NMT models integrate syntax by representing 1-best tree outputs from a well-trained parsing system, e.g., the representative Tree-RNN and Tree-Linearization methods, which may suffer from error propagation.
 
%  By explicitly expressing the structural connections between words and phrases, syntax trees been demonstrated helpful in SMT and NMT. 
% parsing for relation extraction

% For relation extraction
% \cite{miwa-bansal-2016-end} captures both word sequence and dependency tree substructure
% information by stacking bidirectional tree-structured LSTM on bidirectional
% sequential LSTM.
% \cite{guo2019attention} propose Attention guided GCN to complete soft-pruning strategies, which automatically select the useful sub-structures of dependency tree.

% % MTL
% Multi-task learning has been used successfully across all applications of machine learning, which can enable our model to generalize better on our original task By sharing representations (hard/soft parameters sharing) between related tasks.
% \cite{strubell2018linguistically} propose a self-attention based neural MTL model which incorporate dependency parsing as a auxiliary task for SRL.
% \cite{xia2019syntax} integrate syntactic knowledge that extract from word representations of parser model under the MTL framework.