\section{Introduction}
%ORL是一个重要的任务
Opinion and sentiment analysis has a wide range of real-world applications like social media monitoring \cite{bollen2011twitter}, stock market prediction \cite{nguyen2015sentiment}, box office prediction \cite{yu2010mining}, and general e-commerce applications \cite{kim2013h,HU2017opinion,cui-etal-2017-superagent}. In particular, fine-grained opinion analysis aims to identify users' opinions in a text, including opinion expressions, holders of the opinions, targets of the opinions,  target-dependent attitude, and intensity of opinions \cite{marasovic2017srl4orl}, which is very important for understanding political stance, customers' reviews, marketing trends, and other subjective information \cite{RAVI2015a}. As a typical fine-grained opinion mining task, opinion role labeling (ORL) aims to identify different roles relevant to each opinion, i.e., \textit{who expressed what kind of sentiment towards what} \cite{liu2012}.

%但是又很难，比如（举例）；数据也少，不到几k；作为参考，SRL任务数据量几万
Due to the lack of large-scale labeled data, ORL remains a challenging task to tackle. As a reference point, semantic role labeling (SRL) is very similar to ORL in the problem definition, but has 10 times more labeled data and thus achieves much higher performance than ORL (80$\sim$90 vs. 60$\sim$70 in F1 score). Motivated by  the correlations between the two tasks, SRL has been utilized to help the ORL task by many previous studies \cite{ruppenhofer2008finding,marasovic2017srl4orl,zhang2019enhancing}. However, when opinion expressions and arguments compose complicated syntactic structures, it is difficult to correctly recognize the opinion arguments even with shallow semantic representation like SRL \cite{marasovic2017srl4orl}.

\input{content/example.tex}

%Parsing作为一个基本的分析语言的能力，很有用，比如SRL等等；然而在ORL中应用句法的情况还很少
To compensate for the limited scale of labeled data for data-driven approaches, linguistic knowledge like syntax provides structural information representing human understanding of the text. Naturally, dependency relations between words ease the discovering of opinion roles. Taking the example in Figure \ref{graph:example}, the \emph{Target} span is often incompletely recognized without syntactic dependency relations, missing either ``facing Chavez'' or ``challenge''. For the similar SRL task, many previous works have proposed to incorporate syntax into the neural models \cite{marcheggiani2017encoding,he2018syntax,xia2019mtl}. In contrast, few studies in the recent years explore this line of research for ORL. 

%没有用句法的原因一是句法会错，二是错误积累。
%1. 用soft 2. 用MTL
%relations and labels
There are two barriers to apply syntactic dependency parsing to NLP tasks, i.e., 1) inaccuracy of the parsing results, and 2)  error propagation of the processing pipeline. To overcome the first barrier, instead of employing the final discrete outputs (i.e., single 1-best dependency trees), we make use of the probability matrix of all dependency arcs (also can be viewed as an edge-weighted directed graph) before searching for the 1-best tree. Such probabilistic representation of syntax provides more information while alleviating parsing errors. For the second barrier, considering that the pipeline methods are notorious for the error propagation problem, we introduce %joint models or 
multi-task learning (MTL) frameworks, which have been widely used in many NLP models when predictions at various processing levels are needed \cite{collobert2008unified,ruder2017overview}.

Apart from the syntactic information, contextualized word representations like BERT \cite{devlin2018bert} are widely used to compensate for the sparsity of task-specific training data. They compress distributional semantics of words from large corpora, making the local context fluent and natural. However, the long-distance dependencies between words are often ignored, which is ideally able to be captured by syntactic analysis. 
%which could be complementary to the linguistic analysis.
 
%However, whether and what to add into the MTL framework is not a trivial decision. Previous studies have demonstrated that tasks at different levels of processing contribute to each other, and the MTL model usually achieves xxx improvement. (cite?)

%综合以上几点，句法对SRL有用+SRL与ORL相似，我们本文探索句法对于ORL的作用。
In summary, 
based on previous studies in using syntax to improve various tasks, this work 
investigates whether syntax can enhance the neural ORL model. Particularly, we try to answer the following three questions.
\begin{itemize}
%\item Whether multi-task learning works better on combining different modules?
\item How to effectively integrate various syntactic information into the neural ORL model?
\item How to alleviate the propagation of errors brought by syntactic parsing?
\item Is syntactic knowledge already covered by the contextualized word representations like BERT? 
\end{itemize} 

Based on our experiments, we observe that 1) compared with single 1-best  parse trees, encoding the edge-weighted graphs
%encoding a dependency forest (distributions of multiple parse trees???) 
achieves better results, as the model is less sensitive to parsing errors while keeping richer structural information; 2) integrating various syntactic information, both explicit and implicit, boosts performance, and MTL framework can effectively alleviate the error propagation problem; and 3) contributions from  syntactic information, especially from long-distance dependency relations, do not fully overlap with those from the contextualized word representations like BERT. Our overall model delivers a new state-of-the-art result on the benchmark MPQA corpus, with 
4.34 %\%
%6.8\% 
absolute improvement over the previous best result.



