Synchronous Double-channel Recurrent Network for Aspect-Opinion Pair Extraction (2020)

Abstract: Opinion entity extraction is a fundamental task in fine-grained opinion mining. Related studies generally extract aspects and/or opinion expressions without recognizing the relations between them. However, the relations are crucial for downstream tasks, including sentiment classification, opinion summarization, etc. In this paper, we explore Aspect-Opinion Pair Extraction (AOPE) task, which aims at extracting aspects and opinion expressions in pairs. To deal with this task, we propose Synchronous Double-channel Recurrent Network (SDRN) mainly consisting of an opinion entity extraction unit, a relation detection unit, and a synchronization unit. The opinion entity extraction unit and the relation detection unit are developed as two channels to extract opinion entities and relations simultaneously. Furthermore, within the synchronization unit, we design Entity Synchronization Mechanism (ESM) and Relation Synchronization Mechanism (RSM) to enhance the mutual benefit on the above two channels. To verify the performance of SDRN, we manually build three datasets based on SemEval 2014 and 2015 benchmarks. Extensive experiments demonstrate that SDRN achieves state-of-the-art performances.

## introduction opinion entity extraction, which aims at identifying aspects and/or opinion expressions in review sentences, is an important task in fine-grained opinion mining. recently, there have been considerable studies focused on this task. specifically, @xcite , @xcite and @xcite explored aspect term extraction, and @xcite extracted opinion phrases with given aspects. meanwhile, many studies dealt with aspect and opinion term co-extraction @xcite @xcite @xcite . these studies have shown the importance of opinion entity extraction and achieved great progress. however, they neglect to recognize the relations between aspects and opinion expressions. while aspect-opinion relation detection is one of the key parts of an opinion mining system @xcite @xcite , it is neglected or assumed given beforehand, which leaves a significant gap to subsequent opinion mining tasks. for instance, as shown in figure 1 , we can obtain the aspect {food} and the opinion expressions {nice-looking, delicious} from opinion entity extraction. although both nicelooking and delicious express positive sentiment, they further describe food from the appearance and taste perspectives, respectively. therefore, only with the relations between aspects and opinion expressions, e.g., the pair food, delicious , can the more fine-grained subsequent tasks be executed, such as pair-level sentiment classification, pairlevel opinion clustering, etc. to bridge the gap between opinion entity extraction and subsequent tasks, we explore aspect-opinion pair extraction (aope) task, which aims at extracting aspects and opinion expressions along with their relations. specially, aope is not only necessary for subsequent tasks, but also beneficial to both opinion entity extraction and relation detection. however, the studies on aope are very limited. early works @xcite approach aspect-opinion pair extraction in a pipeline manner by dividing it into two isolated tasks. @xcite , @xcite and @xcite attempted to extract opinion entities and relations jointly without considering the interaction between opinion entity extraction and relation detection, which limits the performance. therefore, aope remains a rather challenging task. first, the relational structure of aspects and opinion expressions within a sentence can be complicated, requiring the model to be effective and flexible in detecting relations. for example, the relations can be one-to-many, many-to-one, and even embedded or overlapped. second, opinion entity extraction and relation detection are not two independent tasks as in other multitask learning problems but rely on each other, hence posing a key challenge on how to fuse and learn the two subtasks properly. third, how to synchronize opinion entity extraction with relation detection and make them mutually promotion is another primary challenge. to address the aforementioned challenges, we propose synchronous double-channel recurrent network (sdrn). specifically, we first utilize bidirectional encoder representations from transformers (bert) @xcite to learn context representations. then, the double-channel recurrent network, which consists of an opinion entity extraction unit and a relation detection unit, is constructed to extract aspects, opinion expressions, and relations simultaneously. to enable the information interaction between the above two channels, we design a synchronization unit which contains entity synchronization mechanism (esm) and relation synchronization mechanism (rsm). extensive experiments verify that our model achieves state-ofthe-art performances. in summary, our contributions are three-fold: aspect-opinion pair extraction is a critical task in fine-grained opinion mining. early studies approach this task in a pipeline manner. @xcite used association mining to identify aspects and extract the adjacent adjectives as opinions. @xcite extracted aspects and opinion expressions first, and then mined the relations with dependency relation templates. @xcite proposed an unsupervised model to extract aspects and corresponding opinions from reviews with pre-defined rules. although the above methods achieved great progress, they generally suffered from error propagation. to avoid error propagation, recent studies propose joint learning methods. klinger and cimiano (2013a) adopted imperatively defined factor graph (idf) to analyze the inter-dependencies between aspects and opinion expressions. @xcite presented a joint inference model based on idf to extract aspect terms, opinion terms, and their relations. @xcite employed integer linear programming (ilp) to identify opinion-related entities and their associated relations jointly. however, these works generally based on shallow machine learning methods and depended on hand-crafted features. to automatically capture features, neural network methods have been applied to various finegrained opinion mining tasks. @xcite al. (2018) for aope, @xcite explored lstm-based models to jointly extract opinion entities and their relations with three optimization methods. but this method neglects to learn the interaction between opinion entity extraction and relation detection. therefore, aope is still under-investigated and needs more researches. in this paper, we further explore this task and propose a neural model sdrn. ## model given a review sentence s, aspect-opinion pair extraction (aope) task aims to obtain a collection of aspect-opinion pairs @xmath0, o m ] m @xmath1, where a m and o m represent the aspect and the opinion expression, respectively foot_1 . to deal with aope task, we propose synchronous double-channel recurrent network (sdrn). the overall framework of sdrn is illustrated in figure 2 . specifically, we first adopt bert as the encoding layer to learn the context representations. then, an opinion entity extraction unit and a relation detection unit are constructed as double channels to extract aspects, opinion expressions, and relations simultaneously. furthermore, a synchronization unit is designed to enable information interaction between the double channels. to capture high-level representations, we recurrently execute the above units. after multiple recurrent steps, we adopt an inference layer to obtain aspect-opinion pairs. ## conclusion in this paper, we explored aspect-opinion pair extraction (aope) task and proposed synchronous double-channel recurrent network (sdrn). specifically, the opinion entity extraction unit and the relation detection unit are designed to extract aspects, opinion expressions and their relations simultaneously. the two units update themselves in a recurrent manner and form two channels, respectively. meanwhile, the synchronization unit is devised to integrate high-level interaction information and enable the mutual benefit on opinion entity extraction and relation detection. extensive experiments showed that our model achieves stateof-the-art performances.