Entity-Aware Dependency-Based Deep Graph Attention Network for Comparative Preference Classification (2020)

Abstract: This paper studies the task of comparative preference classification (CPC). Given two entities in a sentence, our goal is to classify whether the first (or the second) entity is preferred over the other or no comparison is expressed at all between the two entities. Existing works either do not learn entity-aware representations well and fail to deal with sentences involving multiple entity pairs or use sequential modeling approaches that are unable to capture long-range dependencies between the entities. Some also use traditional machine learning approaches that do not generalize well. This paper proposes a novel Entity-aware Dependency-based Deep Graph Attention Network (ED-GAT) that employs a multi-hop graph attention over a dependency graph sentence representation to leverage both the semantic information from word embeddings and the syntactic information from the dependency graph to solve the problem. Empirical evaluation shows that the proposed model achieves the state-of-the-art performance in comparative preference classification.

## introduction given a sentence that contains two entities of interest, the task of comparative preference classification is to decide whether there is a comparison between the two entities and if so, which entity is preferred @xcite @xcite . for example, considering sentence s 1 (shown in golf is easier to pick up than baseball. s 2 i'm considering learning python and more php if any of those would be better. the tools based on perl and python is much slower under windows than k9. although closely related, comparative preference classification (cpc) is different from comparative sentence identification (csi), which is a 2-class classification problem that classifies a sentence as a comparative or a non-comparative sentence. in previous work, @xcite did csi without considering which two entities are involved in a comparison. @xcite employed some dependency graph features to approach the csi task given two entities of interest. in this entity-aware case, syntactic features are crucial. however, not using word embeddings in the model makes the model harder to generalize with a good performance given various ways of expressing comparisons. @xcite gave the state-of-the-art result on the cpc task by using a pretrained sentence encoder to produce sentence embeddings as a feature for classification. however, this model is not entity-aware and does not use the dependency graph information. ## proposed model in this section, we first give a brief introduction to the gat model. we then present the proposed ed-gat model and discuss how to apply it to the cpc task. ## conclusion this paper proposes a novel model called ed-gat for comparative preference classification. it naturally leverages dependency graph features and word embeddings to capture the comparison and to classify the preference direction between two given entities. experimental results show that it outperforms all strong baselines and even bert pretrained using a huge corpus. our future work aims to improve the cpc performance further. apart from that, we also plan to design novel models to perform the related tasks of entity extraction and aspect extraction from comparative sentences. performing all these tasks jointly in a multitask learning framework is a promising direction as well because it can exploit the shared features and the inherent relationships of these tasks to perform all tasks better.