Powering Comparative Classification with Sentiment Analysis via Domain Adaptive Knowledge Transfer (2021)

Abstract: We study Comparative Preference Classification (CPC) which aims at predicting whether a preference comparison exists between two entities in a given sentence and, if so, which entity is preferred over the other. High-quality CPC models can significantly benefit applications such as comparative question answering and review-based recommendation. Among the existing approaches, non-deep learning methods suffer from inferior performances. The state-of-the-art graph neural network-based ED-GAT (Ma et al., 2020) only considers syntactic information while ignoring the critical semantic relations and the sentiments to the compared entities. We propose Sentiment Analysis Enhanced COmparative Network (SAECON) which improves CPC accuracy with a sentiment analyzer that learns sentiments to individual entities via domain adaptive knowledge transfer. Experiments on the CompSent-19 (Panchenko et al., 2019) dataset present a significant improvement on the F1 scores over the best existing CPC approaches.

## introduction comparative preference classification (cpc) is a natural language processing (nlp) task that predicts whether a preference comparison exists between two entities in a sentence and, if so, which entity wins the game. for example, given the sentence: python is better suited for data analysis than matlab due to the many available deep learning libraries, a decisive comparison exists between python and matlab and comparatively python is preferred over matlab in the context. the cpc task can profoundly impact various real-world application scenarios. search engine users may query not only factual questions but also comparative ones to meet their specific information needs @xcite . recommendation * these authors contributed equally. several models have been proposed to solve this problem. @xcite first formalize the cpc problem, build and publish the compsent-19 dataset, and experiment with numerous general machine learning models such as support vector machine (svm), representation-based classification, and xgboost. however, these attempts consider cpc as a sentence classification while ignoring the semantics and the contexts of the entities @xcite . ed-gat @xcite ) marks the first entityaware cpc approach that captures long-distance syntactic relations between the entities of interest by applying graph attention networks (gat) to dependency parsing graphs. however, we argue that the disadvantages of such an approach are clear. firstly, ed-gat replaces the entity names with "entitya" and "entityb" for simplicity and hence deprives their semantics. secondly, ed-gat has a deep architecture with ten stacking gat layers to tackle the long-distance issue between compared entities. however, more gat layers result in a heavier computational workload and reduced training stability. thirdly, although the competing entities are typically connected via multiple hops of dependency relations, the unordered tokens along the connection path cannot capture either global or local high-quality semantic context features. in this work, we propose a sentiment analysis enhanced comparative classification network (saecon), a cpc approach that considers not only syntactic but also semantic features of the entities. the semantic features here refer to the context of the entities from which a sentiment analysis model can infer the sentiments toward the entities. specifically, the encoded sentence and entities are fed into a dual-channel context fea-ture extractor to learn the global and local context. in addition, an auxiliary aspect-based sentiment analysis (absa) module is integrated to learn the sentiments towards individual entities which are greatly beneficial to the comparison classification. absa aims to detect the specific emotional inclination toward an aspect within a sentence @xcite @xcite @xcite . for example, the sentence i liked the service and the staff but not the food suggests positive sentiments toward service and staff but a negative one toward food. these aspect entities, such as service, staff, and food, are studied individually. the well-studied absa approaches can be beneficial to cpc when the compared entities in a cpc sentence are considered as the aspects in absa. incorporating the individual sentiments learned by absa methods into cpc has several advantages. firstly, for a comparison to hold, the preferred entity usually receives a positive sentiment while its rival gets a relatively negative one. these sentiments can be easily extracted by the strong absa models. the contrast between the sentiments assigned to the compared entities provides a vital clue for an accurate cpc. secondly, the absa models are designed to target the sentiments toward phrases, which bypasses the complicated and noisy syntactic relation path. thirdly, considering the scarcity of the data resource of cpc, the abundant annotated data of absa can provide sufficient supervision signal to improve the accuracy of cpc. there is one challenge that blocks the knowledge transfer of sentiment analysis from the absa data to the cpc task: domain shift. existing absa datasets are centered around specific topics such as restaurants and laptops, while the cpc data has mixed topics @xcite that are all distant from restaurants. in other words, sentences of absa and cpc datasets are drawn from different distributions, also known as domains. the difference in the distributions is referred to as a "domain shift" @xcite and it is harmful to an accurate knowledge transfer. to mitigate the domain shift, we design a domain adaptive layer to remove the domainspecific feature such as topics and preserve the domain-invariant feature such as sentiments of the text so that the sentiment analyzer can smoothly transfer knowledge from sentiment analysis to comparative classification. ## saecon in this section, we first formalize the problem and then explain saecon in detail. the pipeline of saecon is depicted in figure 1 with essential notations. ## conclusion this paper proposes saecon, a cpc model that incorporates a sentiment analyzer to transfer knowledge from absa corpora. specifically, saecon utilizes a bilstm to learn global comparative features, a syntactic gcn to learn local syntactic information, and a domain adaptive auxiliary sentiment analyzer that jointly learns from absa corpora and cpc corpora for a smooth knowledge transfer. an alternative joint training scheme enables the efficient and effective information transfer. qualitative and quantitative experiments verified the superior performance of saecon. for future work, we will focus on a deeper understanding of cpc data augmentation and an exploration of weighting loss methods for data imbalance.