Bridge-Based Active Domain Adaptation for Aspect Term Extraction (2021)

Abstract: As a fine-grained task, the annotation cost of aspect term extraction is extremely high. Recent attempts alleviate this issue using domain adaptation that transfers common knowledge across domains. Since most aspect terms are domain-specific, they cannot be transferred directly. Existing methods solve this problem by associating aspect terms with pivot words (we call this passive domain adaptation because the transfer of aspect terms relies on the links to pivots). However, all these methods need either manually labeled pivot words or expensive computing resources to build associations. In this paper, we propose a novel active domain adaptation method. Our goal is to transfer aspect terms by actively supplementing transferable knowledge. To this end, we construct syntactic bridges by recognizing syntactic roles as pivots instead of as links to pivots. We also build semantic bridges by retrieving transferable semantic prototypes. Extensive experiments show that our method significantly outperforms previous approaches.

## introduction aspect term extraction (ate) is a fundamental task in aspect-based sentiment analysis. given a review sentence "the pizza here is also absolutely delicious.", ate aims to extract the term pizza. recent studies define ate as a sequence tagging task and propose supervised taggers @xcite . however, due to the high cost of token-level annotation, the lack of labeled data becomes the main obstacle @xcite . to alleviate the data deficiency issue, unsupervised domain adaptation is proposed to transfer knowledge from the labeled source domain to the unlabeled target domain. since ate is a tokenlevel task, it is natural to conduct token-level domain adaptation. then a problem arises: many *corresponding author. figure 1 : the proportion of source aspect terms that appear in target data. r (restaurant), l (laptop), and d (device) are three datasets from different domains. to solve this problem, previous studies try to associate aspect terms with specific pivot words foot_0 . we name these methods passive domain adaptation because the transfer of aspect terms is dependent on their links to the pivots. there are two types of methods along this line. (1) opinion terms as pivots. since aspect and opinion terms usually appear in pairs, it is straightforward to extract aspect terms with the indication from opinion terms. early studies @xcite use common opinion seeds (e.g., good, fancy) and pre-defined rules (e.g., @xmath0. however, it is hard to collect a complete set of seeds or define high-quality rules, and thus these methods often produce inferior performance. several studies @xcite ) manually annotate all opinion terms in reviews and design neural models to capture aspectopinion relations via multi-task learning. while getting improvements, these methods induce additional annotation costs. (2) context terms as pivots. since pre-trained language models (plms) like bert represent words w.r.t their contexts, recent studies @xcite leverage plms to transfer aspect terms with common context terms 2 . however, not all context terms qualify as pivots (e.g., eat). in addition, plms like bert build word associations mainly based on semantic similarity in co-occurring contexts. for an aspect term like pizza, bert tends to link it to hamburger via a flow like @xmath1. consequently, it is hard for these methods to identify keyboard in the target domain based on the labeled term pizza in the source domain. in this paper, we propose a novel active domain adaptation method. concretely, we construct two types of bridges for all words, which can help transfer aspect terms across domains. an example in figure 2 shows how to identify the unseen target term keyboard based on the source term pizza. (1) the syntactic bridge aims to recognize transferable syntactic roles for the words across domains. though pizza and keyboard have almost no semantic relatedness, they often play a similar role in parse trees. in view of this, we treat the involved syntactic roles (including pos tag and dependency relations) of a certain word as its syntactic bridge. previous studies also utilize dependency information. however, we differ our method from existing ones in that we do not use dependency relations to associate pivot words with aspect terms. instead, we treat syntactic roles themselves as pivot features and do not need any manually annotated pivot words. (2) the semantic bridge moves one step further by retrieving transferable prototypes. intuitively, if we correlate pizza with some prototype target terms like {disk, os, mouse}, the domain discrepancy between the training and testing reviews can be largely reduced. hence we regard the proto-2 context terms denote all words that are not aspect terms. hence opinion terms form a subset of context terms. based on the syntactic/semantic bridges, we then develop an end-to-end tagger to fuse reviews with these transferable bridges. we conduct extensive experiments on three datasets. the results show that our method achieves a new state-of-the-art performance with a low computational cost. ## methodology in this section, we first introduce the cross-domain ate task. we then illustrate how to construct syntactic and semantic bridges. lastly, we present the bridge-based sequence tagging. ## conclusion in this paper, we propose a novel active domain adaptation method for aspect term extraction. unlike previous studies that conduct passive domain adaptation by associating aspect terms with pivots, we actively enhance the terms' transferability by constructing syntactic and semantic bridges for them. we then design a lightweight end-toend tagger for bridge-based sequence tagging. experiments on six transfer pairs demonstrate that our method achieves a new state-of-the-art performance with a quite low computational cost.