Making Better Use of Training Corpus: Retrieval-based Aspect Sentiment Triplet Extraction via Label Interpolation (2023)

Abstract: In this paper, we aim to adapt the idea of retrieval-based neural approaches to the Aspect Sentiment Triplet Extraction (ASTE) task. Different from previous studies retrieving semantic similar neighbors, the ASTE task has its specialized challenges when adapting, i.e., the purpose includes predicting the sentiment polarity and it is usually aspect-dependent. Semantic similar neighbors with different polarities will be infeasible even counterproductive. To tackle this issue, we propose a retrieval-based neural ASTE approach, named RLI (Retrieval-based Aspect Sentiment Triplet Extraction via Label Interpolation), which exploits the label information of neighbors. Given an aspect-opinion term pair, we retrieve semantic similar triplets from the training corpus and interpolate their label information into the augmented representation of the target pair. The retriever is jointly trained with the whole ASTE framework, and neighbors with both similar semantics and sentiments can be recalled with the aid of this distant supervision. In addition, we design a simple yet effective pre-train method for the retriever that implicitly encodes the label similarities. Extensive experiments and analysis on two widely-used benchmarks show that the proposed model establishes a new state-of-the-art on ASTE.

## introduction as an emerging sub-task of aspect-based sentiment analysis (absa), aspect sentiment triplets extraction (aste) extracts all sentimental triplets of a given sentence. every triplet contains three elements, namely aspect terms, opinion terms, and their corresponding sentiment polarities. for example, in the sentence "great food but the service is horrible.", aste attempts to identify (food, great, positive) and (service, horrible, negative). since the sentiment polarity of a triplet is aspectdependent and determined by the corresponding opinion terms, establishing reciprocity among elements within the triplets could yield easier sentiment predictions. following this idea, existing work devised advanced methods to explore the correlation between the aspect and the opinion terms. to name some, @xcite ; @xcite proposed new tagging schemes to build connections among three elements within a sentence. @xcite ; @xcite ; @xcite ; @xcite designed various end-to-end frameworks to explore relations among elements by sub-task interaction mechanisms. chen et al. (2021a); @xcite matched the elements by machine reading comprehension. despite their effectiveness, existing methods may fail to clarify the intricate relationships among el-ements in some challenging cases, e.g., sentences with uncommon aspect/opinion terms, or the aspect and opinion terms are distant from each other. for example, as shown in the first sentence in fig. 1 , "scallop roll" may be difficult to be extracted because "scallop" is an uncommon aspect word that rarely appeared in the training set. and it is intractable to connect "scallop roll" with "spicy" due to the long distance between them. these make it challenging to extract the triplet (scallop roll, spicy, positive). to tackle the above issues, we attempt to apply retrieval-based models to aste, which have shown strength in several nlp tasks @xcite @xcite ) such as language model, machine translation, etc. their basic idea is to retrieve semantic similar neighbors from training corpus or external data to improve the model's robustness towards infrequent data points @xcite . however, the aste task has its specialized challenges when adapting, i.e., its purpose includes predicting the sentiment polarities and it is usually aspect-dependent. for example, the two triplets (battery, long, positive) and (boot-time, long, negative) have the same opinion word but opposite aspect-level sentiment. hence, it may derive a drawback of the conventional retrieval-based model: the semantic similar neighbors with different sentiments may be infeasible even counterproductive. to remedy the challenges, we propose a retrieval-based neural aste approach, named rli (retrieval-based aspect sentiment triplet extraction via label interpolation), which can exploit the label information of neighbors. we first collect all triplets from the training set to construct a knowledge store and detect all candidate aspectopinion pairs. for each pair, we retrieve semantic similar triplets from the constructed store. then we interpolate their label information into the augmented representation of the candidate pair to predict the final sentiment. unlike existing retrievalbased methods which retrieve neighbors only according to semantic similarities, we jointly train the retriever and the triplets extraction model such that the neighbors with both similar semantics and sentiment could be fetched. in addition, we propose a simple yet effective method to pre-train the proposed retriever, which could encode label information implicitly by using pseudo-labeled data before the joint training. exhibiting our idea by an example in fig. 1 , rli could retrieve a relevant triplet (tuna roll, spicy, positive) for the candidate pair (scallop roll, spicy) (cf. the green block). by high relevance between "tuna roll" and "scallop roll", we can infer that (scallop roll, spicy) could be a valid pair and deduce its positive polarity. while, as shown in the blue block, the conventional retrieval-based methods may likely fetch a triplet (cocktail, spicy, negative), which has an opposite sentiment and may give false guidance. extensive experimental results and analysis on two standard datasets for aste show that the proposed model establishes a new state-of-the-art on the aste task and performs well on challenging examples. ## conclusion in this paper, we proposed a retrieval-based aste approach name rli, which could exploit the sentiment information of neighbors to solve challenging cases in aste. a retriever fetching both semantic and sentiment-similar triplets is devised, and we jointly train the retriever with the aste framework to remedy the specialized challenges when adapting the retrieve-based methods in aspect-level sentiment analysis tasks. in addition, we proposed the is every penny and you get ( both in and ) . (quality, more than enough, pos) (cake, not ultra sweet, neg) (cake, flurry, pos) was and so was the . the icing made this , it was , , and . a simple yet effective pre-train method for the retriever to implicitly encode the label similarities. extensive experiments and analyses have proven the superiority of the proposed method.