B v SP : Broad-view Soft Prompting for Few-Shot Aspect Sentiment Quad Prediction (2024)

Abstract: Aspect sentiment quad prediction (ASQP) aims to predict four aspect-based elements, including aspect term, opinion term, aspect category, and sentiment polarity. In practice, unseen aspects, due to distinct data distribution, impose many challenges for a trained neural model. Motivated by this, this work formulates ASQP into the few-shot scenario, which aims for fast adaptation in real applications. Therefore, we first construct a few-shot ASQP dataset (FSQP) that contains richer categories and is more balanced for the few-shot study. Moreover, recent methods extract quads through a generation paradigm, which involves converting the input sentence into a templated target sequence. However, they primarily focus on the utilization of a single template or the consideration of different template orders, thereby overlooking the correlations among various templates. To tackle this issue, we further propose a Broad-view Soft Prompting (BvSP) method that aggregates multiple templates with a broader view by taking into account the correlation between the different templates. Specifically, BvSP uses the pre-trained language model to select the most relevant k templates with Jensen–Shannon divergence. BvSP further introduces soft prompts to guide the pre-trained language model using the selected templates. Then, we aggregate the results of multi-templates by voting mechanism. Empirical results demonstrate that BvSP significantly outperforms the state-of-the-art methods under four few-shot settings and other public datasets. Our code and dataset are available at https://github.com/byinhao/BvSP.

## introduction analyzing user reviews, social media posts, product evaluations, and other content on the web to extract sentiment information related to specific aspects helps in understanding users' opinions and * corresponding author. however, in practical situations, aspect categories are not immutable and frozen @xcite . new aspects emerge as people discuss emerging phenomena, trends, products, and more through social media, news articles, and other means on the internet. as the restaurant domain illustrated in figure 1 , the initial aspect category set is pre-defined. yet as the infrastructure upgrades, new aspects, such as "wifi", gradually appear. the sentence's category, i.e. "internet" does not exist in the pre-defined categories. this imposes challenges to the model's comprehensive and accurate understanding of the sentence. moreover, the unseen aspect usually has a distribution shift, which is struggling for trained models to adapt accurately. therefore, researching the few-shot asqp task, i.e. fast adaptation to unseen aspects with only a few labeled samples, becomes crucial, as it aligns more closely with real-world application scenar- ## datasets to construct a dataset that is more representative of real-world scenarios, we manually annotate a new dataset, named few-shot asqp (fsqp). in this section, we will first describe the data collection and annotation process. following that, we will establish the superiority of fsqp by comparing it with previous datasets in terms of key statistics and features. ## conclusion this work studies the asqp task from the few-shot perspective, which aims to handle unseen aspects with only a few supported samples. therefore, we first build a new dataset called fsqp, which is specifically annotated for few-shot asqp. unlike existing asqp datasets, fsqp provides a more balanced representation and covers a wider range of categories, thereby serving as a comprehensive benchmark for evaluating few-shot asqp. moreover, the generation-based paradigm has become the state-of-the-art technique for asqp. however, previous methods overlook the correlation between different templates. in this study, we propose a broad-view soft prompting (bvsp) method to address this limitation. bvsp leverages the js divergence to analyze the correlation among templates and selects relevant ones. it then guides the pretrained language model with soft prompts based on these selected templates. finally, the results are aggregated through voting. extensive experiments conducted under few-shot settings demonstrate that bvsp exhibits universal effectiveness and substantial improvements in both explicit and implicit information.