Low-Resource Comparative Opinion Quintuple Extraction by Data Augmentation with Prompting (2023)

Abstract: Comparative Opinion Quintuple Extraction (COQE) aims to predict comparative opinion quintuples from comparative sentences. These quintuples include subject, object, shareable aspect, comparative opinion, and preference. The existing pipeline-based COQE method fails in error propagation. In addition, the complexity and insufficient amounts of annotated data hinder the performance of COQE models. In this paper, we introduce a novel approach called low-resource comparative opinion quintuple extraction by Data Augmentation with Prompting (DAP). Firstly, we present an end-to-end model architecture better suited to the data augmentation method from triplets to quintuples and can effectively avoid error propagation. Additionally, we introduce a data-centric augmentation approach that leverages the robust generative abilities of ChatGPT and integrates transfer learning techniques. Experimental results over three datasets (Camera, Car, Ele) demonstrate that our approach yields substantial improvements and achieves state-of-the-art results. The source code and data are publicly released at: https://github.com/qtxu-nlp/COQE-DAP.

## introduction coqe is an essential subfield of natural language processing (nlp). its primary objective is to extract five specific components from comparative sentences, namely: subject, object, shareable aspect, comparative opinion, and preference, as defined in @xcite . for example, in the sentence "like the viewfinder, the nikon d80 has the same sensor as the d200.", "nikon d80" and "d200" are respectively the subject and object entities, the aspect term is "sensor", the opinion word is "same", and the comparative preference is "equal". coqe plays a crucial role in various applications, such as comparative opinion mining @xcite @xcite , sentiment analysis @xcite @xcite , and customer satisfaction estimation @xcite . existing pipeline-based method @xcite suffers from error propagation. the heavy reliance on extensive annotated data poses a bottleneck in the training process. to address the aforementioned issues, we propose a data augmentation method with prompting for low-resource coqe. firstly, we propose a bert-based @xcite end-toend deep learning model as our backbone to avoid error propagation. although existing llms such as chatgpt possess rich linguistic knowledge and impressive generative capabilities, they encounter difficulties in generating satisfactory quintuple examples due to the inherent complexity of coqe. in this paper, we propose to develop a lightweight data augmentation, where the triple examples are required to be generated for augmentation, instead of the unabridged quintuple examples. it is relatively easy for chatgpt to produce some qualified triple examples rather than quintuple. additionally, we leverage these generated triple examples to warm up the end-to-end extraction model before training it over the benchmark quintuple dataset. to summarize, the main contributions of our work are as follows: ## conclusion and future work to avoid error propagation, we design an end-toend model. additionally, we propose a data-centric augmentation approach using the powerful generative capability of chatgpt. the performance on three datasets achieves sota. future work will focus on integrating existing annotated triplet data with automatically generated domain-specific data.