# SCRQE: Subjective comparative relation quintuple extraction from questions in product domain

## TL;DR

This paper introduces SCQRE, a model that improves the extraction of subjective comparative relations from product-related questions, outperforming existing methods.

## Contribution

The novel SCQRE model introduces multi-task learning and a specialized adapter for better comparative relation extraction in product questions.

## Key findings

- SCQRE outperforms existing models on question-level and sentence-level datasets.
- The model handles X- and XOR-type preferences and captures implicit comparative nuances.
- SCQRE surpasses recent large language models like GPT-3.5 and Llama-2 in comparative relation extraction.

## Abstract

The extraction of subjective comparative relations is essential in the field of question answering systems, playing a crucial role in accurately interpreting and addressing complex questions. To tackle this challenge, we propose the SCQRE model, specifically designed to extract subjective comparative relations from questions by focusing on entities, aspects, constraints, and preferences. Our approach leverages multi-task learning, the Natural Language Inference (NLI) paradigm, and a specialized adapter integrated into RoBERTa_base_go_emotions to enhance performance in Element Extraction (EE), Compared Elements Identification (CEI), and Comparative Preference Classification (CPC). Key innovations include handling X- and XOR-type preferences, capturing implicit comparative nuances, and the robust extraction of constraints often neglected in existing models. We also introduce the Smartphone-SCQRE dataset, along with another domain-specific dataset, Brands-CompSent-19-SCQRE, both structured as subjective comparative questions. Experimental results demonstrate that our model outperforms existing approaches across multiple question-level and sentence-level datasets and surpasses recent language models, such as GPT-3.5-turbo-0613, Llama-2-70b-chat, and Qwen-1.5-7B-Chat, showcasing its effectiveness in question-based comparative relation extraction.