Unlocking Markets: A Multilingual Benchmark to Cross-Market Question Answering

TL;DR

This paper introduces MCPQA, a new multilingual, cross-market question answering benchmark with over 7 million questions, demonstrating that leveraging cross-market data improves answer quality and question ranking.

Contribution

The paper presents a large-scale multilingual dataset for cross-market product question answering and benchmarks various models, highlighting the benefits of cross-market information integration.

Findings

Cross-market data improves answer accuracy.

LLMs outperform traditional models in this task.

Multilingual dataset enables broader applicability.

Abstract

Users post numerous product-related questions on e-commerce platforms, affecting their purchase decisions. Product-related question answering (PQA) entails utilizing product-related resources to provide precise responses to users. We propose a novel task of Multilingual Cross-market Product-based Question Answering (MCPQA) and define the task as providing answers to product-related questions in a main marketplace by utilizing information from another resource-rich auxiliary marketplace in a multilingual context. We introduce a large-scale dataset comprising over 7 million questions from 17 marketplaces across 11 languages. We then perform automatic translation on the Electronics category of our dataset, naming it as McMarket. We focus on two subtasks: review-based answer generation and product-related question ranking. For each subtask, we label a subset of McMarket using an LLM and further evaluate the quality of the annotations via human assessment. We then conduct experiments to benchmark our dataset, using models ranging from traditional lexical models to LLMs in both single-market and cross-market scenarios across McMarket and the corresponding LLM subset. Results show that incorporating cross-market information significantly enhances performance in both tasks.