Cross-lingual embedding for cross-lingual question retrieval in low-resource community question answering

Abstract

In today’s digital world people are keen on finding the knowledge they need by surfing the internet to find the answers to their questions. To this aim, many Community Question Answering (CQA) systems are established, in which people can ask their question and receive the required information. The gathered data in such systems is a rich repository for people to search through the available questions that have been answered before. CQA users, however, are not always successful in finding their answers in their native CQA systems. One solution to enrich the searching process is translating input questions and searching them in other CQA systems. This solution is useless as the process of translating each question is time-consuming. To make the non-English CQA systems richer in finding the available answers, the systems can develop a model to find similar English questions. To help Persian CQA systems in providing the answers to the questions, we propose a cross-lingual question retrieval model to retrieve relevant English questions to any input Persian question. In the proposed model, we benefit from a translation model-based retrieval using neural cross-lingual word embedding. Our experiment shows that the proposed model achieves 71.4% MRR and 83.5% success@5 using supervised cross-lingual word embedding.