Domain-Specific Cross-Language Relevant Question Retrieval

In software development process, developers often seek solutions to the technical problems they encounter by searching relevant questions on Q&A sites. When developers fail to find solutions on Q&A sites in their native language (e.g., Chinese), they could translate their query and search on the Q&A sites in another language (e.g., English). However, developers who are non-native English speakers often are not comfortable to ask or search questions in English, as they do not know the proper translation of the Chinese technical words into the English technical words. Furthermore, the process of manually formulating cross-language queries and determining the weight of query words is a tedious and time-consuming process. For the purpose of helping Chinese developers take advantage of the rich knowledge base of the English version of Stack Overflow and simplify the retrieval process, we propose an automated crosslanguage relevant question retrieval (CLRQR) system to retrieve relevant English questions on Stack Overflow for a given Chinese question. Our CLRQR system first extracts essential information (both Chinese and English) from the title and description of the input Chinese question, then performs domain-specific translation of the essential Chinese information into English, and formulates a query with highest-scored English words for retrieving relevant questions in a repository of 684,599 Java questions in English from Stack Overflow. To evaluate the performance of our proposed approach, we also propose four online retrieval approaches as baselines. We randomly select 80 Java questions in SegmentFault and V2EX (two Chinese Q&A websites for computer programming) as the query Chinese questions. Each approach returns top-10 most relevant questions for a given Chinese question. We invite 5 users to evaluate the relevance of the retrieved English questions. The experiment results show that CLRQR system outperforms the four baseline approaches, and the statistical tests show the improvements are significant.