Transformer Models in the Home Improvement Domain

Abstract

To find answers for subjective questions about many topics through Q\&A forums, questioners and answerers can cooperatively help themselves by sharing their doubts or answers based on their background and life experiences. These experiences can help machines redirect questioners to find better answers based on community question-answering models. This work proposes a comparative analysis of the pairwise community answer retrieval models in the home improvement domain considering different kinds of user question context information. Community Question-Answering (CQA) models must rank candidate answers in decreasing order of relevance for a user question. Our contribution consists of transformer-based language models using different kinds of user information to accurate the model generalisation. To train our model, we propose a proper CQA dataset in the home improvement domain that consists of information extracted from community forums, including question context information. We evaluate our approach by comparing the performance of each baseline model based on rank-aware evaluation measures.