Answer Ranking for Product-Related Questions via Multiple Semantic Relations Modeling

Many E-commerce sites now offer product-specific question answering platforms for users to communicate with each other by posting and answering questions during online shopping. However, the multiple answers provided by ordinary users usually vary diversely in their qualities and thus need to be appropriately ranked for each question to improve user satisfaction. It can be observed that product reviews usually provide useful information for a given question, and thus can assist the ranking process. In this paper, we investigate the answer ranking problem for product-related questions, with the relevant reviews treated as auxiliary information that can be exploited for facilitating the ranking. We propose an answer ranking model named MUSE which carefully models multiple semantic relations among the question, answers, and relevant reviews. Specifically, MUSE constructs a multi-semantic relation graph with the question, each answer, and each review snippet as nodes. Then a customized graph convolutional neural network is designed for explicitly modeling the semantic relevance between the question and answers, the content consistency among answers, and the textual entailment between answers and reviews. Extensive experiments on real-world E-commerce datasets across three product categories show that our proposed model achieves superior performance on the concerned answer ranking task.Comment: Accepted by SIGIR 202

Length-adaptive Neural Network for Answer Selection

Answer selection focuses on selecting the correct answer for a question. Most previous work on answer selection achieves good performance by employing an RNN, which processes all question and answer sentences with the same feature extractor regardless of the sentence length. These methods often encounter the problem of long-term dependencies. To address this issue, we propose a Length-adaptive Neural Network (LaNN) for answer selection that can auto-select a neural feature extractor according to the length of the input sentence. In particular, we propose a flexible neural structure that applies a BiLSTM-based feature extractor for short sentences and a Transformer-based feature extractor for long sentences. To the best of our knowledge, LaNN is the first neural network structure that can auto-select the feature extraction mechanism based on the input. We quantify the improvements of LaNN against several competitive baselines on the public WikiQA dataset, showing significant improvements over the state-of-the-art