Potential Answerers Research Articles

MATER: Bi-level matching-aggregation model for time-aware expert recommendation

Community question answering (CQA) websites have grown rapidly, but they face a gap between questions and answerers. This gap causes delays in getting answers and overwhelms potential answerers. Expert recommendation (ER) can bridge this gap by providing suitable answerers for the newly posted questions in time. However, most ER methods have some limitations. First, they focus on the representation of a user’s interest, but they ignore their ability to give high-quality answers (user expertise). Second, they do not consider that the answerers’ interests and expertise may change over time. Third, they only use the accepted answer as a good example, but they ignore other good answers that are not accepted. To address these issues, we propose MATER, a Bi-level Matching-Aggregation Model for Time-Aware Expert Recommendation to address these issues. MATER utilizes a matching-aggregation framework at two levels of sentence and question. MATER first matches the target question with each question in the user’s profile by a sentence-level matching-aggregation model. Next, the question matching results are aggregated by a proposed multi-perspective time-aware aggregation layer that considers both time-aware interest and expertise of a user. We propose to employ a listwise loss function to train our model, which takes into consideration all potential experts in the ranking strategy. Experimental results on six real-world CQA datasets from different domains demonstrate that MATER significantly outperforms state-of-the-art ER methods.

Gender screening on question-answering communities

Modern community Question Answering (cQA) platforms encourage their members to publish any sort of question, which later on can get numerous answers from other community peers. However, in this dynamic, there is an intrinsic delay from the moment questions are posted until the arrival of acceptable and/or diverse responses. Therefore, cQA platforms have the pressing need for promoting unresolved questions to potential answerers, while also reducing gender disparity across their topics, for example. Needless to say, demographic analysis occupies a crucial role in successfully responding to these challenges.Nonetheless, there are only a handful of studies dissecting automatic gender recognition across cQA fellows. As far as we know, this work is the first effort to tease out the contribution to this task of the different kinds of textual inputs contained in their profiles (i.e., question titles and bodies, answers and self-descriptions). With this goal, we compare three different types of machine learning approaches under several combinations of these four input signals: traditional neural networks (e.g., RCNN and CNN), fine-tuned pre-trained transformers (e.g., BERT and RoBERTa) and statistical methods enriched with hand-crafted linguistic features (e.g., Bayes and MaxEnt).In a nutshell, our results show that pre-trained transformers are superior when dealing with full questions, conventional neural networks when mixing diverse text signals, and statistical methods when the dataset encompasses mostly noisy user-generated content, namely answers. In addition, our in-depth analysis reveals that dependency parsing is instrumental in designing hand-crafted features capable of modelling topic information, and that both genders are conspicuously represented by some specific topic distributions.

Improving Stack Overflow question title generation with copying enhanced CodeBERT model and bi-modal information

Stack Overflow is very helpful for software developers who are seeking answers to programming problems. Previous studies have shown that a growing number of questions are of low quality and thus obtain less attention from potential answerers. Gao et al. proposed an LSTM-based model (i.e., BiLSTM-CC) to automatically generate question titles from the code snippets to improve the question quality. However, only using the code snippets in the question body cannot provide sufficient information for title generation, and LSTMs cannot capture the long-range dependencies between tokens. This paper proposes CCBERT, a deep learning based novel model to enhance the performance of question title generation by making full use of the bi-modal information of the entire question body. CCBERT follows the encoder–decoder paradigm and uses CodeBERT to encode the question body into hidden representations, a stacked Transformer decoder to generate predicted tokens, and an additional copy attention layer to refine the output distribution. Both the encoder and decoder perform the multi-head self-attention operation to better capture the long-range dependencies. This paper builds a dataset containing around 200,000 high-quality questions filtered from the data officially published by Stack Overflow to verify the effectiveness of the CCBERT model. CCBERT outperforms all the baseline models on the dataset. Experiments on both code-only and low-resource datasets show the superiority of CCBERT with less performance degradation. The human evaluation also shows the excellent performance of CCBERT concerning both readability and correlation criteria. CCBERT is capable of automatically capturing the bi-modal semantic information from the entire question body and parsing the long-range dependencies to achieve better performance. Therefore, CCBERT is an effective approach for generating Stack Overflow question titles.

Hierarchical Attentional Factorization Machines for Expert Recommendation in Community Question Answering

The most challenging task of Community Question Answering (CQA) is to provide high-quality answers to users’ questions. Currently, a variety of expert recommendation methods have been proposed and greatly improved the effective matching between questions and potential good answerers. However, the performance of existing methods can be adversely affected by many common factors such as data sparsity and noise problem, which cause less precise user modeling. Moreover, existing methods often model user-question interactions through simple ways, failing to capture the multiple scale interactions of question and answerers, which make it difficult to find answerers who are able to provide the best answers. In this paper, we propose an attention-based variant of Factorization Machines (FM) called Hierarchical Attentional Factorization Machines (HaFMRank) for answerer recommendation in CQA, which not only models the interactions between pairs of individual features but emphasizes the roles of crucial features and pairwise interactions. Specifically, we introduce the within-field attention layer to capture the inner structure of features belonging to the same field, while a feature-interaction attention layer is adopted to examine the importance of each pairwise interaction. A pre-training procedure is designed to generate latent FM feature embedding that encode question context and user history into the training process of HaFMRank. The performance of the proposed HaFMRank is evaluated by using real-world datasets of Stack Exchange and experimental results demonstrate that it outperforms several state-of-the-art methods in best answerer recommendation.

Retrieving people: Identifying potential answerers in Community Question‐Answering

Community Question‐Answering (CQA) sites have become popular venues where people can ask questions, seek information, or share knowledge with a user community. Although responses on CQA sites are obviously slower than information retrieved by a search engine, one of the most frustrating aspects of CQAs occurs when an asker's posted question does not receive a reasonable answer or remains unanswered. CQA sites could improve users' experience by identifying potential answerers and routing appropriate questions to them. In this paper, we predict the potential answerers based on question content and user profiles. Our approach builds user profiles based on past activity. When a new question is posted, the proposed method computes scores between the question and all user profiles to find the potential answerers. We conduct extensive experimental evaluations on two popular CQA sites ‐ Yahoo! Answers and Stack Overflow ‐ to show the effectiveness of our algorithm. The results show that our technique is able to predict a small group of 1000 users from which at least one user will answer the question with a probability higher than 50% in both CQA sites. Further analysis indicates that topic interest and activity level can improve the correctness of our approach.

A Survey on Expert Recommendation in Community Question Answering

Community question answering (CQA) represents the type of Web applications where people can exchange knowledge via asking and answering questions. One significant challenge of most real-world CQA systems is the lack of effective matching between questions and the potential good answerers, which adversely affects the efficient knowledge acquisition and circulation. On the one hand, a requester might experience many low-quality answers without receiving a quality response in a brief time; on the other hand, an answerer might face numerous new questions without being able to identify the questions of interest quickly. Under this situation, expert recommendation emerges as a promising technique to address the above issues. Instead of passively waiting for users to browse and find their questions of interest, an expert recommendation method raises the attention of users to the appropriate questions actively and promptly. The past few years have witnessed considerable efforts that address the expert recommendation problem from different perspectives. These methods all have their issues that need to be resolved before the advantages of expert recommendation can be fully embraced. In this survey, we first present an overview of the research efforts and state-of-the-art techniques for the expert recommendation in CQA. We next summarize and compare the existing methods concerning their advantages and shortcomings, followed by discussing the open issues and future research directions.

Search clicks analysis for discovering temporally anchored questions in community Question Answering

Nowadays, community Question-Answering (cQA) sites are massive repositories for user-generated content, where members prompt questions expecting satisfactory answers from other members. However, in this dynamic, there is an intrinsic delay between the moment questions are posted to the arrival of acceptable responses. Therefore, cQA platforms have the pressing need for promoting unresolved questions to potential answerers and for taking advantage of resolved questions contained in their archives, whenever possible.This paper studies cQA services from the viewpoint of the time frame where their questions attract the interest of their community members. By drawing a parallel with temporal patterns of user interests in web search activity, we are able to define three main types of temporally anchored questions: trend or bursty, periodic and permanent. Then, by analyzing user click distributions to Yahoo! Answers pages across Yahoo! Search logs, we automatically acquired a set of 35,000 cQA questions labeled with one of these three temporal anchors. Accordingly, we show the practicality of this approach by means of human assessments; and by using this automatically acquired corpus for studying several classification models.Essentially, the proposed method was found to correlate well with these human judgements, and proven to be effective in building systems that automatically identify the temporal anchor of unseen cQA questions. In substance, our outcomes indicate that some contexts are strongly related to a particular temporal anchor. We believe that these anchors will contribute to the discrimination of resolved questions that are capable of being revitalized, as well as to foster the opportune participation in questions that generate enthusiasm only for a short time.

Optimal answerer ranking for new questions in community question answering

Community question answering (CQA) services that enable users to ask and answer questions have become popular on the internet. However, lots of new questions usually cannot be resolved by appropriate answerers effectively. To address this question routing task, in this paper, we treat it as a ranking problem and rank the potential answerers by the probability that they are able to solve the given new question. We utilize tensor model and topic model simultaneously to extract latent semantic relations among asker, question and answerer. Then, we propose a learning procedure based on the above models to get optimal ranking of answerers for new questions by optimizing the multi-class AUC (Area Under the ROC Curve). Experimental results on two real-world CQA datasets show that the proposed method is able to predict appropriate answerers for new questions and outperforms other state-of-the-art approaches.

SOS: A Distributed Mobile Q&A SystemBased on Social Networks

Recently, emerging research efforts have been focused on question and answer (Q&A) systems based on social networks. The social-based Q&A systems can answer non-factual questions, which cannot be easily resolved by web search engines. These systems either rely on a centralized server for identifying friends based on social information or broadcast a user's questions to all of its friends. Mobile Q&A systems, where mobile nodes access the Q&A systems through Internet, are very promising considering the rapid increase of mobile users and the convenience of practical use. However, such systems cannot directly use the previous centralized methods or broadcasting methods, which generate high cost of mobile Internet access, node overload, and high server bandwidth cost with the tremendous number of mobile users. We propose a distributed Social-based mObile Q&A System (SOS) with low overhead and system cost as well as quick response to question askers. SOS enables mobile users to forward questions to potential answerers in their friend lists in a decentralized manner for a number of hops before resorting to the server. It leverages lightweight knowledge engineering techniques to accurately identify friends who are able to and willing to answer questions, thus reducing the search and computation costs of mobile nodes. The trace-driven simulation results show that SOS can achieve a high query precision and recall rate, a short response latency and low overhead. We have also deployed a pilot version of SOS for use in a small group in Clemson University. The feedback from the users shows that SOS can provide high-quality answers.

Link Prediction based on Structural Properties of Online Social Networks

Question-Answering Bulletin Boards (QABB), such as Yahoo! Answers and Windows Live QnA, are gaining popularity recently. Questions are submitted on QABB and let somebody in the internet answer them. Communications on QABB connect users, and the overall connections can be regarded as a social network. If the evolution of social networks can be predicted, it is quite useful for encouraging communications among users. Link prediction on QABB can be used for recommendation to potential answerers.