Community Question Answering

Community Question-Answering (CQA), where questions and answers are generated by peers, has become a popular method of information seeking in online environments. While the content repositories created through CQA sites have been used widely to support general purpose tasks, using them as online digital libraries that support educational needs is an emerging practice. Horizontal CQA services, such as Yahoo! Answers, and vertical CQA services , such as Brainly, are aiming to help students improve their learning process by answering their educational questions. In these services, receiving high quality answer(s) to a question is a critical factor not only for user satisfaction, but also for supporting learning. However, the questions are not necessarily answered by experts, and the askers may not have enough knowledge and skill to evaluate the quality of the answers they receive. This could be problematic when students build their own knowledge base by applying inaccurate information or knowledge acquired from online sources. Using moderators could alleviate this problem. However, a moder-ator's evaluation of answer quality may be inconsistent because it is based on their subjective assessments. Employing human assessors may also be insufficient due to the large amount of content available on a CQA site. To address these issues, we propose a framework for automatically assessing the quality of answers. This is achieved by integrating different groups of features-personal, community-based, textual, and contextual-to build a classification model and determine what constitutes answer quality. To test this evaluation framework, we collected more than 10 million educational answers posted by more than 3 million users on Brainly's United States and Poland sites. The experiments conducted on these datasets show that the model using Random Forest (RF) achieves more than 83% accuracy in identifying high quality of answers. In addition, the findings indicate that personal and community-based features have more prediction power in assessing answer quality. Our approach also achieves high values on other key metrics such as F1-score and Area under ROC curve. The work reported here can be useful in many other contexts where providing automatic quality assessment in a digital repository of textual information is paramount.

In the process of online storytelling, individual users create and consume highly diverse content that contains a great deal of implicit beliefs and not plainly expressed narrative. It is hard to manually detect these implicit beliefs, intentions and moral foundations of the writers. We study and investigate two different tasks, each of which reflect the difficulty of detecting an implicit user's knowledge, intent or belief that may be based on writer's moral foundation: 1) political perspective detection in news articles 2) identification of informational vs. conversational questions in community question answering (CQA) archives and. In both tasks we first describe new interesting annotated datasets and make the datasets publicly available. Second, we compare various classification algorithms, and show the differences in their performance on both tasks. Third, in political perspective detection task we utilize a narrative representation language of local press to identify perspective differences between presumably neutral American and British press.

We address the problem of cross-language adaptation for question-question similarity reranking in community question answering , with the objective to port a system trained on one input language to another input language given labeled training data for the first language and only unlabeled data for the second language. In particular, we propose to use adversarial training of neural networks to learn high-level features that are discriminative for the main learning task, and at the same time are invariant across the input languages. The evaluation results show sizable improvements for our cross-language adversarial neural network (CLANN) model over a strong non-adversarial system.

In this paper we analyse the quality of a recently-released dataset for community question-answering (cQA) research, CQADupStack [8]. This set contains cQA data from the Stack Exchange community, annotated by the forum users with duplicate question information. While there is the expectation that the labels are of high quality, no previous analysis has been done to determine the quality of these annotations in terms of precision or recall. We provide evidence for the reliability of the existing duplicate question labels (precision) and identify in a sample that around 1% of the question pairs is missing a duplicate label (recall). This may extrapolate to as many as 39 million question pairs. Two strategies for sampling question pairs for possible annotation to boost recall are presented. Our results suggest that we can increase the number of duplicates by around 45%, by annotating only 0.0003% of all the question pairs in the data set.

In recent years neural networks (NN) are being used increasingly on Question Answering (QA) systems and they seem to be successful in addressing different issues and challenges that these systems exhibit. This paper presents a review to summarize the state of the art in question answering systems implemented using neural networks. It identifies the main research topics and considers the most relevant research challenges. Furthermore, it analyzes contributions, limitations, evaluation techniques, and directions proposed for future research.