Identifying Unclear Questions in Community Question Answering Websites

Thousands of complex natural language questions are submitted to community question answering websites on a daily basis, rendering them as one of the most important information sources these days. However, oftentimes submitted questions are unclear and cannot be answered without further clarification questions by expert community members. This study is the first to investigate the complex task of classifying a question as clear or unclear, i.e., if it requires further clarification. We construct a novel dataset and propose a classification approach that is based on the notion of similar questions. This approach is compared to state-of-the-art text classification baselines. Our main finding is that the similar questions approach is a viable alternative that can be used as a stepping stone towards the development of supportive user interfaces for question formulation.Comment: Proceedings of the 41th European Conference on Information Retrieval (ECIR '19), 201

Expert recommendation via tensor factorization with regularizing hierarchical topical relationships

© Springer Nature Switzerland AG 2018. Knowledge acquisition and exchange are generally crucial yet costly for both businesses and individuals, especially when the knowledge concerns various areas. Question Answering Communities offer an opportunity for sharing knowledge at a low cost, where communities users, many of whom are domain experts, can potentially provide high-quality solutions to a given problem. In this paper, we propose a framework for finding experts across multiple collaborative networks. We employ the recent techniques of tree-guided learning (via tensor decomposition), and matrix factorization to explore user expertise from past voted posts. Tensor decomposition enables to leverage the latent expertise of users, and the posts and related tags help identify the related areas. The final result is an expertise score for every user on every knowledge area. We experiment on Stack Exchange Networks, a set of question answering websites on different topics with a huge group of users and posts. Experiments show our proposed approach produces steady and premium outputs

Comparison between parameter-efficient techniques and full fine-tuning: a case study on multilingual news article classification

Adapters and Low-Rank Adaptation (LoRA) are parameter-efficient fine-tuning techniques designed to make the training of language models more efficient. Previous results demonstrated that these methods can even improve performance on some classification tasks. This paper complements existing research by investigating how these techniques influence classification performance and computation costs compared to full fine-tuning. We focus specifically on multilingual text classification tasks (genre, framing, and persuasion techniques detection; with different input lengths, number of predicted classes and classification difficulty), some of which have limited training data. In addition, we conduct in-depth analyses of their efficacy across different training scenarios (training on the original multilingual data; on the translations into English; and on a subset of English-only data) and different languages. Our findings provide valuable insights into the applicability of parameter-efficient fine-tuning techniques, particularly for multilabel classification and non-parallel multilingual tasks which are aimed at analysing input texts of varying length

Why Will My Question Be Closed? NLP-Based Pre-Submission Predictions of Question Closing Reasons on Stack Overflow

Closing a question on a community question answering forum such as Stack Overflow is a highly divisive event. On one hand, moderation is of crucial importance in maintaining the content quality indispensable for the future sustainability of the site. On the other hand, details about the closing reason might frequently appear blurred to the user, which leads to debates and occasional negative behavior in answers or comments. With the aim of helping the users compose good quality questions, we introduce a set of classifiers for the categorization of Stack Overflow posts prior to their actual submission. Our binary classifier is capable of predicting whether a question will be closed after posting with an accuracy of71.87%. Additionally, in this study, we propose the first multiclass classifier to estimate the exact reason of closing a question to an accuracy of 48.55%. Both classifiers are based on Gated RecurrentUnits and trained solely on the pre-submission textual information of Stack Overflow posts

Equivalence-checking with infinite-state systems: Techniques and results

The paper presents an overview of techniques and results for equivalence-checking with infinite-state systems. The selection of presented techniques reflects the authors' view on recent development in the area; the overview of existing results should be up-to-date (as of August 2002)