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.     

Generative retrieval for conversational question answering

Effective passage retrieval is crucial for conversation question answering (QA) but challenging due to the ambiguity of questions. Current methods rely on the dual-encoder architecture to embed contextualized vectors of questions in conversations. However, this architecture is limited in the embedding bottleneck and the dot-product operation. To alleviate these limitations, we propose generative retrieval for conversational QA (GCoQA). GCoQA assigns distinctive identifiers for passages and retrieves passages by generating their identifiers token-by-token via the encoder–decoder architecture. In this generative way, GCoQA eliminates the need for a vector-style index and could attend to crucial tokens of the conversation context at every decoding step. We conduct experiments on three public datasets over a corpus containing about twenty million passages. The results show GCoQA achieves relative improvements of +13.6% in passage retrieval and +42.9% in document retrieval. GCoQA is also efficient in terms of memory usage and inference speed, which only consumes 1/10 of the memory and takes in less than 33% of the time. The code and data are released at https://github.com/liyongqi67/GCoQA.     

User interest modeling and its application for question recommendation in user-interactive question answering systems

In this paper, we propose a generative model, the Topic-based User Interest (TUI) model, to capture the user interest in the User-Interactive Question Answering (UIQA) systems. Specifically, our method aims to model the user interest in the UIQA systems with latent topic method, and extract interests for users by mining the questions they asked, the categories they participated in and relevant answer providers. We apply the TUI model to the application of question recommendation, which automatically recommends to certain user appropriate questions he might be interested in. Data collection from Yahoo! Answers is used to evaluate the performance of the proposed model in question recommendation, and the experimental results show the effectiveness of our proposed model.     

A question-guided multi-hop reasoning graph network for visual question answering

Visual Question Answering (VQA) requires reasoning about the visually-grounded relations in the image and question context. A crucial aspect of solving complex questions is reliable multi-hop reasoning, i.e., dynamically learning the interplay between visual entities in each step. In this paper, we investigate the potential of the reasoning graph network on multi-hop reasoning questions, especially over 3 “hops.” We call this model QMRGT: A Question-Guided Multi-hop Reasoning Graph Network. It constructs a cross-modal interaction module (CIM) and a multi-hop reasoning graph network (MRGT) and infers an answer by dynamically updating the inter-associated instruction between two modalities. Our graph reasoning module can apply to any multi-modal model. The experiments on VQA 2.0 and GQA (in fully supervised and O.O.D settings) datasets show that both QMRGT and pre-training V&L models+MRGT lead to improvement on visual question answering tasks. Graph-based multi-hop reasoning provides an effective signal for the visual question answering challenge, both for the O.O.D and high-level reasoning questions.     

Why users keep answering questions in online question answering communities: A theoretical and empirical investigation

This study theorized and validated a model of knowledge sharing continuance in a special type of online community, the online question answering (Q&A) community, in which knowledge exchange is reflected mainly by asking and answering specific questions. We created a model that integrated knowledge sharing factors and knowledge self-efficacy into the expectation confirmation theory. The hypotheses derived from this model were empirically validated using an online survey conducted among users of a famous online Q&A community in China, “Yahoo! Answers China”. The results suggested that users’ intention to continue sharing knowledge (i.e., answering questions) was directly influenced by users’ ex-post feelings as consisting of two dimensions: satisfaction, and knowledge self-efficacy. Based on the obtained results, we also found that knowledge self-efficacy and confirmation mediated the relationship between benefits and satisfaction.     

ReGR: Relation-aware graph reasoning framework for video question answering

As one of the challenging cross-modal tasks, video question answering (VideoQA) aims to fully understand video content and answer relevant questions. The mainstream approach in current work involves extracting appearance and motion features to characterize videos separately, ignoring the interactions between them and with the question. Furthermore, some crucial semantic interaction details between visual objects are overlooked. In this paper, we propose a novel Relation-aware Graph Reasoning (ReGR) framework for video question answering, which first combines appearance–motion and location–semantic multiple interaction relations between visual objects. For the interaction between appearance and motion, we design the Appearance–Motion Block, which is question-guided to capture the interdependence between appearance and motion. For the interaction between location and semantics, we design the Location–Semantic Block, which utilizes the constructed Multi-Relation Graph Attention Network to capture the geometric position and semantic interaction between objects. Finally, the question-driven Multi-Visual Fusion captures more accurate multimodal representations. Extensive experiments on three benchmark datasets, TGIF-QA, MSVD-QA, and MSRVTT-QA, demonstrate the superiority of our proposed ReGR compared to the state-of-the-art methods.     

Answer extraction and ranking strategies for definitional question answering using linguistic features and definition terminology

We propose answer extraction and ranking strategies for definitional question answering using linguistic features and definition terminology. A passage expansion technique based on simple anaphora resolution is introduced to retrieve more informative sentences, and a phrase extraction method based on syntactic information of the sentences is proposed to generate a more concise answer. In order to rank the phrases, we use several evidences including external definitions and definition terminology. Although external definitions are useful, it is obvious that they cannot cover all the possible targets. The definition terminology score which reflects how the phrase is definition-like is devised to assist the incomplete external definitions. Experimental results show that the proposed answer extraction and ranking method are effective and also show that our proposed system is comparable to state-of-the-art systems.     

Be flexible! learn to debias by sampling and prompting for robust visual question answering

Recent studies point out that VQA models tend to rely on the language prior in the training data to answer the questions, which prevents the VQA model from generalization on the out-of-distribution test data. To address this problem, approaches are designed to reduce the language distribution prior effect by constructing negative image–question pairs, while they cannot provide the proper visual reason for answering the question. In this paper, we present a new debiasing framework for VQA by Learning to Sample paired image–question and Prompt for given question (LSP). Specifically, we construct the negative image–question pairs with certain sampling rate to prevent the model from overly relying on the visual shortcut content. Notably, question types provide a strong hint for answering the questions. We utilize question type to constrain the sampling process for negative question–image pairs, and further learn the question type-guided prompt for better question comprehension. Extensive experiments on two public benchmarks, VQA-CP v2 and VQA v2, demonstrate that our model achieves new state-of-the-art results in overall accuracy, i.e., 61.95% and 65.26%.     

Boosting question answering over knowledge graph with reward integration and policy evaluation under weak supervision

Among existing knowledge graph based question answering (KGQA) methods, relation supervision methods require labeled intermediate relations for stepwise reasoning. To avoid this enormous cost of labeling on large-scale knowledge graphs, weak supervision methods, which use only the answer entity to evaluate rewards as supervision, have been introduced. However, lacking intermediate supervision raises the issue of sparse rewards, which may result in two types of incorrect reasoning path: (1) incorrectly reasoned relations, even when the final answer entity may be correct; (2) correctly reasoned relations in a wrong order, which leads to an incorrect answer entity. To address these issues, this paper considers the multi-hop KGQA task as a Markov decision process, and proposes a model based on Reward Integration and Policy Evaluation (RIPE). In this model, an integrated reward function is designed to evaluate the reasoning process by leveraging both terminal and instant rewards. The intermediate supervision for each single reasoning hop is constructed with regard to both the fitness of the taken action and the evaluation of the unreasoned information remained in the updated question embeddings. In addition, to lead the agent to the answer entity along the correct reasoning path, an evaluation network is designed to evaluate the taken action in each hop. Extensive ablation studies and comparative experiments are conducted on four KGQA benchmark datasets. The results demonstrate that the proposed model outperforms the state-of-the-art approaches in terms of answering accuracy.     

Effects of answer weight boosting in strategy-driven question answering

With the advances in natural language processing (NLP) techniques and the need to deliver more fine-grained information or answers than a set of documents, various QA techniques have been developed corresponding to different question and answer types. A comprehensive QA system must be able to incorporate individual QA techniques as they are developed and integrate their functionality to maximize the system’s overall capability in handling increasingly diverse types of questions. To this end, a new QA method was developed to learn strategies for determining module invocation sequences and boosting answer weights for different types of questions. In this article, we examine the roles and effects of the answer verification and weight boosting method, which is the main core of the automatically generated strategy-driven QA framework, in comparison with a strategy-less, straightforward answer-merging approach and a strategy-driven but with manually constructed strategies.     

Linguistic kernels for answer re-ranking in question answering systems

Answer selection is the most complex phase of a question answering (QA) system. To solve this task, typical approaches use unsupervised methods such as computing the similarity between query and answer, optionally exploiting advanced syntactic, semantic or logic representations.     

Counterfactual can be strong in medical question and answering

Medical question and answering is a crucial aspect of medical artificial intelligence, as it aims to enhance the efficiency of clinical diagnosis and improve treatment outcomes. Despite the numerous methods available for medical question and answering, they tend to overlook the data generation mechanism’s imbalance and the pseudo-correlation caused by the task’s text characteristics. This pseudo-correlation is due to the fact that many words in the question and answering task are irrelevant to the answer but carry significant weight. These words can affect the feature representation and establish a false correlation with the final answer. Furthermore, the data imbalance mechanism can cause the model to blindly follow a large number of classes, leading to bias in the final answer. Confounding factors, including the data imbalance mechanism, bias due to textual characteristics, and other unknown factors, may also mislead the model and limit its performance.In this study, we propose a new counterfactual-based approach that includes a feature encoder and a counterfactual decoder. The feature encoder utilizes ChatGPT and label resetting techniques to create counterfactual data, compensating for distributional differences in the dataset and alleviating data imbalance issues. Moreover, the sampling prior to label resetting also helps us alleviate the data imbalance issue. Subsequently, label resetting can yield better and more balanced counterfactual data. Additionally, the construction of counterfactual data aids the subsequent counterfactual classifier in better learning causal features. The counterfactual decoder uses counterfactual data compared with real data to optimize the model and help it acquire the causal characteristics that genuinely influence the label to generate the final answer. The proposed method was tested on PubMedQA, a medical dataset, using machine learning and deep learning models. The comprehensive experiments demonstrate that this method achieves state-of-the-art results and effectively reduces the false correlation caused by confounders.     

Improving graph-based random walks for complex question answering using syntactic,shallow semantic and extended string subsequence kernels

The task of answering complex questions requires inferencing and synthesizing information from multiple documents that can be seen as a kind of topic-oriented, informative multi-document summarization. In generic summarization the stochastic, graph-based random walk method to compute the relative importance of textual units (i.e. sentences) is proved to be very successful. However, the major limitation of the TF^*IDF approach is that it only retains the frequency of the words and does not take into account the sequence, syntactic and semantic information. This paper presents the impact of syntactic and semantic information in the graph-based random walk method for answering complex questions. Initially, we apply tree kernel functions to perform the similarity measures between sentences in the random walk framework. Then, we extend our work further to incorporate the Extended String Subsequence Kernel (ESSK) to perform the task in a similar manner. Experimental results show the effectiveness of the use of kernels to include the syntactic and semantic information for this task.     

Combining evidence with a probabilistic framework for answer ranking and answer merging in question answering

Question answering (QA) aims at finding exact answers to a user’s question from a large collection of documents. Most QA systems combine information retrieval with extraction techniques to identify a set of likely candidates and then utilize some ranking strategy to generate the final answers. This ranking process can be challenging, as it entails identifying the relevant answers amongst many irrelevant ones. This is more challenging in multi-strategy QA, in which multiple answering agents are used to extract answer candidates. As answer candidates come from different agents with different score distributions, how to merge answer candidates plays an important role in answer ranking. In this paper, we propose a unified probabilistic framework which combines multiple evidence to address challenges in answer ranking and answer merging. The hypotheses of the paper are that: (1) the framework effectively combines multiple evidence for identifying answer relevance and their correlation in answer ranking, (2) the framework supports answer merging on answer candidates returned by multiple extraction techniques, (3) the framework can support list questions as well as factoid questions, (4) the framework can be easily applied to a different QA system, and (5) the framework significantly improves performance of a QA system. An extensive set of experiments was done to support our hypotheses and demonstrate the effectiveness of the framework. All of the work substantially extends the preliminary research in Ko et al. (2007a). A probabilistic framework for answer selection in question answering. In: Proceedings of NAACL/HLT.     

Improving neural topic modeling via Sinkhorn divergence

Textual data have been a major form to convey internet users’ content. How to effectively and efficiently discover latent topics among them has essential theoretical and practical value. Recently, neural topic models(NTMs), especially Variational Auto-encoder-based NTMs, proved to be a successful approach for mining meaningful and interpretable topics. However, they usually suffer from two major issues:(1)Posterior collapse: KL divergence will rapidly reach zeros resulting in low-quality representation in latent distribution; (2)Unconstrained topic generative models: Topic generative models are always unconstrained, which potentially leads to discovering redundant topics. To address these issues, we propose Autoencoding Sinkhorn Topic Model based on Sinkhorn Auto-encoder(SAE) and Sinkhorn divergence. SAE utilizes Sinkhorn divergence rather than problematic KL divergence to optimize the difference between posterior and prior, which is free of posterior collapse. Then, to reduce topic redundancy, Sinkhorn Topic Diversity Regularization(STDR) is presented. STDR leverages the proposed Salient Topic Layer and Sinkhorn divergence for measuring distance between salient topic features and serves as a penalty term in loss function facilitating discovering diversified topics in training. Several experiments have been conducted on 2 popular datasets to verify our contribution. Experiment results demonstrate the effectiveness of the proposed model.     

中国社会共同体的演变与重构:以民间组织为视角

共同体是西方学者用来分析现代性影响下西方社会变迁的一个学术概念。该文借用这个概念,系统梳理了中国社会共同体的演变轨迹,并在民间组织视角下探讨了当今中国社会共同体的重构问题。     

中国PC网络游戏行业商业模式创新的演化研究

本文从商业模式的演化逻辑、构成要素和创新的关键驱动因素入手,阐明了商业模式是企业如何创造价值、传递价值和获取价值的基本原理。结合已有文献和案例研究发现,商业模式主要由为谁提供、提供什么、如何提供和如何盈利四要素构成,新技术、竞争压力、消费者需求和企业家精神是商业模式创新的关键驱动力。在此基础上构建了商业模式创新演化的理论模型,并结合中国PC网络游戏行业深入研究了其商业模式演化历程,最后给出了行业未来商业模式创新的可能方向。     

居民视角下民族社区旅游化的空间演变过程及特征——以肇兴侗寨为例

从空间角度揭示旅游化的演变过程和特征可为深刻理解社区现象或问题提供参考.本文选取肇兴侗寨为案例地,通过2013年、2018年2次实地调查,采用参与式制图和半结构访谈获取一手数据,运用景观价值理论和质性空间分析方法,通过居民视角的景观价值变化探索社区旅游化在空间上的演变过程与特征.研究发现:①肇兴侗寨社区旅游化的过程包括...     

大中型工业企业技术创新投资演化行为分析

为了揭示大中型工业企业技术创新投资的演化行为与规律,构建了一个以差分形式表示的企业技术创新投资经济增长非线性动力学模型。采用差分演化算法对模型进行了参数优化并进行了计算机模拟。主要结论是:1企业的人均研发经费投入与科技活动人员比率对企业技术创新投资系统经济增长的演化行为有着决定性的影响,可以通过调节二者的值来改变系统的演化特性;2对二者的调节应在一定范围内进行;3并不是二者的值越大就必然导致企业人均工业增加值的上升;4当二者的值增大到一定条件会出现系统演化行为的分岔,分岔后会出现两种决然不同的结果,并最终进入混沌状态;5随着二者的值不断增大,系统进入分岔与走向混沌的速度也不断加快。