A major challenge to the problem of community question answering is the lexical and semantic gap between the sentence representations. Some solutions to minimize this gap includes the introduction of extra parameters to deep models or augmenting the external handcrafted features. In this paper, we propose a novel attentive recurrent tensor network for solving the lexical and semantic gap in community question answering. We introduce token-level and phrase-level attention strategy that maps input sequences to the output using trainable parameters. Further, we use the tensor parameters to introduce a 3-way interaction between question, answer and external features in vector space. We introduce simplified tensor matrices with L2 regularization that results in smooth optimization during training. The proposed model achieves state-of-the-art performance on the task of answer sentence selection (TrecQA and WikiQA datasets) while outperforming the current state-of-the-art on the tasks of best answer selection (Yahoo! L4) and answer triggering task (WikiQA).
Most of the previous work on automatic question answering for CQA sites show the advantage of feature engineering when used with deep models @cite_18 @cite_13 @cite_19 @cite_20 . Feature engineering introduces additional semantic and lexical rules which help the underlying learning model to gain an overall increase in performance. @cite_4 @cite_11 @cite_16 .
@cite_18 @cite_4 @cite_19 @cite_16 @cite_13 @cite_20 @cite_11
2174492417 2604420197 1986044318 2787553714 2192410469 1976727267 1970875273
We propose a novel attention based deep learning architecture for visual question answering task (VQA). Given an image and an image related natural language question, VQA generates the natural language answer for the question. Generating the correct answers requires the model's attention to focus on the regions corresponding to the question, because different questions inquire about the attributes of different image regions. We introduce an attention based configurable convolutional neural network (ABC-CNN) to learn such question-guided attention. ABC-CNN determines an attention map for an image-question pair by convolving the image feature map with configurable convolutional kernels derived from the question's semantics. We evaluate the ABC-CNN architecture on three benchmark VQA datasets: Toronto COCO-QA, DAQUAR, and VQA dataset. ABC-CNN model achieves significant improvements over state-of-the-art methods on these datasets. The question-guided attention generated by ABC-CNN is also shown to reflect the regions that are highly relevant to the questions. Programming community-based question-answering (PCQA) websites such as Stack Overflow enable programmers to find working solutions to their questions. Despite detailed posting guidelines, duplicate questions that have been answered are frequently created. To tackle this problem, Stack Overflow provides a mechanism for reputable users to manually mark duplicate questions. This is a laborious effort, and leads to many duplicate questions remain undetected. Existing duplicate detection methodologies from traditional community based question-answering (CQA) websites are difficult to be adopted directly to PCQA, as PCQA posts often contain source code which is linguistically very different from natural languages. In this paper, we propose a methodology designed for the PCQA domain to detect duplicate questions. We model the detection as a classification problem over question pairs. To extract features for question pairs, our methodology leverages continuous word vectors from the deep learning literature, topic model features and phrases pairs that co-occur frequently in duplicate questions mined using machine translation systems. These features capture semantic similarities between questions and produce a strong performance for duplicate detection. Experiments on a range of real-world datasets demonstrate that our method works very well; in some cases over 30 improvement compared to state-of-the-art benchmarks. As a product of one of the proposed features, the association score feature, we have mined a set of associated phrases from duplicate questions on Stack Overflow and open the dataset to the public. The rapidly increasing popularity of community-based Question Answering (cQA) services, e.g. Yahoo! Answers, Baidu Zhidao, etc. have attracted great attention from both academia and industry. Besides the basic problems, like question searching and answer finding, it should be noted that the low participation rate of users in cQA service is the crucial problem which limits its development potential. In this paper, we focus on addressing this problem by recommending answer providers, in which a question is given as a query and a ranked list of users is returned according to the likelihood of answering the question. Based on the intuitive idea for recommendation, we try to introduce topic-level model to improve heuristic term-level methods, which are treated as the baselines. The proposed approach consists of two steps: (1) discovering latent topics in the content of questions and answers as well as latent interests of users to build user profiles; (2) recommending question answerers for new arrival questions based on latent topics and term-level model. Specifically, we develop a general generative model for questions and answers in cQA, which is then altered to obtain a novel computationally tractable Bayesian network model. Experiments are carried out on a real-world data crawled from Yahoo! Answers during Jun 12 2007 to Aug 04 2007, which consists of 118510 questions, 772962 answers and 150324 users. The experimental results reveal significant improvements over the baseline methods and validate the positive influence of topic-level information. Visual Question Answering (VQA) has attracted attention from both computer vision and natural language processing communities. Most existing approaches adopt the pipeline of representing an image via pre-trained CNNs, and then using the uninterpretable CNN features in conjunction with the question to predict the answer. Although such end-to-end models might report promising performance, they rarely provide any insight, apart from the answer, into the VQA process. In this work, we propose to break up the end-to-end VQA into two steps: explaining and reasoning, in an attempt towards a more explainable VQA by shedding light on the intermediate results between these two steps. To that end, we first extract attributes and generate descriptions as explanations for an image using pre-trained attribute detectors and image captioning models, respectively. Next, a reasoning module utilizes these explanations in place of the image to infer an answer to the question. The advantages of such a breakdown include: (1) the attributes and captions can reflect what the system extracts from the image, thus can provide some explanations for the predicted answer; (2) these intermediate results can help us identify the inabilities of both the image understanding part and the answer inference part when the predicted answer is wrong. We conduct extensive experiments on a popular VQA dataset and dissect all results according to several measurements of the explanation quality. Our system achieves comparable performance with the state-of-the-art, yet with added benefits of explainability and the inherent ability to further improve with higher quality explanations. Learning the semantic representation using neural network architecture.The neural network is trained via pre-training and fine-tuning phase.The learned semantic level feature is incorporated into a LTR framework. In community question answering (cQA), users pose queries (or questions) on portals like Yahoo! Answers which can then be answered by other users who are often knowledgeable on the subject. cQA is increasingly popular on the Web, due to its convenience and effectiveness in connecting users with queries and those with answers. In this article, we study the problem of finding previous queries (e.g., posed by other users) which may be similar to new queries, and adapting their answers as the answers to the new queries. A key challenge here is to the bridge the lexical gap between new queries and old answers. For example, "company" in the queries may correspond to "firm" in the answers. To address this challenge, past research has proposed techniques similar to machine translation that "translate" old answers to ones using the words in the new queries. However, a key limitation of these works is that they assume queries and answers are parallel texts, which is hardly true in reality. As a result, the translated or rephrased answers may not look intuitive.In this article, we propose a novel approach to learn the semantic representation of queries and answers by using a neural network architecture. The learned semantic level features are finally incorporated into a learning to rank framework. We have evaluated our approach using a large-scale data set. Results show that the approach can significantly outperform existing approaches. This paper focuses on the problem of Question Routing (QR) in Community Question Answering (CQA), which aims to route newly posted questions to the potential answerers who are most likely to answer them. Traditional methods to solve this problem only consider the text similarity features between the newly posted question and the user profile, while ignoring the important statistical features, including the question-specific statistical feature and the user-specific statistical features. Moreover, traditional methods are based on unsupervised learning, which is not easy to introduce the rich features into them. This paper proposes a general framework based on the learning to rank concepts for QR. Training sets consist of triples (q, asker, answerers) are first collected. Then, by introducing the intrinsic relationships between the asker and the answerers in each CQA session to capture the intrinsic labels orders of the users about their expertise degree of the question q, two different methods, including the SVM-based and RankingSVM-based methods, are presented to learn the models with different example creation processes from the training set. Finally, the potential answerers are ranked using the trained models. Extensive experiments conducted on a real world CQA dataset from Stack Overflow show that our proposed two methods can both outperform the traditional query likelihood language model (QLLM) as well as the state-of-the-art Latent Dirichlet Allocation based model (LDA). Specifically, the RankingSVM-based method achieves statistical significant improvements over the SVM-based method and has gained the best performance. This paper studies the problem of question retrieval in community question answering (CQA). To bridge lexical gaps in questions, which is regarded as the biggest challenge in retrieval, state-of-the-art methods learn translation models using answers under an assumption that they are parallel texts. In practice, however, questions and answers are far from "parallel". Indeed, they are heterogeneous for both the literal level and user behaviors. There are a particularly large number of low quality answers, to which the performance of translation models is vulnerable. To address these problems, we propose a supervised question-answer topic modeling approach. The approach assumes that questions and answers share some common latent topics and are generated in a "question language" and "answer language" respectively following the topics. The topics also determine an answer quality signal. Compared with translation models, our approach not only comprehensively models user behaviors on CQA portals, but also highlights the instinctive heterogeneity of questions and answers. More importantly, it takes answer quality into account and performs robustly against noise in answers. With the topic modeling approach, we propose a topic-based language model, which matches questions not only on a term level but also on a topic level. We conducted experiments on large scale data from Yahoo! Answers and Baidu Knows. Experimental results show that the proposed model can significantly outperform state-of-the-art retrieval models in CQA.