Multi-Task Learning with Multi-View Attention for Answer Selection and Knowledge Base Question Answering

Deng, Yang; Xie, Yuexiang; Li, Yaliang; Yang, Min; Du, Nan; Wang, Fan; Lei, Kai; Shen, Ying

doi:10.1609/aaai.v33i01.33016318

Cited by 53 publications

(51 citation statements)

References 7 publications

(15 reference statements)

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“…For example, in some QA cases, the answer type may play a key role in determining the final answer. So the (8) corresponding weight e t will be larger than other weights. The final score of each answer is summed up by scores from different answer aspects in the question-towards-answer attention.…”

Section: Question-towards-answer Attentionmentioning

confidence: 98%

“…Recently, some combined or fused methods are proposed, and at the same time, some domain features are investigated in the models. Deng et al [8] utilized a multitask learning framework to solve answer selection and relation detection simultaneously. Qu et al [22] proposed a similarity matrix-based CNN model to improve the detection performance.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Leveraging Domain Context for Question Answering Over Knowledge Graph

Tong¹,

Zhang²,

Yao³

2019

Data Sci. Eng.

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With the growing availability of different knowledge graphs in a variety of domains, question answering over knowledge graph (KG-QA) becomes a prevalent information retrieval approach. Current KG-QA methods usually resort to semantic parsing, search or neural matching models. However, they cannot well tackle increasingly long input questions and complex information needs. In this work, we propose a new KG-QA approach, leveraging the rich domain context in the knowledge graph. We incorporate the new approach with question and answer domain context descriptions. Specifically, for questions, we enrich them with users' subsequent input questions within a session and expand the input question representation. For the candidate answers, we equip them with surrounding context structures, i.e., meta-paths within the targeting knowledge graph. On top of these, we design a cross-attention mechanism to improve the question and answer matching performance. An experimental study on real datasets verifies these improvements. The new approach is especially beneficial for specific knowledge graphs with complex questions.

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Section: Question-towards-answer Attentionmentioning

confidence: 98%

Section: Related Workmentioning

confidence: 99%

Leveraging Domain Context for Question Answering Over Knowledge Graph

Tong¹,

Zhang²,

Yao³

2019

Data Sci. Eng.

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“…Co-Attention We apply co-attention mechanism (Deng et al 2018) to calculate interactive attention between each QA pair, which enables QA pairs to be aware of their semantic potential relationships. Specifically, We first compute attention matrix M c :…”

Section: Attention Layermentioning

confidence: 99%

Attentive User-Engaged Adversarial Neural Network for Community Question Answering

Xie

Shen

et al. 2020

AAAI

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We study the community question answering (CQA) problem that emerges with the advent of numerous community forums in the recent past. The task of finding appropriate answers to questions from informative but noisy crowdsourced answers is important yet challenging in practice. We present an Attentive User-engaged Adversarial Neural Network (AUANN), which interactively learns the context information of questions and answers, and enhances user engagement with the CQA task. A novel attentive mechanism is incorporated to model the semantic internal and external relations among questions, answers and user contexts. To handle the noise issue caused by introducing user context, we design a two-step denoise mechanism, including a coarse-grained selection process by similarity measurement, and a fine-grained selection process by applying an adversarial training module. We evaluate the proposed method on large-scale real-world datasets SemEval-2016 and SemEval-2017. Experimental results verify the benefits of incorporating user information, and show that our proposed model significantly outperforms the state-of-the-art methods.

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“…Another, equally widespread point of view, underlines the use of SSNSs as sources of scientific knowledge [21; 40; 41; 42]. Researchers passively consult documents uploaded by their colleagues [19] or actively seek the knowledge they need through the use of features like "question and answers" [40]. This attitude is often found in junior researchers or graduate students, but it is also common among more experienced users [43].…”

Section: Scientific Social Networking Sites Knowledge Sharing and Knmentioning

confidence: 99%

An Investigation on the Use by Academic Researchers of Knowledge from Scientific Social Networking Sites

Corvello¹,

Chimenti²,

Giglio³

et al. 2020

Preprint

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Scientific social networking sites like Researchgate or Academia.edu have become part of the work practice of academic researchers. These digital platforms have been designed precisely to encourage the exchange of knowledge between scholars and to help the expansion of collaborative networks among them. Even if studies on this topic have multiplied in recent years, there is a dearth of research on the actual impact of these platforms on scientific production. The goal of this study is to investigate the relation between the attitude of researchers towards scientific social networks and the use of knowledge in their scientific work. Data from users of the scientific social networking site Researchgate were collected. A total of 143 valid responses were received and structural equation modeling was applied for data analysis. Findings of this study confirmed that researchers use knowledge obtained from scientific social networking sites both incorporating it within their research products and to acquire new competences. In particular approaching the platform as a scientific community with a shared language and a shared vision was found to have a positive impact on knowledge use. To the best knowledge of the authors this is the first study investigating the actual use of knowledge from scientific social networking sites by academic researchers. Results help to understand the impact of these platforms on the work practice of a strategic sector like scientific research.

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Multi-Task Learning with Multi-View Attention for Answer Selection and Knowledge Base Question Answering

Cited by 53 publications

References 7 publications

Leveraging Domain Context for Question Answering Over Knowledge Graph

Leveraging Domain Context for Question Answering Over Knowledge Graph

Attentive User-Engaged Adversarial Neural Network for Community Question Answering

An Investigation on the Use by Academic Researchers of Knowledge from Scientific Social Networking Sites

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