Convolutional Neural Tensor Network Architecture for Community-Based Question Answering

By Xipeng Qiu, Xuanjing Huang. 2015

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In Natural Language Processing

Citations in the corpus

A Deep Relevance Matching Model for Ad-hoc Retrieval (2016, background): “[21] proposed Convolutional Neural Tensor Network (CNTN) for community-based question answering.”
Natural Language Processing Advancements By Deep Learning: A Survey (2020, background): “Fixed length vector sentence representation for input Questions and Answers [145].”
Community-Based Question Answering via Heterogeneous Social Network Learning (2016, background): “Though the traditional hand-crafted feature (e.g., bag-of-words) has been proven successful in a range of text modeling tasks (Fang et al. 2015; Qu et al. 2009; Gabrilovich and Markovitch 2007), it is unable to capture the word sequence information which is critical for short text matching task, as…”
Community-Based Question Answering via Asymmetric Multi-Faceted Ranking Network Learning (2017, methodology): “Currently, the problem of community-based question answering has attracted considerable attention (Ji et al. 2012; Yih et al. 2014; Qiu and Huang 2015; Shen et al. 2015; Wang and Nyberg 2015; Dong et al. 2015; Fang et al. 2016).”
Improved Representation Learning for Question Answer Matching (2016, background): “Finally, the question and answer representations can be learned and then matched by certain similarity metrics (Feng et al., 2015; Yu et al., 2014; dos Santos et al., 2015; Qiu and Huang, 2015).”
Question Generation for Question Answering (2017, background): “Question Answering (or QA) is one of the core problems for AI, and consists of several typical tasks, i.e. community-based QA (Qiu and Huang, 2015), knowledge-based QA (Berant et al., 2013), text-based QA (Yu et al., 2014), and reading comprehension (Rajpurkar et al., 2016).”
When large language models meet personalization: perspectives of challenges and opportunities (2023, unknown): “while retrieval-based methods [182], [183], [184] transform the task of generating responses into a retrieval problem by searching for the most relevant response in a response”
Match-SRNN: Modeling the Recursive Matching Structure with Spatial RNN (2016, methodology): “Examples include RAE [Socher et al., 2011], DSSM [Huang et al., 2013], CDSSM [Shen et al., 2014], ARC-I [Hu et al., 2014], CNTN [Qiu and Huang, 2015], LSTM-RNN [Palangi et al., 2015], MultiGranCNN [Yin and Schütze, 2015a; Yin and Schütze, 2015b] and MV-LSTM [Wan et al., 2016].”
How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence (2020, background): “(3) Semantic matching models in sentence level, ABCNN (Yin et al., 2016), and document level, SMASH-RNN (Jiang et al., 2019).”
Modelling Interaction of Sentence Pair with Coupled-LSTMs (2016, background): “Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 1703–1712, Austin, Texas, November 1-5, 2016. c©2016 Association for Computational Linguistics”
A Study of MatchPyramid Models on Ad-hoc Retrieval (2016, methodology): “However, most proposed deep matching models were only evaluated on the natural language processing tasks such as paraphrase identiﬁcation and question answering [10, 7].”
A Deep Look into Neural Ranking Models for Information Retrieval (2019, methodology): “The well-known data sets include the Quora Dataset(7), Yahoo! Answers Dataset [25] and SemEval-2017 Task3 [64].”
Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications (2019, methodology): “Recent developments focused on modeling the interaction between question and answer pairs: Tensor layers (Qiu and Huang, 2015; Wan et al., 2016) and holographic composition (Tay et al.”
Multi-Stage Pretraining for Low-Resource Domain Adaptation (2020, methodology): “Motivated by (Qiu and Huang, 2015; Lei et al., 2016; Rücklé et al., 2019), we create an auxiliary answer selection task from this structure.”