Question-answer topic model for question retrieval in community question answering

Ji, Zongcheng; Xu, Fei; Wang, Bin; He, Ben

doi:10.1145/2396761.2398669

Cited by 99 publications

(61 citation statements)

References 13 publications

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“…Instead, Duan et al (2008) proposed an approach that identifies the topic and focus from questions and compute their similarity. Ji et al (2012) and Zhang et al (2014) learned a probability distribution over the topics that generate the question/answers pairs with LDA and used it to measure similarity between questions. Recently, Da San Martino et al (2016) showed that combining tree kernels (TKs) with text similarity features can improve the results over strong baselines such as Google.…”

Section: Related Workmentioning

confidence: 99%

Effective shared representations with Multitask Learning for Community Question Answering

Bonadiman¹,

Uva²,

Moschitti³

2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 2

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An important asset of using Deep Neural Networks (DNNs) for text applications is their ability to automatically engineer features. Unfortunately, DNNs usually require a lot of training data, especially for high-level semantic tasks such as community Question Answering (cQA). In this paper, we tackle the problem of data scarcity by learning the target DNN together with two auxiliary tasks in a multitask learning setting. We exploit the strong semantic connection between selection of comments relevant to (i) new questions and (ii) forum questions. This enables a global representation for comments, new and previous questions. The experiments of our model on a SemEval challenge dataset for cQA show a 20% relative improvement over standard DNNs.

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Section: Related Workmentioning

confidence: 99%

Effective shared representations with Multitask Learning for Community Question Answering

Bonadiman¹,

Uva²,

Moschitti³

2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 2

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“…Wang et al [31] computed a similarity function on the syntactic-tree representations of the questions. A di erent approach using topic modeling for question retrieval was introduced by Ji et al [16] and Zhang et al [32], who used LDA topic modeling to learn the latent semantic topics in order to retrieve similar questions. Dos Santos et al [8] used neural networks for the same purpose.…”

Section: Related Workmentioning

confidence: 99%

Cross-Language Question Re-Ranking

Martino

Romeo

Barroón-Cedeño

et al. 2017

Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

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We study how to nd relevant questions in community forums when the language of the new questions is di erent from that of the existing questions in the forum. In particular, we explore the Arabic-English language pair. We compare a kernel-based system with a feed-forward neural network in a scenario where a large parallel corpus is available for training a machine translation system, bilingual dictionaries, and cross-language word embeddings. We observe that both approaches degrade the performance of the system when working on the translated text, especially the kernel-based system, which depends heavily on a syntactic kernel. We address this issue using a cross-language tree kernel, which compares the original Arabic tree to the English trees of the related questions. We show that this kernel almost closes the performance gap with respect to the monolingual system. On the neural network side, we use the parallel corpus to train cross-language embeddings, which we then use to represent the Arabic input and the English related questions in the same space. e results also improve to close to those of the monolingual neural network. Overall, the kernel system shows a be er performance compared to the neural network in all cases.

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“…Topic models (Blei et al, 2003) have been used to retrieve topically similar questions with usage of the solution side leading to further improvements (Ji et al, 2012). They have been combined with language modeling whereby question and answer parts are modeled to have been generated from paired latent topics, but in "question and answer languages" (Zhang et al, 2014).…”

Section: Topic Model Based Techniquesmentioning

confidence: 99%

Latent Space Embedding for Retrieval in Question-Answer Archives

Deepak¹,

Garg

Shevade

2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

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Community-driven Question Answering (CQA) systems such as Yahoo! Answers have become valuable sources of reusable information. CQA retrieval enables usage of historical CQA archives to solve new questions posed by users. This task has received much recent attention, with methods building upon literature from translation models, topic models, and deep learning. In this paper, we devise a CQA retrieval technique, LASER-QA, that embeds question-answer pairs within a unified latent space preserving the local neighborhood structure of question and answer spaces. The idea is that such a space mirrors semantic similarity among questions as well as answers, thereby enabling high quality retrieval. Through an empirical analysis on various real-world QA datasets, we illustrate the improved effectiveness of LASER-QA over state-of-theart methods.

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Question-answer topic model for question retrieval in community question answering

Cited by 99 publications

References 13 publications

Effective shared representations with Multitask Learning for Community Question Answering

Effective shared representations with Multitask Learning for Community Question Answering

Cross-Language Question Re-Ranking

Latent Space Embedding for Retrieval in Question-Answer Archives

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