A Dual Attentive Neural Network Framework with Community Metadata for Answer Selection

@inproceedings{Liu2017ADA,
  title={A Dual Attentive Neural Network Framework with Community Metadata for Answer Selection},
  author={Zhiqiang Liu and Mengzhang Li and Tianyu Bai and Rui Yan and Yan Zhang},
  booktitle={NLPCC},
  year={2017}
}
Nowadays the community-based question answering (cQA) sites become popular Web service, which have accumulated millions of questions and their associated answers over time. [] Key Method The representation of questions and answers are first learned by convolutional neural networks (CNNs). Then the DANN learns interactions of questions and answers, which is guided via user network structures and semantic matching of question topics with double attention. We evaluate the performance of our method on the well…

DEEP BELIEF NETWORK BASED QUESTION ANSWERING SYSTEM USING ALTERNATE SKIP-N GRAM MODEL AND NEGATIVE SAMPLING APPROACHES

The proposed DNA approach performs the QA system over DBN by applying alternate skip-N gram and negative sampling, which improves the efficiency of relevant word-pair detection without increasing the computational complexity.

Dynamic user modeling for expert recommendation in community question answering

A deep learning based framework for expert recommendation to exploit user interest and expertise in a dynamic environment and designs user expertise network, which leverages feedback on users’ historical behavior to estimate their expertise on new question.

Enhanced User Interest and Expertise Modeling for Expert Recommendation

This work proposes a unified framework for expert recommendation to exploit user interest and expertise more precisely, and leverages Long Short-Term Memory (LSTM) to model user's short-term interest and combine it with long- term interest.

Question Answering for Technical Customer Support

This work combines question intent categories classification and semantic matching model to filter and select correct answers from a back-end knowledge base and results indicate that neural multi-perspective sentence similarity networks outperform baseline models.

References

SHOWING 1-10 OF 22 REFERENCES

Community-Based Question Answering via Asymmetric Multi-Faceted Ranking Network Learning

This paper proposes a novel asymmetric multi-faceted ranking network learning framework for community-based question answering by jointly exploiting the deep semantic relevance between question-answer pairs and the answerers' authority to the given question.

Community-Based Question Answering via Heterogeneous Social Network Learning

This paper proposes a novel framework which encodes not only the contents of question-answer(Q-A) but also the social interaction cues in the community to boost the cQA tasks and shows that leveraging the heterogeneous social information indeed achieves better performance than other state-of-the-art cZA methods.

Convolutional Neural Tensor Network Architecture for Community-Based Question Answering

This paper proposes a convolutional neural tensor network architecture to encode the sentences in semantic space and model their interactions with a tensor layer, which outperforms the other methods on two matching tasks.

Question/Answer Matching for CQA System via Combining Lexical and Sequential Information

A new architecture is proposed to more effectively model the complicated matching relations between questions and answers which utilises a similarity matrix which contains both lexical and sequential information.

Improving Question Retrieval in Community Question Answering Using World Knowledge

This work proposes a way to build a concept thesaurus based on the semantic relations extracted from the world knowledge of Wikipedia and develops a unified framework to leverage these semantic relations in order to enhance the question similarity in the concept space.

ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs

This work presents a general Attention Based Convolutional Neural Network (ABCNN) for modeling a pair of sentences and proposes three attention schemes that integrate mutual influence between sentences into CNNs; thus, the representation of each sentence takes into consideration its counterpart.

A Long Short-Term Memory Model for Answer Sentence Selection in Question Answering

The proposed method uses a stacked bidirectional Long-Short Term Memory network to sequentially read words from question and answer sentences, and then outputs their relevance scores, which outperforms previous work which requires syntactic features and external knowledge resources.

A Neural Network for Factoid Question Answering over Paragraphs

This work introduces a recursive neural network model, qanta, that can reason over question text input by modeling textual compositionality and applies it to a dataset of questions from a trivia competition called quiz bowl.

Attentional Image Retweet Modeling via Multi-Faceted Ranking Network Learning

A novel attentional multi-faceted ranking network learning framework with multi-modal neural networks is developed for the proposed heterogenous IRM network to learn the joint image tweet representations and user preference representations for prediction task.

A Convolutional Neural Network for Modelling Sentences

A convolutional architecture dubbed the Dynamic Convolutional Neural Network (DCNN) is described that is adopted for the semantic modelling of sentences and induces a feature graph over the sentence that is capable of explicitly capturing short and long-range relations.