Neural Abstractive Summarization with Structural Attention

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2020/514 ◽

2020 ◽

Author(s):

Tanya Chowdhury ◽

Sachin Kumar ◽

Tanmoy Chakraborty

Keyword(s):

Question Answering ◽

Popular Opinion ◽

Document Summarization ◽

Community Question Answering ◽

Proposed Model ◽

Abstractive Summarization

Attentional, RNN-based encoder-decoder architectures have obtained impressive performance on abstractive summarization of news articles. However, these methods fail to account for long term dependencies within the sentences of a document. This problem is exacerbated in multi-document summarization tasks such as summarizing the popular opinion in threads present in community question answering (CQA) websites such as Yahoo! Answers and Quora. These threads contain answers which often overlap or contradict each other. In this work, we present a hierarchical encoder based on structural attention to model such inter-sentence and inter-document dependencies. We set the popular pointer-generator architecture and some of the architectures derived from it as our baselines and show that they fail to generate good summaries in a multi-document setting. We further illustrate that our proposed model achieves significant improvement over the baseline in both single and multi-document summarization settings -- in the former setting, it beats the baseline by 1.31 and 7.8 ROUGE-1 points on CNN and CQA datasets, respectively; in the latter setting, the performance is further improved by 1.6 ROUGE-1 points on the CQA dataset.

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Supervised attention for answer selection in community question answering

IAES International Journal of Artificial Intelligence (IJ-AI) ◽

10.11591/ijai.v9.i2.pp203-211 ◽

2020 ◽

Vol 9 (2) ◽

pp. 203

Author(s):

Thanh Thi Ha ◽

Atsuhiro Takasu ◽

Thanh Chinh Nguyen ◽

Kiem Hieu Nguyen ◽

Van Nha Nguyen ◽

...

Keyword(s):

Language Processing ◽

Question Answering ◽

Irrelevant Information ◽

Social Question ◽

Community Question Answering ◽

Basic Model ◽

Proposed Model ◽

Questions And Answers ◽

Word Attention ◽

Better Than

Answer selection is an important task in Community Question Answering (CQA). In recent years, attention-based neural networks have been extensively studied in various natural language processing problems, including question answering. This paper explores matchLSTM for answer selection in CQA. A lexical gap in CQA is more challenging as questions and answers typical contain multiple sentences, irrelevant information, and noisy expressions. In our investigation, word-by-word attention in the original model does not work well on social question-answer pairs. We propose integrating supervised attention into matchLSTM. Specifically, we leverage lexical-semantic from external to guide the learning of attention weights for question-answer pairs. The proposed model learns more meaningful attention that allows performing better than the basic model. Our performance is among the top on SemEval datasets.

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Dual-View Variational Autoencoders for Semi-Supervised Text Matching

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/737 ◽

2019 ◽

Author(s):

Zhongbin Xie ◽

Shuai Ma

Keyword(s):

Question Answering ◽

Fundamental Problem ◽

Sentence Pair ◽

Community Question Answering ◽

Word Level ◽

Sentence Level ◽

Proposed Model ◽

Variational Autoencoder ◽

Matching Models ◽

Text Matching

Semantically matching two text sequences (usually two sentences) is a fundamental problem in NLP. Most previous methods either encode each of the two sentences into a vector representation (sentence-level embedding) or leverage word-level interaction features between the two sentences. In this study, we propose to take the sentence-level embedding features and the word-level interaction features as two distinct views of a sentence pair, and unify them with a framework of Variational Autoencoders such that the sentence pair is matched in a semi-supervised manner. The proposed model is referred to as Dual-View Variational AutoEncoder (DV-VAE), where the optimization of the variational lower bound can be interpreted as an implicit Co-Training mechanism for two matching models over distinct views. Experiments on SNLI, Quora and a Community Question Answering dataset demonstrate the superiority of our DV-VAE over several strong semi-supervised and supervised text matching models.

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Joint Learning of Answer Selection and Answer Summary Generation in Community Question Answering

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i05.6266 ◽

2020 ◽

Vol 34 (05) ◽

pp. 7651-7658 ◽

Cited By ~ 2

Author(s):

Yang Deng ◽

Wai Lam ◽

Yuexiang Xie ◽

Daoyuan Chen ◽

Yaliang Li ◽

...

Keyword(s):

Large Scale ◽

Question Answering ◽

State Of The Art ◽

Reading Difficulties ◽

Text Summarization ◽

Essential Information ◽

Joint Learning ◽

Community Question Answering ◽

Proposed Model ◽

Correlation Information

Community question answering (CQA) gains increasing popularity in both academy and industry recently. However, the redundancy and lengthiness issues of crowdsourced answers limit the performance of answer selection and lead to reading difficulties and misunderstandings for community users. To solve these problems, we tackle the tasks of answer selection and answer summary generation in CQA with a novel joint learning model. Specifically, we design a question-driven pointer-generator network, which exploits the correlation information between question-answer pairs to aid in attending the essential information when generating answer summaries. Meanwhile, we leverage the answer summaries to alleviate noise in original lengthy answers when ranking the relevancy degrees of question-answer pairs. In addition, we construct a new large-scale CQA corpus, WikiHowQA, which contains long answers for answer selection as well as reference summaries for answer summarization. The experimental results show that the joint learning method can effectively address the answer redundancy issue in CQA and achieves state-of-the-art results on both answer selection and text summarization tasks. Furthermore, the proposed model is shown to be of great transferring ability and applicability for resource-poor CQA tasks, which lack of reference answer summaries.

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Question Tagging via Graph-guided Ranking

ACM Transactions on Information Systems ◽

10.1145/3468270 ◽

2022 ◽

Vol 40 (1) ◽

pp. 1-23

Author(s):

Xiao Zhang ◽

Meng Liu ◽

Jianhua Yin ◽

Zhaochun Ren ◽

Liqiang Nie

Keyword(s):

Directed Acyclic Graph ◽

Question Answering ◽

Vital Role ◽

Portable Devices ◽

Context Modeling ◽

Community Question Answering ◽

Proposed Model ◽

Limited Experience ◽

Multi Level ◽

The One

With the increasing prevalence of portable devices and the popularity of community Question Answering (cQA) sites, users can seamlessly post and answer many questions. To effectively organize the information for precise recommendation and easy searching, these platforms require users to select topics for their raised questions. However, due to the limited experience, certain users fail to select appropriate topics for their questions. Thereby, automatic question tagging becomes an urgent and vital problem for the cQA sites, yet it is non-trivial due to the following challenges. On the one hand, vast and meaningful topics are available yet not utilized in the cQA sites; how to model and tag them to relevant questions is a highly challenging problem. On the other hand, related topics in the cQA sites may be organized into a directed acyclic graph. In light of this, how to exploit relations among topics to enhance their representations is critical. To settle these challenges, we devise a graph-guided topic ranking model to tag questions in the cQA sites appropriately. In particular, we first design a topic information fusion module to learn the topic representation by jointly considering the name and description of the topic. Afterwards, regarding the special structure of topics, we propose an information propagation module to enhance the topic representation. As the comprehension of questions plays a vital role in question tagging, we design a multi-level context-modeling-based question encoder to obtain the enhanced question representation. Moreover, we introduce an interaction module to extract topic-aware question information and capture the interactive information between questions and topics. Finally, we utilize the interactive information to estimate the ranking scores for topics. Extensive experiments on three Chinese cQA datasets have demonstrated that our proposed model outperforms several state-of-the-art competitors.

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User Embedding for Expert Finding in Community Question Answering

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3441302 ◽

2021 ◽

Vol 15 (4) ◽

pp. 1-16

Author(s):

Negin Ghasemi ◽

Ramin Fatourechi ◽

Saeedeh Momtazi

Keyword(s):

Question Answering ◽

Community Relations ◽

Expert Finding ◽

Community Question Answering ◽

Proposed Model

The number of users who have the appropriate knowledge to answer asked questions in community question answering is lower than those who ask questions. Therefore, finding expert users who can answer the questions is very crucial and useful. In this article, we propose a framework to find experts for given questions and assign them the related questions. The proposed model benefits from users’ relations in a community along with the lexical and semantic similarities between new question and existing answers. Node embedding is applied to the community graph to find similar users. Our experiments on four different Stack Exchange datasets show that adding community relations improves the performance of expert finding models.

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A Model of Convolutional Neural Network Combined with External Knowledge to Measure the Question Similarity for Community Question Answering Systems

International Journal of Machine Learning and Computing ◽

10.18178/ijmlc.2021.11.3.1035 ◽

2021 ◽

Vol 11 (3) ◽

pp. 194-201

Author(s):

Van-Tu Nguyen ◽

◽

Anh-Cuong Le ◽

Ha-Nam Nguyen

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Question Answering ◽

Learning Models ◽

Learning Methods ◽

Data Set ◽

External Knowledge ◽

Community Question Answering ◽

Proposed Model

Automatically determining similar questions and ranking the obtained questions according to their similarities to each input question is a very important task to any community Question Answering system (cQA). Various methods have applied for this task including conventional machine learning methods with feature extraction and some recent studies using deep learning methods. This paper addresses the problem of how to combine advantages of different methods into one unified model. Moreover, deep learning models are usually only effective for large data, while training data sets in cQA problems are often small, so the idea of integrating external knowledge into deep learning models for this cQA problem becomes more important. To this objective, we propose a neural network-based model which combines a Convolutional Neural Network (CNN) with features from other methods so that the deep learning model is enhanced with addtional knowledge sources. In our proposed model, the CNN component will learn the representation of two given questions, then combined additional features through a Multilayer Perceptron (MLP) to measure similarity between the two questions. We tested our proposed model on the SemEval 2016 task-3 data set and obtain better results in comparison with previous studies on the same task.

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Hybrid Deep Neural Model for Duplicate Question Detection in Transliterated Bi-lingual Data

Recent Patents on Computer Science ◽

10.2174/2213275912666190710152709 ◽

2019 ◽

Vol 12 ◽

Author(s):

Seema Rani ◽

Avadhesh Kumar ◽

Naresh Kumar

Keyword(s):

Question Answering ◽

Neural Model ◽

Manhattan Distance ◽

Semantic Matching ◽

English Only ◽

Detection Model ◽

Proposed Model ◽

Reported Study ◽

Social Media Platforms ◽

Efficient Information

Background: Duplicate content often corrupts the filtering mechanism in online question answering. Moreover, as users are usually more comfortable conversing in their native language questions, transliteration adds to the challenges in detecting duplicate questions. This compromises with the response time and increases the answer overload. Thus, it has now become crucial to build clever, intelligent and semantic filters which semantically match linguistically disparate questions. Objective: Most of the research on duplicate question detection has been done on mono-lingual, majorly English Q&A platforms. The aim is to build a model which extends the cognitive capabilities of machines to interpret, comprehend and learn features for semantic matching in transliterated bi-lingual Hinglish (Hindi + English) data acquired from different Q&A platforms. Method: In the proposed DQDHinglish (Duplicate Question Detection) Model, firstly language transformation (transliteration & translation) is done to convert the bi-lingual transliterated question into a mono-lingual English only text. Next a hybrid of Siamese neural network containing two identical Long-term-Short-memory (LSTM) models and Multi-layer perceptron network is proposed to detect semantically similar question pairs. Manhattan distance function is used as the similarity measure. Result: A dataset was prepared by scrapping 100 question pairs from various social media platforms, such as Quora and TripAdvisor. The performance of the proposed model on the basis of accuracy and F-score. The proposed DQDHinglish achieves a validation accuracy of 82.40%. Conclusion: A deep neural model was introduced to find semantic match between English question and a Hinglish (Hindi + English) question such that similar intent questions can be combined to enable fast and efficient information processing and delivery. A dataset was created and the proposed model was evaluated on the basis of performance accuracy. To the best of our knowledge, this work is the first reported study on transliterated Hinglish semantic question matching.

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Modeling of unforced demand response programs

International Journal of Emerging Electric Power Systems ◽

10.1515/ijeeps-2020-0208 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Hassan Jalili ◽

Pierluigi Siano

Keyword(s):

Demand Response ◽

Load Shifting ◽

Response Models ◽

Temperature Changes ◽

Proposed Model ◽

Equipment Failures ◽

Demand Response Programs ◽

Simultaneous Implementation ◽

Long Term Studies

Abstract Demand response programs are useful options in reducing electricity price, congestion relief, load shifting, peak clipping, valley filling and resource adequacy from the system operator’s viewpoint. For this purpose, many models of these programs have been developed. However, the availability of these resources has not been properly modeled in demand response models making them not practical for long-term studies such as in the resource adequacy problem where considering the providers’ responding uncertainties is necessary for long-term studies. In this paper, a model considering providers’ unavailability for unforced demand response programs has been developed. Temperature changes, equipment failures, simultaneous implementation of demand side management resources, popular TV programs and family visits are the main reasons that may affect the availability of the demand response providers to fulfill their commitments. The effectiveness of the proposed model has been demonstrated by numerical simulation.

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