scholarly journals Dual-View Variational Autoencoders for Semi-Supervised Text Matching

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.

scholarly journals MKPM: Multi keyword-pair matching for natural language sentences

2021 ◽  
Author(s):  
Xin Lu ◽  
Yao Deng ◽  
Ting Sun ◽  
Yi Gao ◽  
Jun Feng ◽  
...  
Keyword(s):  
Natural Language ◽  
Word Pair ◽  
Question Answering ◽  
Semantic Relationship ◽  
Sentence Pair ◽  
Word Level ◽  
Sentence Level ◽  
Pair Matching ◽  
Task Architecture ◽  
Sentence Matching

AbstractSentence matching is widely used in various natural language tasks, such as natural language inference, paraphrase identification and question answering. For these tasks, we need to understand the logical and semantic relationship between two sentences. Most current methods use all information within a sentence to build a model and hence determine its relationship to another sentence. However, the information contained in some sentences may cause redundancy or introduce noise, impeding the performance of the model. Therefore, we propose a sentence matching method based on multi keyword-pair matching (MKPM), which uses keyword pairs in two sentences to represent the semantic relationship between them, avoiding the interference of redundancy and noise. Specifically, we first propose a sentence-pair-based attention mechanism sp-attention to select the most important word pair from the two sentences as a keyword pair, and then propose a Bi-task architecture to model the semantic information of these keyword pairs. The Bi-task architecture is as follows: 1. In order to understand the semantic relationship at the word level between two sentences, we design a word-pair task (WP-Task), which uses these keyword pairs to complete sentence matching independently. 2. We design a sentence-pair task (SP-Task) to understand the sentence level semantic relationship between the two sentences by sentence denoising. Through the integration of the two tasks, our model can understand sentences more accurately from the two granularities of word and sentence. Experimental results show that our model can achieve state-of-the-art performance in several tasks. Our source code is publicly available1.

scholarly journals Supervised attention for answer selection in community question answering

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

<span class="fontstyle0">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 </span><span class="fontstyle2">matchLSTM </span><span class="fontstyle0">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 </span><span class="fontstyle2">matchLSTM</span><span class="fontstyle0">. 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.</span> <br /><br />

scholarly journals Deep Learning Based on Hierarchical Self-Attention for Finance Distress Prediction Incorporating Text

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Sumei Ruan ◽  
Xusheng Sun ◽  
Ruanxingchen Yao ◽  
Wei Li
Keyword(s):  
Financial Distress ◽  
Financial Risk ◽  
Large Scale ◽  
Early Stage ◽  
Fine Tuning ◽  
Original Text ◽  
Word Level ◽  
Sentence Level ◽  
Proposed Model ◽  
Red Flag

To detect comprehensive clues and provide more accurate forecasting in the early stage of financial distress, in addition to financial indicators, digitalization of lengthy but indispensable textual disclosure, such as Management Discussion and Analysis (MD&A), has been emphasized by researchers. However, most studies divide the long text into words and count words to treat the text as word count vectors, bringing massive invalid information but ignoring meaningful contexts. Aiming to efficiently represent the text of large size, an end-to-end neural networks model based on hierarchical self-attention is proposed in this study after the state-of-the-art pretrained model is introduced for text embedding including contexts. The proposed model has two notable characteristics. First, the hierarchical self-attention only affords the essential content with high weights in word-level and sentence-level and automatically neglects lots of information that has no business with risk prediction, which is suitable for extracting effective parts of the large-scale text. Second, after fine-tuning, the word embedding adapts the specific contexts of samples and conveys the original text expression more accurately without excessive manual operations. Experiments confirm that the addition of text improves the accuracy of financial distress forecasting and the proposed model outperforms benchmark models better at AUC and F2-score. For visualization, the elements in the weight matrix of hierarchical self-attention act as scalers to estimate the importance of each word and sentence. In this way, the “red-flag” statement that implies financial risk is figured out and highlighted in the original text, providing effective references for decision-makers.

scholarly journals Neural Abstractive Summarization with Structural Attention

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.

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

2020 ◽  
Vol 34 (05) ◽  
pp. 7651-7658 ◽  
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.

Chronological Ordering Based on Context Overlap Detection

2012 ◽  
Vol 2 (4) ◽  
pp. 31-44
Author(s):  
Mohamed H. Haggag ◽  
Bassma M. Othman
Keyword(s):  
Language Processing ◽  
Linguistic Knowledge ◽  
Syntactic Analysis ◽  
Text Generation ◽  
Context Processing ◽  
Domain Specific ◽  
Word Level ◽  
Sentence Level ◽  
Proposed Model ◽  
Chronological Sequence

Context processing plays an important role in different Natural Language Processing applications. Sentence ordering is one of critical tasks in text generation. Following the same order of sentences in the row sources of text is not necessarily to be applied for the resulted text. Accordingly, a need for chronological sentence ordering is of high importance in this regard. Some researches followed linguistic syntactic analysis and others used statistical approaches. This paper proposes a new model for sentence ordering based on sematic analysis. Word level semantics forms a seed to sentence level sematic relations. The model introduces a clustering technique based on sentences senses relatedness. Following to this, sentences are chronologically ordered through two main steps; overlap detection and chronological cause-effect rules. Overlap detection drills down into each cluster to step through its sentences in chronological sequence. Cause-effect rules forms the linguistic knowledge controlling sentences relations. Evaluation of the proposed algorithm showed the capability of the proposed model to process size free texts, non-domain specific and open to extend the cause-effect rules for specific ordering needs.

Question Tagging via Graph-guided Ranking

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.

User Embedding for Expert Finding in Community Question Answering

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.

scholarly journals Big Data Management and Analytics in Scientific Programming: A Deep Learning-Based Method for Aspect Category Classification of Question-Answering-Style Reviews

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Hanqian Wu ◽  
Mumu Liu ◽  
Shangbin Zhang ◽  
Zhike Wang ◽  
Siliang Cheng
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Question Answering ◽  
Product Information ◽  
Empirical Studies ◽  
Product Reviews ◽  
Related Information ◽  
Basic Task ◽  
Word Level ◽  
Sentence Level

Online product reviews are exploring on e-commerce platforms, and mining aspect-level product information contained in those reviews has great economic benefit. The aspect category classification task is a basic task for aspect-level sentiment analysis which has become a hot research topic in the natural language processing (NLP) field during the last decades. In various e-commerce platforms, there emerge various user-generated question-answering (QA) reviews which generally contain much aspect-related information of products. Although some researchers have devoted their efforts on the aspect category classification for traditional product reviews, the existing deep learning-based approaches cannot be well applied to represent the QA-style reviews. Thus, we propose a 4-dimension (4D) textual representation model based on QA interaction-level and hyperinteraction-level by modeling with different levels of the text representation, i.e., word-level, sentence-level, QA interaction-level, and hyperinteraction-level. In our experiments, the empirical studies on datasets from three domains demonstrate that our proposals perform better than traditional sentence-level representation approaches, especially in the Digit domain.

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