Intelligent Question Answering in Restricted Domains Using Deep Learning and Question Pair Matching

Cai, Linqin; Min, Wei; Sitong, Zhou; Yan, Xun

doi:10.1109/access.2020.2973728

Cited by 29 publications

(17 citation statements)

References 35 publications

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“…They fine-tuned a BERT model for selfsupervised learning of language representations (ALBERT) [19] for retrieving all COVID relevant information to the query. Cai et al [20] proposed an integrated framework for answering Chinese questions in restricted domains by modeling the question pair, comparing the input question to the existing question, and then identifying the answer output.…”

Section: B Closed-domain Qamentioning

confidence: 99%

Adaptable Closed-Domain Question Answering Using Contextualized CNN-Attention Models and Question Expansion

et al. 2022

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In closed-domain Question Answering (QA), the goal is to retrieve answers to questions within a specific domain. The main challenge of closed-domain QA is to develop a model that only requires small datasets for training since large-scale corpora may not be available. One approach is a flexible QA model that can adapt to different closed domains and train on their corpora. In this paper, we present a novel versatile reading comprehension style approach for closed-domain QA (called CA-AcdQA). The approach is based on pre-trained contextualized language models, Convolutional Neural Network (CNN), and a self-attention mechanism. The model captures the relevance between the question and context sentences at different levels of granularity by exploring the dependencies between the features extracted by the CNN. Moreover, we include candidate answer identification and question expansion techniques for context reduction and rewriting ambiguous questions. The model can be tuned to different domains with a small training dataset for sentence-level QA. The approach is tested on four publicly-available closed-domain QA datasets: Tesla (person), California (region), EU-law (system), and COVID-QA (biomedical) against nine other QA approaches. Results show that the ALBERT model variant outperforms all approaches on all datasets with a significant increase in Exact Match and F1 score. Furthermore, for the Covid-19 QA in which the text is complicated and specialized, the model is improved considerably with additional biomedical training resources (an F1 increase of 15.9 over the next highest baseline).

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Section: B Closed-domain Qamentioning

confidence: 99%

Adaptable Closed-Domain Question Answering Using Contextualized CNN-Attention Models and Question Expansion

et al. 2022

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“…Chinese Intelligent question answering system in the closed domain proposed by Cai et al [1] was developed on the basis of CNN-BiLSTM, coattention, and attention mechanisms. The framework which extracts patterns in query graph from the knowledge graph proposed by Jayaswal and Dixit [2] has been used for structured queries construction in RDF question answering task.…”

Section: Related Workmentioning

confidence: 99%

Query-Based Retrieval Using Universal Sentence Encoder

Deepthi¹,

Sowjanya²

2021

RIA

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In Natural language processing, various tasks can be implemented with the features provided by word embeddings. But for obtaining embeddings for larger chunks like sentences, the efforts applied through word embeddings will not be sufficient. To resolve such issues sentence embeddings can be used. In sentence embeddings, complete sentences along with their semantic information are represented as vectors so that the machine finds it easy to understand the context. In this paper, we propose a Question Answering System (QAS) based on sentence embeddings. Our goal is to obtain the text from the provided context for a user-query by extracting the sentence in which the correct answer is present. Traditionally, infersent models have been used on SQUAD for building QAS. In recent times, Universal Sentence Encoder with USECNN and USETrans have been developed. In this paper, we have used another variant of the Universal sentence encoder, i.e. Deep averaging network in order to obtain pre-trained sentence embeddings. The results on the SQUAD-2.0 dataset indicate our approach (USE with DAN) performs well compared to Facebook’s infersent embedding.

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“…This system can determine the redundancy of questions with similar meanings on the system, and it ranks the resulting answer using "likes" and "comments" received from users. Separately, Lin-Qin et al [12] present an integrated framework for Chinese language intelligent QA in restricted domains. This model is implemented using a convolutional neural network, a bidirectional long short-term memory network, and question pair matching to perform QA processing.…”

Section: Related Workmentioning

confidence: 99%

Integrated Question-Answering System for Natural Disaster Domains Based on Social Media Messages Posted at the Time of Disaster

Kemavuthanon

Uchida

2020

Information

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Natural disasters are events that humans cannot control, and Japan has suffered from many such disasters over its long history. Many of these have caused severe damage to human lives and property. These days, numerous Japanese people have gained considerable experience preparing for disasters and are now striving to predict the effects of disasters using social network services (SNSs) to exchange information in real time. Currently, Twitter is the most popular and powerful SNS tool used for disaster response in Japan because it allows users to exchange and disseminate information quickly. However, since almost all of the Japanese-related content is also written in the Japanese language, which restricts most of its benefits to Japanese people, we feel that it is necessary to create a disaster response system that would help people who do not understand Japanese. Accordingly, this paper presents the framework of a question-answering (QA) system that was developed using a Twitter dataset containing more than nine million tweets compiled during the Osaka North Earthquake that occurred on 18 June 2018. We also studied the structure of the questions posed and developed methods for classifying them into particular categories in order to find answers from the dataset using an ontology, word similarity, keyword frequency, and natural language processing. The experimental results presented herein confirm the accuracy of the answer results generated from our proposed system.

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Intelligent Question Answering in Restricted Domains Using Deep Learning and Question Pair Matching

Cited by 29 publications

References 35 publications

Adaptable Closed-Domain Question Answering Using Contextualized CNN-Attention Models and Question Expansion

Adaptable Closed-Domain Question Answering Using Contextualized CNN-Attention Models and Question Expansion

Query-Based Retrieval Using Universal Sentence Encoder

Integrated Question-Answering System for Natural Disaster Domains Based on Social Media Messages Posted at the Time of Disaster

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