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

Abstract

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).

Question‐driven text summarization using an extractive‐abstractive framework

“…We used Stanford CoreNLP 79 and settings provided in Reference 76 for document analysis and candidate answers selection in CAI module. We have utilized the Answer Sentence Natural Questions (ASNQ) 80 derived from the Google Natural Questions (NQ) dataset 81 for training the the CNN and multi‐head attention based answer selector component.…”

“…We have utilized CAI module introduced in Reference 76 which has six functions based on linguistic and syntactic features and patterns for reducing the document to sentences (candidate answer sentences) that could answer the given question. We designed a joint CNN and multi‐head attention neural network to analyze and assign a score to each candidate answer sentence based on its relevance to the question.…”