Open- D omain Question- A nswering for COVID -19 and Other Emergent Domains (2021)

Abstract: Since late 2019, COVID-19 has quickly emerged as the newest biomedical domain, resulting in a surge of new information. As with other emergent domains, the discussion surrounding the topic has been rapidly changing, leading to the spread of misinformation. This has created the need for a public space for users to ask questions and receive credible, scientific answers. To fulfill this need, we turn to the task of open-domain question-answering, which we can use to efficiently find answers to free-text questions from a large set of documents. In this work, we present such a system for the emergent domain of COVID-19. Despite the small data size available, we are able to successfully train the system to retrieve answers from a large-scale corpus of published COVID-19 scientific papers. Furthermore, we incorporate effective re-ranking and question-answering techniques, such as document diversity and multiple answer spans. Our open-domain question-answering system can further act as a model for the quick development of similar systems that can be adapted and modified for other developing emergent domains.

## introduction with the rise of social media and other online sources, it is easy to access information from sites without third-party filtering @xcite . as such, it is important in today's society to create systems that can provide credible and reliable information to users. this is especially true in the context of emergent domains which, unlike more established sectors, may contain rapidly changing information. covid-19 follows this pattern, with over 100,000 related articles published in 2020 and new research findings still frequently reported @xcite . however, the vast interest and exposure surrounding this topic have consequently generated a rise in misinformation @xcite . this can lead to lower compliance with various preventative measures such as social distancing, which in turn can continue the spread of the virus @xcite . a question-answering system that allows users to ask free-text questions with answers deriving from published articles and reliable scientific sources can help mitigate this spread of misinformation and inform the public at the same time. the task of open-domain question-answering has risen in prominence in recent years @xcite @xcite . systems have evolved from keyword-based approaches @xcite to the utilization of neural networks with dense passage retrieval @xcite . furthermore, largescale datasets have been used to train and test these systems, such as general knowledge datasets @xcite and domainspecific datasets foot_0 @xcite . however, many of these systems are evaluated on these established datasets with abundant questions and clearly defined answers. in the case of an emergent domain system, this likely will not be available and the reduced data size can result in lower answer precision. in this paper, we build an open-domain questionanswering system in the emergent domain of covid-19. we aim to overcome a staple issue with emergent domain question-answering systems: lack of data. while several covid-19-related datasets have been published since the beginning of the pandemic @xcite , they are small in scale and cannot be used for training our models. we tackle the issue of data shortage by fine-tuning pre-trained biomedical language models with a small in-domain dataset. though these models are not trained on covid-19 data, they allow our system to warm start with general biomedical terminology. other covid19-related question-answering systems have been created in recent months @xcite @xcite . however, our system incorporates multiple state-of-the-art information retrieval techniques with dense retrieval and bm25 @xcite and the additional functionality of diversity re-ranking and multiple answer spans. our system is comprised of two models: the retrieval model and reading comprehension model. our system consists of several layers of document and answer re-ranking to increase both quality and diversity in our answers. the overall system can be seen in figure 1 . we additionally provide code foot_1 to create an online demo site to visualize our system and provide multiple filters for users to further refine their queries. our contributions are 1. we set a precedent for quickly creating an effective open-domain question-answering system for an emergent domain. the retrieval model consists of a dense retriever and contains further layers of re-ranking. in the following sections, we describe the data used to train our model, along with the model details and re-ranking strategies. ## reading comprehension the second stage of our system consists of a reading comprehension model that can answer the original query based on the retrieved documents. we describe the training data, model design, and document re-ranking associated with our model in the following sections. ## open-domain question answering in the previous sections, we describe the retrieval and reading comprehension models. we combine the two models for the end-to-end open-domain question-answering task. the full system overview can be seen in figure 1 . once the retriever is trained, the cord-19 corpus is encoded and stored. when a user queries the system with a question, this question is encoded using the unified retriever model and the resulting vector is used to retrieve similar documents from the dense corpus. once the top documents are retrieved, they are re-ranked with the bm25 algorithm and further clustered/reranked to introduce diversity to the results. the top remaining documents are used as input to the reading comprehension model along with the initial question. this model computes the answer span (and potentially spans) for each document. the documents are then re-ranked given the reading comprehension model's confidence score in the top answer span and the answers for each document are highlighted. ## demo we build an online demo that allows users to easily utilize our system. this website is powered through streamlit 5 . ## conclusion in this paper, we present an open-domain question answering system for the emergent domain of covid-19. our system is comprised of retrieval and reading comprehension components, with several layers of refinement to increase the quality and diversity of responses. the system allows users to quickly search covid-19-related questions and obtain a set of answers from biomedical publications. additionally, we provide a demo website that allows users to easily interact with our system and apply additional filters to further refine their search. we hope that amidst the time of a global pandemic, our system can serve as both a resource