Addressing Issues of Cross-Linguality in Open-Retrieval Question Answering Systems For Emergent Domains (2023)

Abstract: Open-retrieval question answering systems are generally trained and tested on large datasets in well-established domains. However, low-resource settings such as new and emerging domains would especially benefit from reliable question answering systems. Furthermore, multilingual and cross-lingual resources in emergent domains are scarce, leading to few or no such systems. In this paper, we demonstrate a cross-lingual open-retrieval question answering system for the emergent domain of COVID-19.Our system adopts a corpus of scientific articles to ensure that retrieved documents are reliable. To address the scarcity of cross-lingual training data in emergent domains, we present a method utilizing automatic translation, alignment, and filtering to produce English-to-all datasets. We show that a deep semantic retriever greatly benefits from training on our English-to-all data and significantly outperforms a BM25 baseline in the cross-lingual setting. We illustrate the capabilities of our system with examples and release all code necessary to train and deploy such a system.

## introduction one challenge of emergent domains is that the originating locality is unknown, leading to the need for reliable information to cross language barriers. however, it is unlikely that domain-specific information will be available across multiple languages for a new domain. furthermore, information rapidly changes in emerging domains, compounding the challenge of accessing credible data. an example of a prominent emergent domain is covid-19, which has quickly spread across the globe. to combat the spread of misinformation about covid-19, researchers have developed open-retrieval question answering @xcite systems which use large collections of trusted documents. for example, @xcite , @xcite , and @xcite all develop open-retrieval qa systems using large corpuses of scientific journal articles. however, because these systems focus on english, they leave a gap for implementation on emergent domains that do not originate in english-speaking locations. to address the limitations of prior systems, we implement a cross-lingual open-retrieval question answering system that retrieves answers from a large collection of multilingual documents, where answers may be in a language different from the question @xcite . in this work we take covid-19 as an exemplar of an emergent domain and present our system, which addresses two main areas of importance: this system demonstration provides in-depth technical descriptions of the individual components of our cross-lingual open-retrieval question answering system: cross-lingual retrieval and crosslingual reading comprehension modules. then, we describe how to combine the components along with document re-ranking into the complete system, shown in figure 1 , and present several examples taken from our system. ## cross-lingual dense retrieval training a dense retriever is challenging in lowresource settings, such as emergent domains, due to the data-hungry nature of large language models. this challenge is compounded in the cross-lingual setting, where we aim to train a model to encode concepts from multiple languages into a similar location in the embedding space. in this section, we discuss how we overcome these challenges. ## cross-lingual open-retrieval question answering our system is composed of the retrieval and reading comprehension modules described in sections 2 and 3. the full end-to-end system is shown in figure 1 . after the retriever has been trained, the mcord-19 corpus is encoded and stored in the dense multilingual corpus index. when a ques-tion is posed to the system, the query is encoded, and a maximum inner product search is performed over the index to find documents most similar to the query. answers are then extracted from the retrieved documents and the documents are re-ranked based on answer confidence from the span extraction model. finally, the answer spans and full documents are translated into english and presented to the user with highlighted answers. ## demo the demonstration retrieves documents from our mcord-19 corpus, which has been encoded by the deep semantic retriever from section 2.3. we provide examples from the demo in figures 4 , 5 , and 6. ## conclusion in this work, we tackled two challenging areas in open-retrieval qa: cross-linguality and data scarcity. we presented methods for generating cross-lingual data in an emergent domain, covid-19. then, we demonstrated that an open-retrieval qa system trained on our data significantly outperforms a bm25 baseline. we hope that the methods presented here allow for increased access to reliable information in future emergent domains.