Cross-Lingual QA as a Stepping Stone for Monolingual Open QA in I celandic (2022)

Abstract: It can be challenging to build effective open question answering (open QA) systems for languages other than English, mainly due to a lack of labeled data for training. We present a data efficient method to bootstrap such a system for languages other than English. Our approach requires only limited QA resources in the given language, along with machine-translated data, and at least a bilingual language model. To evaluate our approach, we build such a system for the Icelandic language and evaluate performance over trivia style datasets. The corpora used for training are English in origin but machine translated into Icelandic. We train a bilingual Icelandic/English language model to embed English context and Icelandic questions following methodology introduced with DensePhrases (Lee et al., 2021). The resulting system is an open domain cross-lingual QA system between Icelandic and English. Finally, the system is adapted for Icelandic only open QA, demonstrating how it is possible to efficiently create an open QA system with limited access to curated datasets in the language of interest.

## introduction open qa systems are question-answering systems that suggest answers to questions by searching through a text corpus. such systems have improved significantly in recent years, which can, to a large extent, be attributed to transformer-based vector representations of text that are well suited for the task @xcite . the most successful systems have been trained with a focus on english using large datasets such as natural questions @xcite (>320k questions), and squad @xcite (>150k questions). in some cases, questions have been generated from text using large generative neural networks @xcite . for most languages, such large datasets do not exist, and the generative models do not perform as well as for english which constitutes the bulk of the training data. for this reason, we investigate what performance can be reached in qa for icelandic, a language with low qa resources. in that investigation, we study the question of whether english qa data can aid qa system development through the use of machine translation. in this paper, we present a method to bootstrap an open qa system for icelandic where just a few thousand labelled data entries are available. we adapt the densephrases @xcite method by applying a bilingual language model, and machine-translated data, in a cross-lingual manner to create a monolingual open qa system for icelandic, the first of its kind built exclusively for the language. an overview of the build process is shown step by step in figure 1 . open qa methods solve a common issue in information retrieval where it is not known in what document an answer lies. the simpler reading comprehension methods can be used as components in open qa systems by combining them with a retriever component. bm25 @xcite , a tf-idf variant, is an example of a commonly used retriever that ranks context based on term frequencies that are shared with the question and their overall commonality. the top documents found by the retriever can then be fed to the reading comprehension component along with the question. the reading comprehension component can, for example, be a fine-tuned variant of a neural language model such as bert @xcite . the reading comprehension component is trained to predict the start and end location of an answer span or report whether an answer is not found within the given context by training on a dataset of context and question pairs. ## discussion and future work as noted in the literature review, good results have been achieved in multilingual qa using an answer generator to generate an answer in a selected language @xcite . for a monolingual setting, our approach provides a way to create an open qa system without an answer generator as in the original densephrases approach.