Self-Teaching Machines to Read and Comprehend with Large-Scale Multi-Subject Question-Answering Data (2021)

Abstract: Despite considerable progress, most machine reading comprehension (MRC) tasks still lack sufficient training data to fully exploit powerful deep neural network models with millions of parameters, and it is laborious, expensive, and time-consuming to create large-scale, high-quality MRC data through crowdsourcing. This paper focuses on generating more training data for MRC tasks by leveraging existing question-answering (QA) data. We first collect a large-scale multi-subject multiple-choice QA dataset for Chinese, ExamQA. We next use incomplete, yet relevant snippets returned by a web search engine as the context for each QA instance to convert it into a weakly-labeled MRC instance. To better use the weakly-labeled data to improve a target MRC task, we evaluate and compare several methods and further propose a self-teaching paradigm. Experimental results show that, upon state-of-the-art MRC baselines, we can obtain +5.1% in accuracy on a multiple-choice Chinese MRC dataset, Cˆ3, and +3.8% in exact match on an extractive Chinese MRC dataset, CMRC 2018, demonstrating the usefulness of the generated QA-based weakly-labeled data for different types of MRC tasks as well as the effectiveness of self-teaching. ExamQA will be available at https://dataset.org/examqa/ .

## introduction constructing high-quality, large-scale data remains a major challenge for machine reading comprehension (mrc) tasks, which aim to answer questions derived from a given document @xcite @xcite . and it is laborious, expensive, and timeconsuming to create large-scale mrc data through crowdsourcing, considering factors such as ensuring a high degree of difficulty for the questions and strong relevance between the designed questions and their associated documents. therefore, crowdsourced mrc datasets, especially those requiring external knowledge beyond the given text (e.g., @xcite @xcite ), are usually small-scale, making it difficult to fully exploit prevailing mrc approaches based on pre-trained language models with millions of parameters @xcite . to alleviate this problem, most previous studies utilize the data of a target mrc task @xcite @xcite or other mrc datasets of the same task type @xcite for data augmentation. in contrast, we examine the potential of using subject-area question answering data to generate additional mrc training data, motivated by the following two considerations. first, at some level, mrc and question answering (qa), which standardly requires retrieval of snippets of text from a large corpus that answer a given question @xcite @xcite , seem to be quite related, and it has been demonstrated that medium-scale mrc datasets can be employed to improve performance of qa systems on small-scale subject-area qa datasets @xcite . second, there exists an enormous amount of real-world qa data across various subjects created by subject-matter experts, which is relatively easy and cheap to acquire but seldom used to help other tasks such as mrc. as most of the existing multi-subject qa datasets are relatively small-scale, we first collect a large-scale question-answering dataset from exams (examqa) covering a wide range of subjects (e.g., sociology, education, and psychology), which contains 638k multiple-choice instances. we then present a method to convert qa instances in examqa into training instances for a target mrc task to benefit from knowledge transfer @xcite . unlike previous studies that augment each qa instance with relevant sentences retrieved from offline corpora, we rely on a standard information-seeking protocol enabled by modern search engines: users type their questions into a web search engine and read through the snippets from a variety of sources returned by the search engine to seek potential answers. imitating this protocol, we use relevant snippets retrieved by a web search engine as the context of each qa instance. we regard such an mrc instance as weakly-labeled as the context is a form of distant supervision: while it might contain the answer to the question as required for mrc, it is also likely to be noisy, incomplete, and/or irrelevant (section 3). nevertheless, we find that this method for adding context to qa instances outperforms an approach that uses information from a single source such as wikipedia as context for qa instances (section 5.7). there is also a challenge of using the largescale qa-based weakly-labeled mrc data to improve a small-scale mrc task. we implement and compare several methods that use weaklylabeled data, such as classical sequential transfer learning @xcite and a very recent teacher-student paradigm with multiple teachers trained with different subsets of weakly-labeled data @xcite to generate soft-labeled mrc data for students. furthermore, inspired by self-training @xcite that iteratively regards the student as a teacher to relabel the unlabeled data for training a new student, we propose a paradigm, called self-teaching, to iteratively train a single teacher to provide soft labels of weakly-labeled or target mrc data. we always use the ground-truth hard labels of examqa to obtain more reliable soft labels of weakly-labeled data (section 4). the "naturally" injected noise caused by context retrieval of our approach seems to help models learn better from weakly-labeled mrc data, playing a similar role as the noise (e.g., dropout and stochastic depth) that is intentionally injected into student models in previous studies (e.g., @xcite ) (section 5.7). we study the effect of our large-scale weaklylabeled mrc data on representative mrc datasets for chinese: a multiple-choice dataset, c 3 @xcite , in which most questions cannot be solved solely by matching or paraphrasing, and an extractive dataset, cmrc 2018 @xcite , in which all answers are spans in the given documents. experimental results show that soft-label paradigms such as multi-teacher and self-teaching achieve better performance than hard-label base-lines. in particular, self-teaching does not need to carefully divide data for training several teachers at one stage as multi-teacher, yet performs equally well or better than multi-teacher. based on state-ofthe-art baselines @xcite , self-teaching leads to an +5.1% in accuracy on c 3 and +3.8% in exact match on cmrc 2018 over the same baselines without using any extra training data. we also demonstrate that our qabased mrc data can be easily combined with other types of weakly-labeled mrc data in which noise is introduced by different factors (e.g., machine translation and knowledge extraction) for further gains (e.g., up to +2.5% in accuracy on c 3 ). as the proposed paradigm is language-independent and knowledge in many subjects (e.g., mathematics and physics) can also be culture-independent, we hope this work will benefit other tasks in different languages, perhaps through powerful multi-lingual language models or machine translation. the contributions of this paper are as follows. ## self-teaching paradigm we will introduce a self-teaching paradigm to better leverage large-scale weakly-labeled mrc data to improve the performance of existing supervised methods on an mrc task of interest, which is relatively small-scale. due to limited space, here we only discuss multiple-choice tasks and we leave the reformulation (e.g., soft labels and loss functions) for extractive mrc tasks in appendix a. ## conclusions we focus on using multi-subject qa instances to construct large-scale weakly-labeled mrc data to improve a target mrc task, which lacks sufficient training data. we collect a large-scale multi-subject multiple-choice qa dataset examqa and use incomplete, yet relevant snippets returned by a search engine as context of each qa instance to convert it into a weakly-labeled mrc instance. we evaluate and compare several methods and further propose self-teaching to better use these weakly-labeled mrc instances. experimental results show that we can obtain +5.1% in accuracy on a multiplechoice mrc dataset c 3 and +3.8% in exact match on an extractive mrc dataset cmrc 2018, supporting the effectiveness of self-teaching and the usefulness of qa-based augmented data for mrc.