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

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 largescale, 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, Ex-amQA. 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, C 3 , and +3.8% in exact match on an extractive Chinese MRC dataset, CMRC 2018, demonstrating the usefulness of the generated QAbased 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/.

End-to-End Chinese Speaker Identification

“…For example, just using the pre-trained language model to initialize 2 and 3 hurts F1 by 0.2% (2 vs. 2A) and 0.4% (3 vs. 3A), respectively. This finding, at first glance, seems to be contrary to those in some previous studies (e.g., Yu et al, 2021)) that also leverage pseudo-labeled or distantly-labeled data. This is perhaps because in CST teachers (except for 0) are trained with pseudo-labeled data constructed based on DIFFERENT sets of books, instead of relying on a FIXED set of unlabeled or distantly-labeled resources.…”