Cooperative Self-training of Machine Reading Comprehension

Anonymous

Paper Link: https://openreview.net/forum?id=oI5rz7RpJkE

Paper Type: Long paper (up to eight pages of content + unlimited references and appendices)

Abstract: Pretrained language models have significantly improved the performance of downstream language understanding tasks, including extractive question answering, by providing high-quality contextualized word embeddings. However, training question answering models still requires large amounts of annotated data for specific domains. In this work, we propose a cooperative self-training framework, RGX, for automatically generating more non-trivial question-answer pairs to improve model performance. RGX is built upon a masked answer extraction task with an interactive learning environment containing an answer entity Recognizer, a question Generator, and an answer eXtractor. Given a passage with a masked entity, the generator generates a question around the entity, and the extractor is trained to extract the masked entity with the generated question and raw texts. The framework allows the training of question generation and answering models on any text corpora without annotation. We further leverage a self-training technique to improve the performance of both question generation and answer extraction models. Experiment results show that RGX outperforms the state-of-the-art (SOTA) pretrained language models and transfer learning approaches on standard question-answering benchmarks, and yields the new SOTA performance under given model size and transfer learning settings.

Presentation Mode: This paper will be presented virtually

Virtual Presentation Timezone: UTC-4

Copyright Consent Signature (type Name Or NA If Not Transferrable): Hongyin Luo

Copyright Consent Name And Address: Massachusetts Institute of Technology. 77 Massachusetts Ave., Cambridge, MA 02139