Fine-tuning Multi-hop Question Answering with Hierarchical Graph Network

Abstract

In this paper, we present a two stage model for multi-hop question answering. The first stage is a hierarchical graph network, which is used to reason over multi-hop question and is capable to capture different levels of granularity using the nature structure(i.e., paragraphs, questions, sentences and entities) of documents. The reasoning process is convert to node classify task(i.e., paragraph nodes and sentences nodes). The second stage is a language model fine-tuning task. In a word, stage one use graph neural network to select and concatenate support sentences as one paragraph, and stage two find the answer span in language model fine-tuning paradigm.

Incomplete Work

References in corpus (11)

• Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
• XLNet: Generalized Autoregressive Pretraining for Language Understanding
• ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
• SQuAD: 100,000+ Questions for Machine Comprehension of Text
• Know What You Don't Know: Unanswerable Questions for SQuAD
• HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
• Exploring Graph-structured Passage Representation for Multi-hop Reading Comprehension with Graph Neural Networks
• Coarse-grain Fine-grain Coattention Network for Multi-evidence Question Answering
• Hierarchical Graph Network for Multi-hop Question Answering
• Cognitive Graph for Multi-Hop Reading Comprehension at Scale
• Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs