Coarse-grained decomposition and fine-grained interaction for multi-hop question answering

by   Xing Cao, et al.

Recent advances regarding question answering and reading comprehension have resulted in models that surpass human performance when the answer is contained in a single, continuous passage of text, requiring only single-hop reasoning. However, in actual scenarios, lots of complex queries require multi-hop reasoning. The key to the Question Answering task is semantic feature interaction between documents and questions, which is widely processed by Bi-directional Attention Flow (Bi-DAF), but Bi-DAF generally captures only the surface semantics of words in complex questions and fails to capture implied semantic feature of intermediate answers. As a result, Bi-DAF partially ignores part of the contexts related to the question and cannot extract the most important parts of multiple documents. In this paper we propose a new model architecture for multi-hop question answering, by applying two completion strategies: (1) Coarse-Grain complex question Decomposition (CGDe) strategy are introduced to decompose complex question into simple ones under the condition of without any additional annotations (2) Fine-Grained Interaction (FGIn) strategy are introduced to better represent each word in the document and extract more comprehensive and accurate sentences related to the inference path. The above two strategies are combined and tested on the SQuAD and HotpotQA datasets, and the experimental results show that our method outperforms state-of-the-art baselines.


page 1

page 2

page 3

page 4


Towards Graph-hop Retrieval and Reasoning in Complex Question Answering over Textual Database

In Textual question answering (TQA) systems, complex questions often req...

Identifying Supporting Facts for Multi-hop Question Answering with Document Graph Networks

Recent advances in reading comprehension have resulted in models that su...

Question-Aware Memory Network for Multi-hop Question Answering in Human-Robot Interaction

Knowledge graph question answering is an important technology in intelli...

Multi-hop Reading Comprehension through Question Decomposition and Rescoring

Multi-hop Reading Comprehension (RC) requires reasoning and aggregation ...

Ruminating Reader: Reasoning with Gated Multi-Hop Attention

To answer the question in machine comprehension (MC) task, the models ne...

Classifier Combination Approach for Question Classification for Bengali Question Answering System

Question classification (QC) is a prime constituent of automated questio...

BAG: Bi-directional Attention Entity Graph Convolutional Network for Multi-hop Reasoning Question Answering

Multi-hop reasoning question answering requires deep comprehension of re...

Please sign up or login with your details

Forgot password? Click here to reset