1,023 research outputs found
Fine-tuning Multi-hop Question Answering with Hierarchical Graph Network
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.Comment: the experience result is not as good as I excep
Heterogeneous Graph Attention Network for Multi-hop Machine Reading Comprehension
Multi-hop machine reading comprehension is a challenging task in natural
language processing, which requires more reasoning ability and explainability.
Spectral models based on graph convolutional networks grant the inferring
abilities and lead to competitive results, however, part of them still face the
challenge of analyzing the reasoning in a human-understandable way. Inspired by
the concept of the Grandmother Cells in cognitive neuroscience, a spatial graph
attention framework named crname, imitating the procedure was proposed. This
model is designed to assemble the semantic features in multi-angle
representations and automatically concentrate or alleviate the information for
reasoning. The name "crname" is a metaphor for the pattern of the model: regard
the subjects of queries as the start points of clues, take the reasoning
entities as bridge points, and consider the latent candidate entities as the
grandmother cells, and the clues end up in candidate entities. The proposed
model allows us to visualize the reasoning graph and analyze the importance of
edges connecting two entities and the selectivity in the mention and candidate
nodes, which can be easier to be comprehended empirically. The official
evaluations in open-domain multi-hop reading dataset WikiHop and Drug-drug
Interactions dataset MedHop prove the validity of our approach and show the
probability of the application of the model in the molecular biology domain
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