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arXiv · 1910.08435

Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs

Angela Fan·Claire Gardent·Chloe Braud·Antoine Bordes
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Abstract

Query-based open-domain NLP tasks require information synthesis from long and diverse web results. Current approaches extractively select portions of web text as input to Sequence-to-Sequence models using methods such as TF-IDF ranking. We propose constructing a local graph structured knowledge base for each query, which compresses the web search information and reduces redundancy. We show that by linearizing the graph into a structured input sequence, models can encode the graph representations within a standard Sequence-to-Sequence setting. For two generative tasks with very long text input, long-form question answering and multi-document summarization, feeding graph representations as input can achieve better performance than using retrieved text portions.

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Angela Fan, Claire Gardent, Chloe Braud, Antoine Bordes. 2019-10-18. Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs. https://arxiv.org/abs/1910.08435

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