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.- Anthology ID:
- D19-1428
- Volume:
- Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Editors:
- Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
- Venues:
- EMNLP | IJCNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4186–4196
- Language:
- URL:
- https://aclanthology.org/D19-1428
- DOI:
- 10.18653/v1/D19-1428
- Cite (ACL):
- Angela Fan, Claire Gardent, Chloé Braud, and Antoine Bordes. 2019. Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 4186–4196, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs (Fan et al., EMNLP-IJCNLP 2019)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/D19-1428.pdf
- Data
- ELI5, WebText, WikiSum