Abstract
A number of researchers have recently questioned the necessity of increasingly complex neural network (NN) architectures. In particular, several recent papers have shown that simpler, properly tuned models are at least competitive across several NLP tasks. In this work, we show that this is also the case for text generation from structured and unstructured data. We consider neural table-to-text generation and neural question generation (NQG) tasks for text generation from structured and unstructured data, respectively. Table-to-text generation aims to generate a description based on a given table, and NQG is the task of generating a question from a given passage where the generated question can be answered by a certain sub-span of the passage using NN models. Experimental results demonstrate that a basic attention-based seq2seq model trained with the exponential moving average technique achieves the state of the art in both tasks. Code is available at https://github.com/h-shahidi/2birds-gen.- Anthology ID:
- 2020.acl-main.355
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3864–3870
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.355
- DOI:
- 10.18653/v1/2020.acl-main.355
- Cite (ACL):
- Hamidreza Shahidi, Ming Li, and Jimmy Lin. 2020. Two Birds, One Stone: A Simple, Unified Model for Text Generation from Structured and Unstructured Data. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3864–3870, Online. Association for Computational Linguistics.
- Cite (Informal):
- Two Birds, One Stone: A Simple, Unified Model for Text Generation from Structured and Unstructured Data (Shahidi et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2020.acl-main.355.pdf
- Code
- h-shahidi/2birds-gen
- Data
- SQuAD