Handling Rare Items in Data-to-Text Generation

By Anastasia Shimorina, Claire Gardent. 2018

46 citations recorded by the source corpus.

Neural approaches to data-to-text generation generally handle rare input items using either delexicalisation or a copy mechanism. We investigate the relative impact of these two methods on two datasets (E2E and WebNLG) and using two evaluation settings. We show (i) that rare items strongly impact performance; (ii) that combining delexicalisation and copying yields the strongest improvement; (iii) that copying underperforms for rare and unseen items and (iv) that the impact of these two mechanisms greatly varies depending on how the dataset is constructed and on how it is split into train, dev and test.

Subfields: Artificial Intelligence, Natural Language Generation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Caiming Xiong
2. Percy Liang
3. Luke S. Zettlemoyer
4. Mohit Bansal
5. Richard Socher
6. Dragomir R. Radev
7. Hannaneh Hajishirzi
8. Graham Neubig

In Natural Language Generation

1. Rui Zhang
2. Amir Saffari
3. Richard Socher
4. Caiming Xiong
5. Hamza Harkous
6. Isabel Groves
7. Dragomir R. Radev

Citations in the corpus

• DART: Open-Domain Structured Data Record to Text Generation (2020, background): “2), and (3) incorporation of existing datasets including WebNLG 2017 (Gardent et al., 2017a,b; Shimorina and Gardent, 2018) and Cleaned E2E (Novikova et al.”
• Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training (2021, background): “Many systems (van der Lee et al., 2018; Castro Ferreira et al., 2019; Shimorina and Gardent, 2018) have been developed and evaluated on these”
• Sticking to the Facts: Confident Decoding for Faithful Data-to-Text Generation (2019, background): “1 with constrained split (Shimorina and Gardent, 2018) contains 16,095 data inputs in the format of RDF triples, and 42,873 data-text pairs (i.”
• Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity (2020, methodology): “ semantic accuracy as measured by the better fidelity metric. The baseline method uses heuristics to label each data-text tuple as accurate (A H) or erroneous (E H). For this, we use the heuristics by Shimorina and Gardent (2018) for WebNLG, by Juraska et al. (2018) for ViGGO, and by Dusek et al. (20”
• Large Scale Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training (2020, background): “Many systems (van der Lee et al., 2018; Castro Ferreira et al., 2019; Shimorina and Gardent, 2018) have been developed for these tasks, and evaluated on these benchmark datasets, such as CRFs (Lebret et al., 2016), latent templates for interpretability (Wiseman et al., 2018), graph trans-formers…”