Generating Referring Expressions in Context: The GREC Task Evaluation Challenges

By Anya Belz, Eric Kow, Jette Viethen, Albert Gatt. 2010

42 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Evaluation & Benchmarks, 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. Emiel Krahmer
2. Anastasia Shimorina
3. Thiago Castro Ferreira
4. Kees van Deemter
5. Laura Perez-Beltrachini
6. Claire Gardent
7. Guanyi Chen
8. Fahime Same

In Evaluation & Benchmarks

1. Anastasia Shimorina
2. Emiel Krahmer
3. Kees van Deemter
4. Laura Perez-Beltrachini
5. Shashi Narayan
6. Albert Gatt
7. Claire Gardent
8. Sander Wubben

In Natural Language Generation

1. Kees van Deemter
2. Emiel Krahmer
3. Laura Perez-Beltrachini
4. Fahime Same
5. Guanyi Chen
6. Thiago Castro Ferreira
7. Albert Gatt
8. Claire Gardent

Citations in the corpus

• The WebNLG Challenge: Generating Text from RDF Data (2017, background): “…(NLG) challenges have focused on surface realisation (Banik et al., 2013; Belz et al., 2011), referring expression generation (Belz and Gatt, 2007; Gatt et al., 2008; Gatt et al., 2009; Belz et al., 2008; Belz et al., 2009; Belz et al., 2010) and content selection (Bouayad-Agha et al., 2013).”
• Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation (2017, methodology): “There have also been a number of shared tasks in this area, including the coco (‘Common Objects in Context’) Captioning Challenge7, organised as part of the Large-Scale Scene Understanding Challenge (lsun)8 and the Multimodal Machine Translation Task (Elliott et al., 2016).”
• Towards more variation in text generation: Developing and evaluating variation models for choice of referential form (2016, methodology): “The GREC project (Belz et al., 2010) motivated the development of many of those data-driven models.”
• Computational Generation of Referring Expressions: A Survey (2012, background): “GREC (Belz et al. 2010) focuses on the task of deciding which form a referring expression should take in a textual context, which is important for generating coherent texts such as summaries (see also Section 6).”
• Non-neural Models Matter: a Re-evaluation of Neural Referring Expression Generation Systems (2022, methodology): “The GREC Shared Task (Belz et al., 2010) triggered a plethora of ML-based models for building REG-in-context (e.g., Greenbacker and McCoy, 2009; Hendrickx et al., 2008).”