The attribute selection for GRE challenge: overview and evaluation results

By Anya Belz, Albert Gatt. 2007

52 citations recorded by the source corpus.

How researchers see this paper

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

In Evaluation & Benchmarks

Citations in the corpus

The WebNLG Challenge: Generating Text from RDF Data (2017, background): “…Language Generation (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…”
Evaluating the State-of-the-Art of End-to-End Natural Language Generation: The E2E NLG Challenge (2019, background): “Shared challenges have become an established way of pushing research boundaries in the field of Natural Language Processing, with NLG benchmarking tasks running since 2007 (Belz and Gatt, 2007).”
Findings of the E2E NLG Challenge (2018, background): “Shared tasks have become an established way of pushing research boundaries in the field of natural language processing, with NLG benchmarking tasks running since 2007 (Belz and Gatt, 2007).”
The TUNA-REG Challenge 2009: Overview and Evaluation Results (2009, methodology): “10 In previous TUNA identiﬁcation experiments (Belz and Gatt, 2007; Gatt et al., 2008), subjects had to read the description before identifying the intended referent.”
An Investigation into the Validity of Some Metrics for Automatically Evaluating Natural Language Generation Systems (2009, methodology): “…by conducting experiments in which participants were presented with generated referring expressions and asked to identify the target referent (Belz and Gatt 2007; Gatt, Belz, and Kow 2008, 2009); these were carried out in conjunction with shared-task events organized under the Generation…”
Intrinsic vs. Extrinsic Evaluation Measures for Referring Expression Generation (2008, methodology): “Both experiments used a Repeated Latin Squares design, and involved 30 participants and 2,250 individual trials (see Belz & Gatt (2007) for full details).”
The TUNA Challenge 2008: Overview and Evaluation Results (2008, methodology): “In what follows, we will use the following terminology, in keeping with their usage in Belz and Gatt (2007): a peer system is a system submitted to the shared-task challenge, while peer output is an attribute set or a description (in the form of a word string) produced by a peer system.”