Phrase-Based Statistical Language Generation Using Graphical Models and Active Learning

By François Mairesse, Milica Gasic, Filip Jurcícek, Simon Keizer, Blaise Thomson, Kai Yu, Susannah Young. 2010

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In Natural Language Processing

Citations in the corpus

Semantic Noise Matters for Neural Natural Language Generation (2019, background): “However, NNLG requires large volumes of in-domain data, which is typically crowdsourced (e.g. Mairesse et al., 2010; Novikova et al., 2016; Wen et al., 2015, 2016; Howcroft et al., 2017), introducing noise.”
Neural Generation for Czech: Data and Baselines (2019, methodology): “A prime example is the delexicalization technique used by most current generators (e.g., Oh and Rudnicky, 2000; Mairesse et al., 2010; Wen et al., 2015a,b; Juraska et al., 2018): It is generally assumed that attribute (slot) values from the input meaning representation (MR) can be replaced by…”
The E2E Dataset: New Challenges For End-to-End Generation (2017, background): “Following Perez-Beltrachini and Gardent (2017), we describe several different dimensions of our dataset and compare them to the BAGEL and SF Restaurants (SFRest) datasets, which use the same domain.”
GPTScore: Evaluate as You Desire (2023, methodology): “We consider the BAGEL and SFRES datasets for the evaluation of data to text task.”
Why We Need New Evaluation Metrics for NLG (2017, background): “• BAGEL (Mairesse et al., 2010) provides information about restaurants in Cambridge.”
Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems (2015, background): “Although active learning (Mairesse et al., 2010) was also proposed to allow learning online directly from users, the requirement for human annotated alignments limits the scalability of the system.”
A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version (2015, background): “…language generation (Langkilde and Knight, 1998; Oh and Rudnicky, 2000; Walker et al., 2002; Ratnaparkhi, 2002; Stent et al., 2004; Rieser and Lemon, 2010; Mairesse et al., 2010; Mairesse and Young, 2014; Wen et al., 2015; Sharma et al., 2016), and dialogue policy learning (Young et al., 2013).”
Evaluating the State-of-the-Art of End-to-End Natural Language Generation: The E2E NLG Challenge (2019, methodology): “In the remainder of this section, we first describe the detailed setup used to crowdsource our data (Section 3.3) and then finally evaluate the pre-study by comparing pictorial MRs to text-based MRs used by previous crowdsourcing work (Mairesse et al. 2010; Wang et al. 2012) in Section 3.4.”
Sequence-to-Sequence Generation for Spoken Dialogue via Deep Syntax Trees and Strings (2016, methodology): “We perform our experiments on the BAGEL data set of Mairesse et al. (2010), which contains 202 DA from the restaurant information domain with two natural language paraphrases each, de-”
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation (2017, methodology): “For example, Dušek and Jurč́ıček (2015) use the dialogue acts from the bagel dataset (Mairesse et al., 2010) as meaning representations; the bagel reference texts are parsed using an off-the-shelf deep syntactic analyser.”
Findings of the E2E NLG Challenge (2018, methodology): “1 So far, end-to-end approaches to NLG are limited to small, delexicalised datasets, e.g. BAGEL (Mairesse et al., 2010), SF Hotels/Restau-rants (Wen et al., 2015b), or RoboCup (Chen and Mooney, 2008), whereas the E2E shared task is based on a new crowdsourced dataset of 50k instances in the restaura”
Crowd-sourcing NLG Data: Pictures Elicit Better Data. (2016, methodology): “(Mairesse et al., 2010) demonstrate that crowd workers can produce NL descriptions from abstract MRs, a method which also has shown success in related NLP tasks, such as Spoken Dialogue Systems (Wang et al.”
Training a Natural Language Generator From Unaligned Data (2015, methodology): “We performed our experiments on the BAGEL data set of Mairesse et al. (2010), which ﬁts our usage scenario in a spoken dialogue sys-tem and is freely available.”
BARTScore: Evaluating Generated Text as Text Generation (2021, neutral): “(1) BAGEL [45] provides information about restaurants.”
TNT-NLG , System 2 : Data Repetition and Meaning Representation Manipulation to Improve Neural Generation (2018, background): “E2E NLG datasets in the past were small and delexicalised, e.g BAGEL or RoboCup (Mairesse et al., 2010; Kitano et al., 1998).”
Neural personalized response generation as domain adaptation (2017, background): “[22] proposed a statistical language generator which used a dynamic Bayesian networks to generate responses in dialogue.”