Neural network based approaches to  natural language generation (NLG) have gained popularity in recent years . The goal of the task is to generate a natural language string to realize an input meaning representation , hence large datasets of paired utterances and their meaning representations are used for training the network . However, dataset creation for language generation is an arduous task , and popular datasets designed for training these generators mostly consist of simple meaning representations composed of slot and value tokens to be realized. These simple meaning representations do not include any contextual information that may be helpful for training an NLG system  to generalize, such as domain information and descriptions of slots and values. In this paper, we present the novel task of Schema-Guided Natural Language Generation , in which we repurpose an existing dataset for another task: dialog state tracking . Dialog state tracking data includes a large and rich schema spanning multiple different attributes, including information about the domain, user intent, and slot descriptions. We train different state-of-the-art models for neural natural language generation on this data and show that inclusion of the rich schema allows our models to produce higher quality outputs both in terms of semantics and diversity. We also conduct experiments comparing model performance on seen versus unseen domains . Finally, we present human evaluation results and analysis demonstrating high ratings for overall output quality.
Neural network based approaches to data-to-text natural language generation (NLG) have gained popularity in recent years , with the goal of generating a natural language prompt that accurately realizes an input meaning representation . To facilitate the training of neural network models, researchers created large datasets of paired utterances and their meaning representations . However, the creation of such datasets is an arduous task and they mostly consist of simple meaning representations composed of slot and value tokens to be realized. These representations do not include any contextual information that an NLG system can use when trying to generalize, such as domain information and descriptions of slots and values. In this paper, we present the novel task of Schema-Guided Natural Language Generation (SG-NLG). Here, the goal is still to generate a natural language prompt, but in SG-NLG, the input MRs are paired with rich schemata providing contextual information. To generate a dataset for SG-NLG we re-purpose an existing dataset for another task: dialog state tracking , which includes a large and rich schema spanning multiple different attributes, including information about the domain, user intent, and slot descriptions. We train different state-of-the-art models for neural natural language generation on this dataset and show that in many cases, including rich schema information allows our models to produce higher quality outputs both in terms of semantics and diversity. We also conduct experiments comparing model performance on seen versus unseen domains , and present a human evaluation demonstrating high ratings for overall output quality.
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data-to-text
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. The goal of the task is to generate
, with the goal of generating
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string to realize
prompt that accurately realizes
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, hence
. To facilitate the training of neural network models, researchers created
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are used for training the network
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dataset creation for language generation
the creation of such datasets
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, and popular datasets designed for training these generators
and they
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simple meaning
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may be helpful for training
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can use when trying
R
, in which we repurpose
(SG-NLG). Here, the goal is still to generate a natural language prompt, but in SG-NLG, the input MRs are paired with rich schemata providing contextual information. To generate a dataset for SG-NLG we re-purpose
R
. Dialog state tracking data
, which
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data
dataset
R
inclusion of the rich schema
in many cases, including rich schema information
R
. Finally, we present human evaluation results and analysis
, and present a human evaluation