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The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
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In: Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021) ; https://hal.archives-ouvertes.fr/hal-03466171 ; Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021), Aug 2021, Online, France. pp.96-120, ⟨10.18653/v1/2021.gem-1.10⟩ (2021)
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THEaiTRobot 1.0
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Rosa, Rudolf; Dušek, Ondřej; Kocmi, Tom. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2021. : The Švanda Theatre in Smíchov, 2021. : The Academy of Performing Arts in Prague, Theatre Faculty (DAMU), 2021
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The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics ...
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MiRANews: Dataset and Benchmarks for Multi-Resource-Assisted News Summarization ...
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One Model, Many Languages: Meta-learning for Multilingual Text-to-Speech ...
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Evaluating the State-of-the-Art of End-to-End Natural Language Generation: The E2E NLG Challenge ...
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RankME: Reliable Human Ratings for Natural Language Generation ...
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Better Conversations by Modeling,Filtering,and Optimizing for Coherence and Diversity ...
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The E2E Dataset: New Challenges For End-to-End Generation ...
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Alex Context NLG Dataset
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Dušek, Ondřej; Jurčíček, Filip. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2016
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Abstract:
A dataset intended for fully trainable natural language generation (NLG) systems in task-oriented spoken dialogue systems (SDS), covering the English public transport information domain. It includes preceding context (user utterance) along with each data instance (pair of source meaning representation and target natural language paraphrase to be generated). Taking the form of the previous user utterance into account for generating the system response allows NLG systems trained on this dataset to entrain (adapt) to the preceding utterance, i.e., reuse wording and syntactic structure. This should presumably improve the perceived naturalness of the output, and may even lead to a higher task success rate. Crowdsourcing has been used to obtain natural context user utterances as well as natural system responses to be generated.
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Keyword:
dialogue alignment; dialogue system; entrainment; natural language generation
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URL: http://hdl.handle.net/11234/1-1675
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