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The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
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
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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3
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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5
AggGen: Ordering and Aggregating while Generating ...
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6
Discovering Dialogue Slots with Weak Supervision ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.189 Abstract: Task-oriented dialogue systems typically require manual annotation of dialogue slots in training data, which is costly to obtain. We propose a method that eliminates this requirement: We use weak supervision from existing linguistic annotation models to identify potential slot candidates, then automatically identify domain-relevant slots by using clustering algorithms. Furthermore, we use the resulting slot annotation to train a neural-network-based tagger that is able to perform slot tagging with no human intervention. This tagger is trained solely on the outputs of our method and thus does not rely on any labeled data. Our model demonstrates state-of-the-art performance in slot tagging without labeled training data on four different dialogue domains. Moreover, we find that slot annotations discovered by our model significantly improve the performance of an end-to-end dialogue response generation model, compared to using no slot annotation ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://dx.doi.org/10.48448/a5x4-0t35
https://underline.io/lecture/25515-discovering-dialogue-slots-with-weak-supervision
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7
One Model, Many Languages: Meta-learning for Multilingual Text-to-Speech ...
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Neural Generation for Czech: Data and Baselines ...
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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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10
RankME: Reliable Human Ratings for Natural Language Generation ...
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11
Better Conversations by Modeling,Filtering,and Optimizing for Coherence and Diversity ...
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12
Findings of the E2E NLG Challenge ...
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13
Czech restaurant information dataset for NLG
Dušek, Ondřej; Jurčíček, Filip; Dvořák, Josef. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2017
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14
Khresmoi Summary Translation Test Data 2.0
Dušek, Ondřej; Hajič, Jan; Hlaváčová, Jaroslava. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2017
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15
Khresmoi Query Translation Test Data 2.0
Pecina, Pavel; Dušek, Ondřej; Hajič, Jan. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2017
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The E2E Dataset: New Challenges For End-to-End Generation ...
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The E2E Challenge Dataset ...
Novikova, Jekaterina; Dusek, Ondrej; Rieser, Verena. - : Heriot-Watt University, 2017
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CzEng 1.6: Enlarged Czech-English Parallel Corpus with Processing Tools Dockered
Bojar, Ondřej [Verfasser]; Dušek, Ondřej [Verfasser]; Kocmi, Tom [Verfasser]. - Aachen : Universitätsbibliothek der RWTH Aachen, 2016
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19
Alex Context NLG Dataset
Dušek, Ondřej; Jurčíček, Filip. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2016
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Vystadial 2016 – Czech data
Plátek, Ondřej; Dušek, Ondřej; Jurčíček, Filip. - : Charles University, Faculty of Mathematics and Physics, 2016
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