@Book{W18-65:2018,
  editor    = {Emiel Krahmer  and  Albert Gatt  and  Martijn Goudbeek},
  title     = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  url       = {http://www.aclweb.org/anthology/W18-65}
}

@InProceedings{marcheggiani-perezbeltrachini:2018:W18-65,
  author    = {Marcheggiani, Diego  and  Perez-Beltrachini, Laura},
  title     = {Deep Graph Convolutional Encoders for Structured Data to Text Generation},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {1--9},
  abstract  = {Most previous work on neural text generation from graph-structured data relies on standard sequence to sequence methods. These approaches linearise the input graph to be fed to a recurrent neural network. In this paper, we propose an alternative encoder based on graph convolutional networks that directly exploits the input structure. We report results on two graph to sequence datasets that empirically show the benefits of explicitly encoding the input},
  url       = {http://www.aclweb.org/anthology/W18-6501}
}

@InProceedings{wang-EtAl:2018:W18-65,
  author    = {Wang, Qingyun  and  Pan, Xiaoman  and  Huang, Lifu  and  Zhang, Boliang  and  Jiang, Zhiying  and  Ji, Heng  and  Knight, Kevin},
  title     = {Describing a Knowledge Base},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {10--21},
  abstract  = {We aim to automatically generate natural language narratives about an input structured knowledge base (KB). We build our generation framework based on a pointer network which can copy facts from the input KB, and add two attention mechanisms: (i) slot-aware attention to capture the association between a slot type and its corresponding slot value; and (ii) a new table position self-attention to capture the inter-dependencies among related slots. For evaluation, besides standard metrics including BLEU, METEOR, and ROUGE, we also propose a KB reconstruction based metric by extracting a KB from the generation output and comparing it with the input KB. We also create a new data set which includes 106,216 pairs of structured KBs and their corresponding natural language descriptions for two distinct entity types. Experiments show that our approach significantly outperforms state-of-the-art methods. The reconstructed KB achieves 68.8% - 72.6% F-score.},
  url       = {http://www.aclweb.org/anthology/W18-6502}
}

@InProceedings{deriu-cieliebak:2018:W18-65,
  author    = {Deriu, Jan Milan  and  Cieliebak, Mark},
  title     = {Syntactic Manipulation for Generating more Diverse and Interesting Texts},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {22--34},
  abstract  = {Natural Language Generation plays an important role in the domain of dialogue systems as it determines how users perceive the system. Recently, deep-learning based systems have been proposed to tackle this task, as they generalize better and require less amounts of manual effort to implement them for new domains. However, deep learning systems usually adapt a very homogeneous sounding writing style which expresses little variation. \\},
  url       = {http://www.aclweb.org/anthology/W18-6503}
}

@InProceedings{vanderlee-krahmer-wubben:2018:W18-65,
  author    = {van der Lee, Chris  and  Krahmer, Emiel  and  Wubben, Sander},
  title     = {Automated learning of templates for data-to-text generation: comparing rule-based, statistical and neural methods},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {35--45},
  abstract  = {The current study explored novel techniques and methods for trainable approaches to data-to-text generation. Neural Machine Translation was explored for the conversion from data to text as well as the addition of extra templatization steps of the data input and text output in the conversion process. Evaluation using BLEU did not find the Neural Machine Translation technique to perform any better compared to rule-based or Statistical Machine Translation, and the templatization method seemed to perform similar or sometimes worse compared to direct data-to-text conversion. However, the human evaluation metrics indicated that Neural Machine Translation yielded the highest quality output and that the templatization method was able to increase text quality in multiple situations.},
  url       = {http://www.aclweb.org/anthology/W18-6504}
}

@InProceedings{gehrmann-EtAl:2018:W18-65,
  author    = {Gehrmann, Sebastian  and  Dai, Falcon  and  Elder, Henry  and  Rush, Alexander},
  title     = {End-to-End Content and Plan Selection for Data-to-Text Generation},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {46--56},
  abstract  = {Learning to generate fluent natural language from structured data with neural networks has become an common approach for NLG. This problem can be challenging when the form of the structured data varies between examples. This paper presents a survey of several extensions to sequence-to-sequence models to account for the latent content selection process, particularly variants of copy attention and coverage decoding. We further propose a training method based on diverse ensembling to encourage the model to learn latent generation of plans during training. An empirical evaluation of these techniques shows an increase in quality of generated text across five automated metrics, as well as human evaluation.},
  url       = {http://www.aclweb.org/anthology/W18-6505}
}

@InProceedings{chen-vandeemter-lin:2018:W18-651,
  author    = {Chen, Guanyi  and  van Deemter, Kees  and  Lin, Chenghua},
  title     = {SimpleNLG-ZH: a Linguistic Realisation Engine for Mandarin},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {57--66},
  abstract  = {We introduce SimpleNLG-ZH, a realisation engine for Mandarin that follows the software design paradigm of SimpleNLG. We explain the core grammar (morphology and syntax) and the lexicon of SimpleNLG-ZH, which is very different from English and other languages for which SimpleNLG engines have been built. The system was evaluated by regenerating expressions from a body of test sentences and a corpus of human-authored expressions. Human evaluation was conducted to estimate the quality of regenerated sentences.},
  url       = {http://www.aclweb.org/anthology/W18-6506}
}

@InProceedings{cascallarfuentes-ramossoto-bugarndiz:2018:W18-65,
  author    = {Cascallar Fuentes, Andrea  and  Ramos Soto, Alejandro  and  Bugarín Diz, Alberto},
  title     = {Adapting SimpleNLG to Galician language},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {67--72},
  abstract  = {In this paper, we describe SimpleNLG-GL, an adaptation of the linguistic realisation SimpleNLG library for the Galician language. This implementation is derived from SimpleNLG-ES, the English-Spanish version of this library. It has been tested using a battery of examples which covers the most common rules for Galician.},
  url       = {http://www.aclweb.org/anthology/W18-6507}
}

@InProceedings{dejong-theune:2018:W18-65,
  author    = {de Jong, Ruud  and  Theune, Mariët},
  title     = {Going Dutch: Creating SimpleNLG-NL},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {73--78},
  abstract  = {This paper presents SimpleNLG-NL, an adaptation of the SimpleNLG surface realisation engine for the Dutch language. It describes a novel method for determining and testing the grammatical constructions to be implemented, using target sentences sampled from a treebank.},
  url       = {http://www.aclweb.org/anthology/W18-6508}
}

@InProceedings{chen-EtAl:2018:W18-65,
  author    = {Chen, Wei-Fan  and  Wachsmuth, Henning  and  Al Khatib, Khalid  and  Stein, Benno},
  title     = {Learning to Flip the Bias of News Headlines},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {79--88},
  abstract  = {This paper introduces the task of "flipping" the bias of news articles: Given an article with a political bias (left or right), generate an article with the same topic but opposite bias. To study this task, we create a corpus with bias-labeled articles from allsides.com. As a first step, we analyze the corpus and discuss intrinsic characteristics of bias. They point to the main challenges of bias flipping, which in turn lead to a specific setting in the generation process. The paper in hand scales down the general bias flipping task to focus on bias flipping for news article headlines. A manual annotation of headlines from each side reveals that headlines are self-informative in general and often convey bias. We apply an autoencoder incorporating information from an article's content to learn how to automatically flip the bias. From 200 generated headlines, 73 are classified as understandable by annotators, and 83 maintain the topic while having opposite bias. Insights from our analysis shed light on how to solve the main challenges of bias flipping.},
  url       = {http://www.aclweb.org/anthology/W18-6509}
}

@InProceedings{fikri-takamura-okumura:2018:W18-65,
  author    = {Fikri, Abdurrisyad  and  Takamura, Hiroya  and  Okumura, Manabu},
  title     = {Stylistically User-Specific Generation},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {89--98},
  abstract  = {Recent neural models for response generation show good results in terms of general responses. In real conversations, however, depending on the speaker/responder, similar utterances should require different responses. },
  url       = {http://www.aclweb.org/anthology/W18-6510}
}

@InProceedings{chiyahgarcia-EtAl:2018:W18-65,
  author    = {Chiyah Garcia, Francisco Javier  and  Robb, David A  and  Liu, Xingkun  and  Laskov, Atanas  and  Patron, Pedro  and  Hastie, Helen},
  title     = {Explainable Autonomy: A Study of Explanation Styles for Building Clear Mental Models},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {99--108},
  abstract  = {As vehicles become more autonomous, it is important to maintain a level of transparency about their behaviour and how they work. This is particularly important in remote locations where they cannot be observed. Here, we describe a natural language chat interface that enables the reasoning behind the behaviour of underwater vehicles to be queried. We do this by deriving an interpretable model of autonomy through having an expert `speak out-loud' and provide various levels of detail based on this model. We corroborate previous research that has shown that it is important to inform the user of all possible explanations (high completeness) for improving the user's general mental model of how a system works. For understanding specific behaviours, a high level of completeness is similarly important, however, we show it is better to have the multiple explanations worded in general terms (low soundness). This work has implications for designing interfaces for autonomy as well as for explainable AI and operator training.},
  url       = {http://www.aclweb.org/anthology/W18-6511}
}

@InProceedings{groves-tian-douratsos:2018:W18-65,
  author    = {Groves, Isabel  and  Tian, Ye  and  Douratsos, Ioannis},
  title     = {Treat the system like a human student: Automatic naturalness evaluation of generated text without reference texts},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {109--118},
  abstract  = {The current most popular method for automatic Natural Language Generation (NLG) evaluation is comparing generated text with human-written reference sentences using a metrics system, which has drawbacks around reliability and scalability. We draw inspiration from second language (L2) assessment and extract a set of linguistic features to predict human judgments of sentence naturalness. Our experiment using a small dataset showed that the feature-based approach yields promising results, with the added potential of providing interpretability into the source of the problems.},
  url       = {http://www.aclweb.org/anthology/W18-6512}
}

@InProceedings{loyola-EtAl:2018:W18-65,
  author    = {Loyola, Pablo  and  Marrese-Taylor, Edison  and  Balazs, Jorge  and  Matsuo, Yutaka  and  Satoh, Fumiko},
  title     = {Content Aware Source Code Change Description Generation},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {119--128},
  abstract  = {We propose to study the generation of descriptions from source code changes by integrating the messages included on code commits and the intra-code documentation inside the source in the form of docstrings. Our hypothesis is that although both types of descriptions are not directly aligned in semantic terms ---one explaining a change and the other the actual functionality of the code being modified--- there could be certain common ground that is useful for the generation. To this end, we propose an architecture that uses the source code-docstring relationship to guide the description generation. We discuss the results of the approach comparing against a baseline based on a sequence-to-sequence model, using standard automatic natural language generation metrics as well as with a human study, thus offering a comprehensive view of the feasibility of the approach.},
  url       = {http://www.aclweb.org/anthology/W18-6513}
}

@InProceedings{agarwal-EtAl:2018:W18-65,
  author    = {Agarwal, Shubham  and  Dušek, Ondřej  and  Konstas, Ioannis  and  Rieser, Verena},
  title     = {Improving Context Modelling in Multimodal Dialogue Generation},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {129--134},
  abstract  = {In this work, we investigate the task of textual},
  url       = {http://www.aclweb.org/anthology/W18-6514}
}

@InProceedings{aoki-EtAl:2018:W18-65,
  author    = {Aoki, Tatsuya  and  Miyazawa, Akira  and  Ishigaki, Tatsuya  and  Goshima, Keiichi  and  Aoki, Kasumi  and  Kobayashi, Ichiro  and  Takamura, Hiroya  and  Miyao, Yusuke},
  title     = {Generating Market Comments Referring to External Resources},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {135--139},
  abstract  = {Comments on a stock market often include the reason or the cause of the changes in the stock price "Nikkei turns lower as yen's rise hits exporters".},
  url       = {http://www.aclweb.org/anthology/W18-6515}
}

@InProceedings{belz-EtAl:2018:W18-65,
  author    = {Belz, Anja  and  Muscat, Adrian  and  Anguill, Pierre  and  Sow, Mouhamadou  and  Vincent, Gaetan  and  Zinessabah, Yassine},
  title     = {SpatialVOC2K: A Multilingual Dataset of Images with Annotations and Features for Spatial Relations between Objects},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {140--145},
  abstract  = {We present the first multilingual image},
  url       = {http://www.aclweb.org/anthology/W18-6516}
}

@InProceedings{birmingham-muscat-belz:2018:W18-65,
  author    = {Birmingham, Brandon  and  Muscat, Adrian  and  Belz, Anja},
  title     = {Adding the Third Dimension to Spatial Relation Detection in 2D Images},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {146--151},
  abstract  = {Spatial relation detection in images has become a popular subject in image description research recently. A range of different language and geometric features have been used in this context, but methods have not so far used explicit information about the third dimension (depth), except when manually added to annotations. The lack of such information hampers detection of many different spatial relations that are inherently 3D. In this paper, we use a fully automatic method for creating a depth map of an image and derive several different object-level depth features from it which we add to an existing feature set to test the effect on spatial relation detection. We show that performance increases are obtained by adding depth features in all scenarios tested.},
  url       = {http://www.aclweb.org/anthology/W18-6517}
}

@InProceedings{chali-baghaee:2018:W18-65,
  author    = {Chali, Yllias  and  Baghaee, Tina},
  title     = {Automatic Opinion Question Generation},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {152--158},
  abstract  = {We study the problem of opinion question generation from sentences with the help of community-based question answering systems. For this purpose, we use a sequence to sequence attentional model, and we adopt coverage mechanism to prevent sentences from repeating themselves. Experimental results on the Amazon question/answer dataset show an improvement in automatic evaluation metrics as well as human evaluations from the state-of-the-art question generation systems.},
  url       = {http://www.aclweb.org/anthology/W18-6518}
}

@InProceedings{chen-vandeemter-lin:2018:W18-652,
  author    = {Chen, Guanyi  and  van Deemter, Kees  and  Lin, Chenghua},
  title     = {Modelling Pro-drop with the Rational Speech Acts Model},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {159--164},
  abstract  = {We extend the classic Referring Expressions Generation task by considering zero pronouns in ``pro-drop'' languages such as Chinese, modelling their use by means of the Bayesian Rational Speech Acts model. By assuming that highly salient referents are most likely to be referred to by zero pronouns (i.e., pro-drop is more likely for salient referents than the less salient ones), the model offers an attractive explanation of a phenomenon not previously addressed probabilistically.},
  url       = {http://www.aclweb.org/anthology/W18-6519}
}

@InProceedings{choi-EtAl:2018:W18-65,
  author    = {Choi, Hyungtak  and  K.M., Siddarth  and  Yang, Haehun  and  Jeon, Heesik  and  Hwang, Inchul  and  Kim, Jihie},
  title     = {Self-Learning Architecture for Natural Language Generation},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {165--170},
  abstract  = {In this paper, we propose a self-learning architecture for generating natural language templates for conversational assistants. Generating templates to cover all the combinations of slots in an intent is time consuming and labor-intensive. We examine three different models based on our proposed architecture - Rule-based model, Sequence-to-Sequence (Seq2Seq) model and Semantically Conditioned LSTM (SC-LSTM) model for the IoT domain - to reduce the human labor required for template generation. We demonstrate the feasibility of template generation for the IoT domain using our self-learning architecture. In both automatic and human evaluation, the self-learning architecture performs better than previous works trained with a fully human-labeled dataset. This is promising for commercial conversational assistant solutions.},
  url       = {http://www.aclweb.org/anthology/W18-6520}
}

@InProceedings{castroferreira-EtAl:2018:W18-65,
  author    = {Castro Ferreira, Thiago  and  Moussallem, Diego  and  Krahmer, Emiel  and  Wubben, Sander},
  title     = {Enriching the WebNLG corpus},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {171--176},
  abstract  = {This paper describes the enrichment of WebNLG corpus, with the aim to further extend its usefulness as a resource for evaluating common NLG tasks, including Discourse Ordering, Lexicalization and Referring Expression Generation. We also produce a silver-standard German translation of the corpus to enable the exploitation of NLG approaches to other languages than English. The enriched corpus will be publicly available.},
  url       = {http://www.aclweb.org/anthology/W18-6521}
}

@InProceedings{forrest-EtAl:2018:W18-65,
  author    = {Forrest, James  and  Sripada, Somayajulu  and  Pang, Wei  and  Coghill, George},
  title     = {Towards making NLG a voice for interpretable Machine Learning},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {177--182},
  abstract  = {This paper presents a study to understand the issues related to using NLG to humanise explanations from a popular interpretable machine learning framework called LIME.},
  url       = {http://www.aclweb.org/anthology/W18-6522}
}

@InProceedings{gatti-vanderlee-theune:2018:W18-65,
  author    = {Gatti, Lorenzo  and  van der Lee, Chris  and  Theune, Mariët},
  title     = {Template-based multilingual football reports generation using Wikidata as a knowledge base},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {183--188},
  abstract  = {This paper presents a new version of a football reports generation system called PASS. The original version generated Dutch text and relied on a limited hand-crafted knowledge base. We describe how, in a short amount of time, we extended PASS to produce English texts, exploiting machine translation and Wikidata as a large-scale source of multilingual knowledge.},
  url       = {http://www.aclweb.org/anthology/W18-6523}
}

@InProceedings{xing-fernndez:2018:W18-65,
  author    = {Xing, Yujie  and  Fernández, Raquel},
  title     = {Automatic Evaluation of Neural Personality-based Chatbots},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {189--194},
  abstract  = {Stylistic variation is critical to render the utterances generated by conversational agents natural and engaging. In this paper, we focus on sequence-to-sequence models for open-domain dialogue response generation and propose a new method to evaluate the extent to which such models are able to generate responses that reflect different personality traits.},
  url       = {http://www.aclweb.org/anthology/W18-6524}
}

@InProceedings{hmlinen:2018:W18-65,
  author    = {Hämäläinen, Mika},
  title     = {Poem Machine - a Co-creative NLG Web Application for Poem Writing},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {195--196},
  abstract  = {We present Poem Machine, an interactive online tool for co-authoring Finnish poetry with a computationally creative agent. Poem Machine can produce poetry of its own and assist the user in authoring poems. The main target group for the system is primary school children, and its use as a part of teaching is currently under study.},
  url       = {http://www.aclweb.org/anthology/W18-6525}
}

@InProceedings{iwama-kano:2018:W18-65,
  author    = {Iwama, Kango  and  Kano, Yoshinobu},
  title     = {Japanese Advertising Slogan Generator using Case Frame and Word Vector},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {197--198},
  abstract  = {There has been many works published for automatic sentence generation of a variety of domains. However, there would be still no single method available at present that can generate sentences for all of domains. Each domain will require a suitable generation method. We focus on automatic generation of Japanese advertisement slogans in this paper. We use our advertisement slogan database, case frame information, and word vector information. We employed our system to apply for a copy competition for human copywriters, where our advertisement slogan was left as a finalist. Our system could be regarded as the world first system that generates slogans in a practical level, as an advertising agency already employs our system in their business.},
  url       = {http://www.aclweb.org/anthology/W18-6526}
}

@InProceedings{mille-EtAl:2018:W18-65,
  author    = {Mille, Simon  and  Belz, Anja  and  Bohnet, Bernd  and  Wanner, Leo},
  title     = {Underspecified Universal Dependency Structures as Inputs for Multilingual Surface Realisation},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {199--209},
  abstract  = {This paper presents the datasets used in the First Multilingual Surface Realisation Shared Task (SR'18), describes in detail how they were created, and evaluates their quality. In addition, we examine (a) the NLG subtask of surface realisation itself, (b) the motivation for, and likely usefulness of, deriving NLG inputs from annotations in resources originally developed for Natural Language Understanding (NLU), (c) whether the resulting inputs supply enough information of the right kind for the final stage in the NLG process, and more tentatively, (d) what role surface realisation is likely to play in the future in the NLG context.},
  url       = {http://www.aclweb.org/anthology/W18-6527}
}

@InProceedings{fu-white:2018:W18-65,
  author    = {Fu, Reid  and  White, Michael},
  title     = {LSTM Hypertagging},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {210--220},
  abstract  = {We implemented an LSTM hypertagger using techniques from Lewis et al. 2016, a recent paper on supertagging for parsing. We compared this new hypertagger with the existing hypertagger in OpenCCG, both in tagging accuracy and in effect on realization performance, and saw significant improvement in both. We did human evaluations to confirm that our findings were significant, and the human evaluations confirmed that they were.},
  url       = {http://www.aclweb.org/anthology/W18-6528}
}

@InProceedings{jagfeld-jenne-vu:2018:W18-65,
  author    = {Jagfeld, Glorianna  and  Jenne, Sabrina  and  Vu, Ngoc Thang},
  title     = {Sequence-to-Sequence Models for Data-to-Text Natural Language Generation: Word- vs. Character-based Processing and Output Diversity},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {221--232},
  abstract  = {We present a comparison of word-based and character-based sequence-to-sequence models for data-to-text natural language generation, which generate natural language descriptions for structured inputs. On the datasets of two recent generation challenges, our models achieve comparable or better automatic evaluation results than the best challenge submissions.},
  url       = {http://www.aclweb.org/anthology/W18-6529}
}

@InProceedings{camargodesouza-EtAl:2018:W18-65,
  author    = {Camargo de Souza, José G.  and  Kozielski, Michael  and  Mathur, Prashant  and  Chang, Ernie  and  Guerini, Marco  and  Negri, Matteo  and  Turchi, Marco  and  Matusov, Evgeny},
  title     = {Generating E-Commerce Product Titles and Predicting their Quality},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {233--243},
  abstract  = {E-commerce platforms present products using titles that summarize product information. These titles cannot be created by hand, therefore an algorithmic solution is required. The task of automatically generating these titles given noisy user provided listing titles is one way to achieve the goal. The setting requires the generation process to be fast and the generated title to be both human-readable and concise. Furthermore, we need to understand if such generated titles are usable. As such, we propose approaches that (i) automatically generate product titles, (ii) predict their quality. Our approach scales to millions of products and both automatic and human evaluations performed on real-world data indicate our approaches are effective and applicable to existing e-commerce scenarios.},
  url       = {http://www.aclweb.org/anthology/W18-6530}
}

@InProceedings{anselma-mazzei:2018:W18-65,
  author    = {Anselma, Luca  and  Mazzei, Alessandro},
  title     = {Designing and testing the messages produced by a virtual dietitian},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {244--253},
  abstract  = {This paper presents a project about the automatic generation of persuasive messages in the context of the diet management. In the first part of the paper we introduce the basic mechanisms related to data interpretation and content selection for a numerical data-to-text generation architecture. In the second part of the paper we discuss a number of factors influencing the design of the messages. In particular, we consider the design of the aggregation procedure. Finally, we present the results of a human-based evaluation concerning this design factor.},
  url       = {http://www.aclweb.org/anthology/W18-6531}
}

@InProceedings{qader-EtAl:2018:W18-65,
  author    = {Qader, Raheel  and  Jneid, Khoder  and  Portet, François  and  Labbé, Cyril},
  title     = {Generation of Company descriptions using concept-to-text and text-to-text deep models: dataset collection and systems evaluation},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {254--263},
  abstract  = {In this paper we study the performance of several state-of-the-art sequence-to-sequence models applied to generation of short company descriptions. The models are evaluated on a newly created and publicly available company dataset that has been collected from Wikipedia. The dataset consists of around 51K company descriptions that can be used for both concept-to-text and text-to-text generation tasks. Automatic metrics and human evaluation scores computed on the generated company descriptions show promising results despite the difficulty of the task as the dataset (like most available datasets) has not been originally designed for machine learning. In addition, we perform correlation analysis between automatic metrics and human evaluations and show that certain automatic metrics are more correlated to human judgments.},
  url       = {http://www.aclweb.org/anthology/W18-6532}
}

@InProceedings{parde-nielsen:2018:W18-65,
  author    = {Parde, Natalie  and  Nielsen, Rodney},
  title     = {Automatically Generating Questions about Novel Metaphors in Literature},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {264--273},
  abstract  = {The automatic generation of stimulating questions is crucial to the development of intelligent cognitive exercise applications. We developed an approach that generates appropriate Questioning the Author queries based on novel metaphors in diverse syntactic relations in literature. We show that the generated questions are comparable to human-generated questions in terms of naturalness, sensibility, and depth, and score slightly higher than human-generated questions in terms of clarity. We also show that questions generated about novel metaphors are rated as cognitively deeper than questions generated about non- or conventional metaphors, providing evidence that metaphor novelty can be leveraged to promote cognitive exercise.},
  url       = {http://www.aclweb.org/anthology/W18-6533}
}

@InProceedings{alnajjar-hmlinen:2018:W18-65,
  author    = {Alnajjar, Khalid  and  Hämäläinen, Mika},
  title     = {A Master-Apprentice Approach to Automatic Creation of Culturally Satirical Movie Titles},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {274--283},
  abstract  = {Satire has played a role in indirectly expressing critique towards an authority or a person from the times immemorial. We present an autonomously creative master-apprentice approach consisting of a genetic algorithm and an NMT model to produce humorous and culturally apt satire out of movie titles automatically. Furthermore, we evaluate the approach in terms of its creativity and its output. We provide a solid definition for creativity to maximize the objectiveness of the evaluation.},
  url       = {http://www.aclweb.org/anthology/W18-6534}
}

@InProceedings{reed-oraby-walker:2018:W18-65,
  author    = {Reed, Lena  and  Oraby, Shereen  and  Walker, Marilyn},
  title     = {Can Neural Generators for Dialogue Learn Sentence Planning and Discourse Structuring?},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {284--295},
  abstract  = {Responses in task-oriented dialogue systems often realize multiple propositions whose ultimate form depends on the use of sentence planning and discourse structuring operations. For example a recommendation may consist of an explicitly evaluative utterance e.g. "Chanpen Thai is the best option.", along with content related by the justification discourse relation, e.g. "It has great food and service.", that combines multiple propositions into a single phrase. While neural generation methods integrate sentence planning and surface realization in one end-to-end learning framework, previous work has not shown that neural generators can: (1) perform common sentence planning and discourse structuring operations; (2) make decisions as to whether to realize content in a single sentence or over multiple sentences; (3) generalize sentence planning and discourse relation operations beyond what was seen in training. We systematically create large training corpora that exhibit particular sentence planning operations and then test neural models to see what they learn. We compare models without explicit latent variables for sentence planning with ones that provide explicit supervision during training. We show that only the models with additional supervision can reproduce sentence planning and discourse operations and generalize to situations unseen in training.},
  url       = {http://www.aclweb.org/anthology/W18-6535}
}

@InProceedings{harrison-walker:2018:W18-65,
  author    = {Harrison, Vrindavan  and  Walker, Marilyn},
  title     = {Neural Generation of Diverse Questions using Answer Focus, Contextual and Linguistic Features},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {296--306},
  abstract  = {Question Generation is the task of automatically creating questions},
  url       = {http://www.aclweb.org/anthology/W18-6536}
}

@InProceedings{amidei-piwek-willis:2018:W18-65,
  author    = {Amidei, Jacopo  and  Piwek, Paul  and  Willis, Alistair},
  title     = {Evaluation methodologies in Automatic Question Generation 2013-2018},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {307--317},
  abstract  = {In the last few years Automatic Question Generation (AQG) has attracted increasing interest. In this paper we survey the evaluation methodologies used in AQG. Based on a sample of 37 papers, our research shows that the systems’ development is not accompanied by similar developments in the methodologies used for the systems’ evaluation. Indeed, in the papers we examine here, we find a wide variety of both intrinsic and extrinsic evaluation methodologies. Such diverse evaluation practices make it difficult to reliably compare the quality of different generation systems. Our study suggests that, given the rapidly increasing level of research in the area, a common framework is urgently needed to compare the performance of AQG systems and NLG systems more generally.},
  url       = {http://www.aclweb.org/anthology/W18-6537}
}

@InProceedings{syed-EtAl:2018:W18-65,
  author    = {Syed, Shahbaz  and  Völske, Michael  and  Potthast, Martin  and  Lipka, Nedim  and  Stein, Benno  and  Schütze, Hinrich},
  title     = {Task Proposal: The TL;DR Challenge},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {318--321},
  abstract  = {The TL;DR challenge fosters research in},
  url       = {http://www.aclweb.org/anthology/W18-6538}
}

@InProceedings{duek-novikova-rieser:2018:W18-65,
  author    = {Dušek, Ondřej  and  Novikova, Jekaterina  and  Rieser, Verena},
  title     = {Findings of the E2E NLG Challenge},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {322--328},
  abstract  = {This paper summarises the experimental setup and results of the first shared task on end-to-end (E2E) natural language generation (NLG) in spoken dialogue systems (SDS). Recent end-to-end generation systems are promising since they reduce the need for data annotation. However, they are currently limited to small, delexicalised datasets.},
  url       = {http://www.aclweb.org/anthology/W18-6539}
}

@InProceedings{mndez-EtAl:2018:W18-65,
  author    = {Méndez, Gonzalo  and  Hervas, Raquel  and  Gervás, Pablo  and  de la Rosa, Ricardo  and  Ruiz, Daniel},
  title     = {Adapting Descriptions of People to the Point of View of a Moving Observer},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {329--338},
  abstract  = {This paper addresses the task of generating descriptions of people for an observer that is moving within a scene. As the observer moves, the descriptions of the people around him also change. A referring expression generation algorithm adapted to this task needs to continuously monitor the changes in the field of view of the observer, his relative position to the people being described, and the relative position of these people to any landmarks around them, and to take these changes into account in the referring expressions generated. This task presents two advantages: many of the mechanisms already available for static contexts may be applied with small adaptations, and it introduces the concept of changing conditions into the task of referring expression generation. In this paper we describe the design of an algorithm that takes these aspects into account in order to create descriptions of people within a 3D virtual environment. The evaluation of this algorithm has shown that, by changing the descriptions in real time according to the observers point of view, they are able to identify the described person quickly and effectively.},
  url       = {http://www.aclweb.org/anthology/W18-6540}
}

@InProceedings{ngongangomo-EtAl:2018:W18-65,
  author    = {Ngonga Ngomo, Axel-Cyrille  and  Röder, Michael  and  Moussallem, Diego  and  Usbeck, Ricardo  and  Speck, René},
  title     = {BENGAL: An Automatic Benchmark Generator for Entity Recognition and Linking},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {339--349},
  abstract  = {The manual creation of gold standards for named entity recognition and entity linking is time- and resource-intensive. Moreover, recent works show that such gold standards contain a large proportion of mistakes in addition to being difficult to maintain. We hence present \Bengal{}, a novel automatic generation of such gold standards as a complement to manually created benchmarks. The main advantage of our benchmarks is that they can be readily generated at any time, and are cost-effective while being guaranteed to be free of annotation errors. We compare the performance of 11 tools on benchmarks in English generated by \Bengal{} with their performance on 16 benchmarks created manually. We show that our approach can be ported easily across languages by presenting results achieved on Brazilian Portuguese and Spanish. Overall, our results suggest that our automatic benchmark generation approach can create varied benchmarks that have characteristics similar to those of existing benchmarks. Our approach is open-source. Our experimental results are available at~\url{  url       = {http://www.aclweb.org/anthology/W18-65, http://www.aclweb.org/anthology/W18-6541}
}

@InProceedings{shvets-mille-wanner:2018:W18-65,
  author    = {Shvets, Alexander  and  Mille, Simon  and  Wanner, Leo},
  title     = {Sentence Packaging in Text Generation from Semantic Graphs as a Community Detection Problem},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {350--359},
  abstract  = {An increasing amount of research tackles the challenge of text generation from abstract ontological or semantic structures, which are in their very nature potentially large connected graphs. These graphs must be "packaged" into sentence-wise subgraphs. We interpret the problem of sentence packaging as a community detection problem with post optimization. Experiments on the texts of the VerbNet/FrameNet structure annotated-Penn Treebank, which have been converted into graphs by a coreference merge using Stanford CoreNLP, show a high F1-score of 0.738.},
  url       = {http://www.aclweb.org/anthology/W18-6542}
}

@InProceedings{shimorina-gardent:2018:W18-65,
  author    = {Shimorina, Anastasia  and  Gardent, Claire},
  title     = {Handling Rare Items in Data-to-Text Generation},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {360--370},
  abstract  = {Neural approaches to data-to-text generation generally handle rare iput items using either delexicalisation or the copy mechanism. We investigate the relative impact of these two approaches on two datasets (E2E and WebNLG) and using two evaluation settings. We show (i) that while copy and coverage markedly improved results compared to a setting where delexicalisation is not applied, delexicalisation usually performs better than copy and coverage; (ii) that in the more challenging evaluation setting where the number of rare items is greater, the performances of copying decreases; and (iii) that the impact of these two mechanisms varies greatly depending on how the dataset is constructed and on how it is split into dev, test and train.},
  url       = {http://www.aclweb.org/anthology/W18-6543}
}

@InProceedings{thomson-reiter-sripada:2018:W18-65,
  author    = {Thomson, Craig  and  Reiter, Ehud  and  Sripada, Somayajulu},
  title     = {Comprehension Driven Document Planning in Natural Language Generation Systems},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {371--380},
  abstract  = {This paper proposes an approach to NLG system design which focuses on generating output text which can be more easily processed by the reader. Ways in which cognitive theory might be combined with existing NLG techniques are discussed and two simple experiments in content ordering are presented.},
  url       = {http://www.aclweb.org/anthology/W18-6544}
}

@InProceedings{zhang-tan-wan:2018:W18-65,
  author    = {Zhang, Jianmin  and  Tan, Jiwei  and  Wan, Xiaojun},
  title     = {Adapting Neural Single-Document Summarization Model for Abstractive Multi-Document Summarization: A Pilot Study},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {381--390},
  abstract  = {Till now, neural abstractive summarization methods have achieved great},
  url       = {http://www.aclweb.org/anthology/W18-6545}
}

@InProceedings{howcroft-klakow-demberg:2018:W18-65,
  author    = {Howcroft, David M.  and  Klakow, Dietrich  and  Demberg, Vera},
  title     = {Toward Bayesian Synchronous Tree Substitution Grammars for Sentence Planning},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {391--396},
  abstract  = {Developing conventional natural language generation systems requires extensive attention from human experts in order to craft complex sets of sentence planning rules. We propose a Bayesian nonparametric approach to learn sentence planning rules by inducing synchronous tree substitution grammars for pairs of text plans and morphosyntactically-specified dependency trees. Our system is able to learn rules which can be used to generate novel texts after training on small datasets.},
  url       = {http://www.aclweb.org/anthology/W18-6546}
}

@InProceedings{ilinykh-zarrie-schlangen:2018:W18-65,
  author    = {Ilinykh, Nikolai  and  Zarrieß, Sina  and  Schlangen, David},
  title     = {The Task Matters: Comparing Image Captioning and Task-Based Dialogical Image Description},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {397--402},
  abstract  = {Image captioning models are typically trained on data that is collected from people who are asked to describe an image, without being given any further task context. As we argue here, this context independence is likely to cause problems for transferring to task settings in which image description is bound by task demands.},
  url       = {http://www.aclweb.org/anthology/W18-6547}
}

@InProceedings{kuptavanich-EtAl:2018:W18-65,
  author    = {Kuptavanich, Kittipitch  and  Reiter, Ehud  and  van Deemter, Kees  and  Siddharthan, Advaith},
  title     = {Generating Summaries of Sets of Consumer Products: Learning from Experiments},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {403--407},
  abstract  = {We explored the task of creating a textual summary describing a large set of objects characterised by a small number of features using an e-commerce dataset. When a set of consumer products is large and varied, it can be difficult for a consumer to understand how the products in the set differ; consequently, it can be challenging to choose the most suitable product from the set. To assist consumers, we generated high-level summaries of product sets. Two generation algorithms are presented, discussed, and evaluated with human users. Our evaluation results suggest a positive contribution to consumers' understanding of the domain.},
  url       = {http://www.aclweb.org/anthology/W18-6548}
}

@InProceedings{manome-EtAl:2018:W18-65,
  author    = {Manome, Kana  and  Yoshikawa, Masashi  and  Yanaka, Hitomi  and  Martínez-Gómez, Pascual  and  Mineshima, Koji  and  Bekki, Daisuke},
  title     = {Neural sentence generation from formal semantics},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {408--414},
  abstract  = {Sequence-to-sequence models have shown strong performance in a wide range of NLP tasks, yet their applications to sentence generation from logical representations are underdeveloped. In this paper, we present a first sequence-to-sequence model for generating sentences from logical semantic representations based on event semantics. We use a semantic parsing system based on Combinatory Categorial Grammar (CCG) to obtain data annotated with logical formulas. We augment our sequence-to-sequence model with masking for predicates to contain output sentences. We also propose a novel evaluation method for generation using Recognizing Textual Entailment (RTE): combining parsing and generation, we test whether or not the output sentence entails the original text and vice versa. The experiments showed that our model outperformed a baseline with respect to both BLEU scores and accuracies in RTE.},
  url       = {http://www.aclweb.org/anthology/W18-6549}
}

@InProceedings{vanmiltenburg-elliott-vossen:2018:W18-65,
  author    = {van Miltenburg, Emiel  and  Elliott, Desmond  and  Vossen, Piek},
  title     = {Talking about other people: an endless range of possibilities},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {415--420},
  abstract  = {Image description datasets, such as Flickr30K and MS COCO, show a high degree of variation in the ways that crowd-workers talk about the world. Although this gives us a rich and diverse collection of data to work with, it also introduces uncertainty about how the world should be described. This paper shows the extent of this uncertainty in the PEOPLE-domain. We present a taxonomy of different ways to talk about other people. This taxonomy serves as a reference point to think about how other people should be described, and can be used to classify and compute statistics about labels applied to people.},
  url       = {http://www.aclweb.org/anthology/W18-6550}
}

@InProceedings{ramossoto-EtAl:2018:W18-65,
  author    = {Ramos Soto, Alejandro  and  Reiter, Ehud  and  van Deemter, Kees  and  Alonso, Jose  and  Gatt, Albert},
  title     = {Meteorologists and Students: A resource for language grounding of geographical descriptors},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {421--425},
  abstract  = {We present a data resource which can be useful for research purposes on language grounding tasks in the context of geographical referring expression generation. The resource is composed of two data sets that encompass 25 different geographical descriptors and a set of associated graphical representations, drawn as polygons on a map by two groups of human subjects: teenage students and expert meteorologists.},
  url       = {http://www.aclweb.org/anthology/W18-6551}
}

@InProceedings{sharma-EtAl:2018:W18-65,
  author    = {Sharma, Vasu  and  Sharma, Harsh  and  Bishnu, Ankita  and  Patel, Labhesh},
  title     = {Cyclegen: Cyclic consistency based product review generator from attributes},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {426--430},
  abstract  = {In this paper we present an automatic review generator system which can generate personalized reviews based on the user identity, product identity and designated rating the user wishes to allot to the review. We combine this with a sentiment analysis system which performs the complimentary task of assigning ratings to reviews based purely on the textual content of the review. We introduce an additional loss term to ensure cyclic consistency of the sentiment rating of the generated review with the conditioning rating used to generate the review. The introduction of this new loss term constraints the generation space while forcing it to generate reviews adhering better to the requested rating. The use of `soft' generation and cyclic consistency allows us to train our model in an end to end fashion. We demonstrate the working of our model on product reviews from Amazon dataset.},
  url       = {http://www.aclweb.org/anthology/W18-6552}
}

@InProceedings{song-zhang-gildea:2018:W18-65,
  author    = {Song, Linfeng  and  Zhang, Yue  and  Gildea, Daniel},
  title     = {Neural Transition-based Syntactic Linearization},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {431--440},
  abstract  = {The task of linearization is to find a grammatical order given a set of words. Traditional models use statistical methods. Syn-},
  url       = {http://www.aclweb.org/anthology/W18-6553}
}

@InProceedings{juraska-walker:2018:W18-65,
  author    = {Juraska, Juraj  and  Walker, Marilyn},
  title     = {Characterizing Variation in Crowd-Sourced Data for Training Neural Language Generators to Produce Stylistically Varied Outputs},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {441--450},
  abstract  = {One of the biggest challenges of end-to-end language generation from meaning representations in dialogue systems is making the outputs more natural and varied. Here we take a large corpus of 50K crowd-sourced utterances in the restaurant domain and develop text analysis methods that systematically characterize types of sentences in the training data. We then automatically label the training data to allow us to conduct two kinds of experiments with a neural generator. First, we test the effect of training the system with different stylistic partitions and quantify the effect of smaller, but more stylistically controlled training data. Second, we try a method of labeling the style variants during training, and show that we can modify the style of the output to some extent using our stylistic labels. We contrast and compare these methods that can be used with any existing large corpus, showing how they vary in terms of semantic quality and stylistic control.},
  url       = {http://www.aclweb.org/anthology/W18-6554}
}

@InProceedings{agarwal-dymetman-gaussier:2018:W18-65,
  author    = {Agarwal, Shubham  and  Dymetman, Marc  and  Gaussier, Eric},
  title     = {Char2char Generation with Reranking for the E2E NLG Challenge},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {451--456},
  abstract  = {This paper describes our submission to},
  url       = {http://www.aclweb.org/anthology/W18-6555}
}

@InProceedings{elder-EtAl:2018:W18-65,
  author    = {Elder, Henry  and  Gehrmann, Sebastian  and  O'Connor, Alexander  and  Liu, Qun},
  title     = {E2E NLG Challenge Submission: Towards Controllable Generation of Diverse Natural Language},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {457--462},
  abstract  = {In natural language generation (NLG), the task is to generate utterances from a more abstract input, such as structured data. An added challenge is to generate utterances that contain an accurate representation of the input, while reflecting the fluency and variety of human-generated text. In this paper, we report experiments with NLG models that can be used in task oriented dialogue systems. We explore the use of additional input to the model to encourage diversity and control of outputs. While our submission does not rank highly using automated metrics, qualitative investigation of generated utterances suggests the use of additional information in neural network NLG systems to be a promising research direction.},
  url       = {http://www.aclweb.org/anthology/W18-6556}
}

@InProceedings{puzikov-gurevych:2018:W18-65,
  author    = {Puzikov, Yevgeniy  and  Gurevych, Iryna},
  title     = {E2E NLG Challenge: Neural Models vs. Templates},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {463--471},
  abstract  = {E2E NLG Challenge is a shared task on generating restaurant descriptions from sets of key-value pairs. This paper describes the results of our participation in the challenge. We develop a simple, yet effective neural encoder-decoder model which produces fluent restaurant descriptions and outperforms a strong baseline. We further analyze the data provided by the organizers and conclude that the task can also be approached with a template-based model developed in just a few hours.},
  url       = {http://www.aclweb.org/anthology/W18-6557}
}

@InProceedings{smiley-EtAl:2018:W18-65,
  author    = {Smiley, Charese  and  Davoodi, Elnaz  and  Song, Dezhao  and  Schilder, Frank},
  title     = {The E2E NLG Challenge: A Tale of Two Systems},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {472--477},
  abstract  = {This paper presents the two systems we entered into the 2017 E2E NLG Challenge: TemplGen, a templated-based system and SeqGen, a neural network-based system. Through the automatic evaluation, SeqGen achieved competitive results compared to the template-based approach and to other participating systems as well.},
  url       = {http://www.aclweb.org/anthology/W18-6558}
}

@InProceedings{funke-helaoui-harma:2018:W18-65,
  author    = {Funke, Isabel  and  Helaoui, Rim  and  Harma, Aki},
  title     = {Interactive health insight miner: an adaptive, semantic-based approach},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {478--479},
  abstract  = {This paper describes an ontology-based system for interactive and adaptive mining of data-driven behavioral insights.},
  url       = {http://www.aclweb.org/anthology/W18-6559}
}

@InProceedings{madsack-EtAl:2018:W18-65,
  author    = {Madsack, Andreas  and  Heininger, Johanna  and  Davaasambuu, Nyamsuren  and  Voronik, Vitaliia  and  Käufl, Michael  and  Weißgraeber, Robert},
  title     = {Multi-Language Surface Realisation as REST API based NLG Microservice},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {480--481},
  abstract  = {We present a readily available NLG API that solves the morphology component for surface realizers in 10 languages (e.g., English, German and Finnish) for any topic and is available as REST API.},
  url       = {http://www.aclweb.org/anthology/W18-6560}
}

@InProceedings{li-vandeemter-lin:2018:W18-65,
  author    = {Li, Xiao  and  van Deemter, Kees  and  Lin, Chenghua},
  title     = {Statistical NLG for Generating the Content and Form of Referring Expressions},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {482--491},
  abstract  = {This paper argues that a new generic approach to statistical NLG can be made to perform Referring Expression Generation (REG) successfully. The model does not only select attributes and values for referring to a target referent, but also performs Linguistic Realisation, generating an actual Noun Phrase. Our evaluations suggest that the attribute selection aspect of the algorithm exceeds classic REG algorithms, while the Noun Phrases generated are as similar to those in a previously developed corpus as were Noun Phrases produced by a new set of human speakers.},
  url       = {http://www.aclweb.org/anthology/W18-6561}
}

@InProceedings{gatt-EtAl:2018:W18-65,
  author    = {Gatt, Albert  and  Marín, Nicolás  and  Rivas-Gervilla, Gustavo  and  Sánchez, Daniél},
  title     = {Specificity measures and reference},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {492--502},
  abstract  = {In this paper we study empirically the validity of measures of referential success for referring expressions involving gradual properties. More specifically, we study the ability of several measures to predict the success of a user in choosing the right object, given a referring expression. Experimental results indicate that certain fuzzy measures of success are able to predict human accuracy in reference resolution. Such measures are therefore suitable for the estimation of the success or otherwise of a referring expression produced by a generation algorithm, especially in case the properties in a domain cannot be assumed to have crisp denotations.},
  url       = {http://www.aclweb.org/anthology/W18-6562}
}

@InProceedings{zarrie-schlangen:2018:W18-65,
  author    = {Zarrieß, Sina  and  Schlangen, David},
  title     = {Decoding Strategies for Neural Referring Expression Generation},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {503--512},
  abstract  = {RNN-based sequence generation is now widely used in NLP and NLG (natural language generation). Most work focusses on how to train RNNs, even though also decoding is not necessarily straightforward: previous work on neural MT found seq2seq models to radically prefer short candidates, and has proposed a number of beam search heuristics to deal with this. In this work, we assess decoding strategies for referring expression generation with neural models. Here, expression length is crucial: output should neither contain too much or too little information, in order to be pragmatically adequate. We find that most beam search heuristics developed for MT do not generalize well to referring expression generation (REG), and do not generally outperform greedy decoding. We observe that beam search heuristics for termination seem to override the model's knowledge of what a good stopping point is. Therefore, we also explore a recent approach called trainable decoding, which uses a small network to modify the RNN's hidden state for better decoding results. We find this approach to consistently outperform greedy decoding for REG.},
  url       = {http://www.aclweb.org/anthology/W18-6563}
}