Ruud Koolen


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On task effects in NLG corpus elicitation: a replication study using mixed effects modeling
Emiel van Miltenburg | Merel van de Kerkhof | Ruud Koolen | Martijn Goudbeek | Emiel Krahmer
Proceedings of the 12th International Conference on Natural Language Generation

Task effects in NLG corpus elicitation recently started to receive more attention, but are usually not modeled statistically. We present a controlled replication of the study by Van Miltenburg et al. (2018b), contrasting spoken with written descriptions. We collected additional written Dutch descriptions to supplement the spoken data from the DIDEC corpus, and analyzed the descriptions using mixed effects modeling to account for variation between participants and items. Our results show that the effects of modality largely disappear in a controlled setting.


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DIDEC: The Dutch Image Description and Eye-tracking Corpus
Emiel van Miltenburg | Ákos Kádár | Ruud Koolen | Emiel Krahmer
Proceedings of the 27th International Conference on Computational Linguistics

We present a corpus of spoken Dutch image descriptions, paired with two sets of eye-tracking data: Free viewing, where participants look at images without any particular purpose, and Description viewing, where we track eye movements while participants produce spoken descriptions of the images they are viewing. This paper describes the data collection procedure and the corpus itself, and provides an initial analysis of self-corrections in image descriptions. We also present two studies showing the potential of this data. Though these studies mainly serve as an example, we do find two interesting results: (1) the eye-tracking data for the description viewing task is more coherent than for the free-viewing task; (2) variation in image descriptions (also called ‘image specificity’; Jas and Parikh, 2015) is only moderately correlated across different languages. Our corpus can be used to gain a deeper understanding of the image description task, particularly how visual attention is correlated with the image description process.

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Varying image description tasks: spoken versus written descriptions
Emiel van Miltenburg | Ruud Koolen | Emiel Krahmer
Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018)

Automatic image description systems are commonly trained and evaluated on written image descriptions. At the same time, these systems are often used to provide spoken descriptions (e.g. for visually impaired users) through apps like TapTapSee or Seeing AI. This is not a problem, as long as spoken and written descriptions are very similar. However, linguistic research suggests that spoken language often differs from written language. These differences are not regular, and vary from context to context. Therefore, this paper investigates whether there are differences between written and spoken image descriptions, even if they are elicited through similar tasks. We compare descriptions produced in two languages (English and Dutch), and in both languages observe substantial differences between spoken and written descriptions. Future research should see if users prefer the spoken over the written style and, if so, aim to emulate spoken descriptions.


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Learning Preferences for Referring Expression Generation: Effects of Domain, Language and Algorithm
Ruud Koolen | Emiel Krahmer | Mariët Theune
INLG 2012 Proceedings of the Seventh International Natural Language Generation Conference


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Does Size Matter – How Much Data is Required to Train a REG Algorithm?
Mariët Theune | Ruud Koolen | Emiel Krahmer | Sander Wubben
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies


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The D-TUNA Corpus: A Dutch Dataset for the Evaluation of Referring Expression Generation Algorithms
Ruud Koolen | Emiel Krahmer
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

We present the D-TUNA corpus, which is the first semantically annotated corpus of referring expressions in Dutch. Its primary function is to evaluate and improve the performance of REG algorithms. Such algorithms are computational models that automatically generate referring expressions by computing how a specific target can be identified to an addressee by distinguishing it from a set of distractor objects. We performed a large-scale production experiment, in which participants were asked to describe furniture items and people, and provided all descriptions with semantic information regarding the target and the distractor objects. Besides being useful for evaluating REG algorithms, the corpus addresses several other research goals. Firstly, the corpus contains both written and spoken referring expressions uttered in the direction of an addressee, which enables systematic analyses of how modality (text or speech) influences the human production of referring expressions. Secondly, due to its comparability with the English TUNA corpus, our Dutch corpus can be used to explore the differences between Dutch and English speakers regarding the production of referring expressions.

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Cross-linguistic Attribute Selection for REG: Comparing Dutch and English
Mariët Theune | Ruud Koolen | Emiel Krahmer
Proceedings of the 6th International Natural Language Generation Conference