Anouck Braggaar


2024

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ReproHum: #0033-03: How Reproducible Are Fluency Ratings of Generated Text? A Reproduction of August et al. 2022
Emiel van Miltenburg | Anouck Braggaar | Nadine Braun | Martijn Goudbeek | Emiel Krahmer | Chris van der Lee | Steffen Pauws | Frédéric Tomas
Proceedings of the Fourth Workshop on Human Evaluation of NLP Systems (HumEval) @ LREC-COLING 2024

In earlier work, August et al. (2022) evaluated three different Natural Language Generation systems on their ability to generate fluent, relevant, and factual scientific definitions. As part of the ReproHum project (Belz et al., 2023), we carried out a partial reproduction study of their human evaluation procedure, focusing on human fluency ratings. Following the standardised ReproHum procedure, our reproduction study follows the original study as closely as possible, with two raters providing 300 ratings each. In addition to this, we carried out a second study where we collected ratings from eight additional raters and analysed the variability of the ratings. We successfully reproduced the inferential statistics from the original study (i.e. the same hypotheses were supported), albeit with a lower inter-annotator agreement. The remainder of our paper shows significant variation between different raters, raising questions about what it really means to reproduce human evaluation studies.

2023

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How reproducible is best-worst scaling for human evaluation? A reproduction of ‘Data-to-text Generation with Macro Planning’
Emiel van Miltenburg | Anouck Braggaar | Nadine Braun | Debby Damen | Martijn Goudbeek | Chris van der Lee | Frédéric Tomas | Emiel Krahmer
Proceedings of the 3rd Workshop on Human Evaluation of NLP Systems

This paper is part of the larger ReproHum project, where different teams of researchers aim to reproduce published experiments from the NLP literature. Specifically, ReproHum focuses on the reproducibility of human evaluation studies, where participants indicate the quality of different outputs of Natural Language Generation (NLG) systems. This is necessary because without reproduction studies, we do not know how reliable earlier results are. This paper aims to reproduce the second human evaluation study of Puduppully & Lapata (2021), while another lab is attempting to do the same. This experiment uses best-worst scaling to determine the relative performance of different NLG systems. We found that the worst performing system in the original study is now in fact the best performing system across the board. This means that we cannot fully reproduce the original results. We also carry out alternative analyses of the data, and discuss how our results may be combined with the other reproduction study that is carried out in parallel with this paper.

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Missing Information, Unresponsive Authors, Experimental Flaws: The Impossibility of Assessing the Reproducibility of Previous Human Evaluations in NLP
Anya Belz | Craig Thomson | Ehud Reiter | Gavin Abercrombie | Jose M. Alonso-Moral | Mohammad Arvan | Anouck Braggaar | Mark Cieliebak | Elizabeth Clark | Kees van Deemter | Tanvi Dinkar | Ondřej Dušek | Steffen Eger | Qixiang Fang | Mingqi Gao | Albert Gatt | Dimitra Gkatzia | Javier González-Corbelle | Dirk Hovy | Manuela Hürlimann | Takumi Ito | John D. Kelleher | Filip Klubicka | Emiel Krahmer | Huiyuan Lai | Chris van der Lee | Yiru Li | Saad Mahamood | Margot Mieskes | Emiel van Miltenburg | Pablo Mosteiro | Malvina Nissim | Natalie Parde | Ondřej Plátek | Verena Rieser | Jie Ruan | Joel Tetreault | Antonio Toral | Xiaojun Wan | Leo Wanner | Lewis Watson | Diyi Yang
Proceedings of the Fourth Workshop on Insights from Negative Results in NLP

We report our efforts in identifying a set of previous human evaluations in NLP that would be suitable for a coordinated study examining what makes human evaluations in NLP more/less reproducible. We present our results and findings, which include that just 13% of papers had (i) sufficiently low barriers to reproduction, and (ii) enough obtainable information, to be considered for reproduction, and that all but one of the experiments we selected for reproduction was discovered to have flaws that made the meaningfulness of conducting a reproduction questionable. As a result, we had to change our coordinated study design from a reproduce approach to a standardise-then-reproduce-twice approach. Our overall (negative) finding that the great majority of human evaluations in NLP is not repeatable and/or not reproducible and/or too flawed to justify reproduction, paints a dire picture, but presents an opportunity for a rethink about how to design and report human evaluations in NLP.

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Breakdowns and Repairs. Detecting Patterns that Lead to Breakdowns in Customer Service Messages
Anouck Braggaar
Proceedings of the 19th Annual Meeting of the Young Reseachers' Roundtable on Spoken Dialogue Systems

Many companies use dialogue systems for their customer service, and although there has been a rise in the usage of these systems (Costello and LoDolce, 2022), many of these systems still face challenges in comprehending and properly responding to the customer (Følstadet al., 2021). In our project we aim to figure out how to develop and improve these conversational agents. Part of this project (detailed in this paper) will focus on the detection of breakdown patterns and the possible solutions (repairs) to mitigate negative results of these errors.

2022

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A reproduction study of methods for evaluating dialogue system output: Replicating Santhanam and Shaikh (2019)
Anouck Braggaar | Frédéric Tomas | Peter Blomsma | Saar Hommes | Nadine Braun | Emiel van Miltenburg | Chris van der Lee | Martijn Goudbeek | Emiel Krahmer
Proceedings of the 15th International Conference on Natural Language Generation: Generation Challenges

In this paper, we describe our reproduction ef- fort of the paper: Towards Best Experiment Design for Evaluating Dialogue System Output by Santhanam and Shaikh (2019) for the 2022 ReproGen shared task. We aim to produce the same results, using different human evaluators, and a different implementation of the automatic metrics used in the original paper. Although overall the study posed some challenges to re- produce (e.g. difficulties with reproduction of automatic metrics and statistics), in the end we did find that the results generally replicate the findings of Santhanam and Shaikh (2019) and seem to follow similar trends.

2021

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Challenges in Annotating and Parsing Spoken, Code-switched, Frisian-Dutch Data
Anouck Braggaar | Rob van der Goot
Proceedings of the Second Workshop on Domain Adaptation for NLP

While high performance have been obtained for high-resource languages, performance on low-resource languages lags behind. In this paper we focus on the parsing of the low-resource language Frisian. We use a sample of code-switched, spontaneously spoken data, which proves to be a challenging setup. We propose to train a parser specifically tailored towards the target domain, by selecting instances from multiple treebanks. Specifically, we use Latent Dirichlet Allocation (LDA), with word and character N-grams. We use a deep biaffine parser initialized with mBERT. The best single source treebank (nl_alpino) resulted in an LAS of 54.7 whereas our data selection outperformed the single best transfer treebank and led to 55.6 LAS on the test data. Additional experiments consisted of removing diacritics from our Frisian data, creating more similar training data by cropping sentences and running our best model using XLM-R. These experiments did not lead to a better performance.