Papers by Piek Vossen

19 papers
Measuring the Diversity of Automatic Image Descriptions (C18-1)

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Challenge: a lack of diversity in automatic image description systems is a general problem in natural language generation . authors use established metrics to evaluate system performance on the head of the vocabulary . automatic image descriptions are difficult because of the unbounded range of variation in natural languages .
Approach: They propose to frame automatic image description as a word recall task to quantify the production of generic sentences as 'undiversity' they propose to use established metrics to evaluate the diversity of the output .
Outcome: The proposed metrics evaluate the diversity of sentences generated by state-of-the-art systems on a MS COCO dataset.
A Shared Task of a New, Collaborative Type to Foster Reproducibility: A First Exercise in the Area of Language Science and Technology with REPROLANG2020 (2020.lrec-1)

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Challenge: Scientific knowledge is grounded on falsifiable predictions and therefore its credibility and raison d'être rely on the possibility of repeating experiments and getting similar results as originally obtained and reported.
Approach: They propose a collaborative task which is collaborative rather than competitive and supports reproduction of research results.
Outcome: The proposed task is called REPROLANG-The Shared Task on the Reproduction of Research Results in Science and Technology of Natural Language Processing (LREC2020).
Do Differences in Values Influence Disagreements in Online Discussions? (2023.emnlp-main)

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Challenge: Disagreement is an important aspect of online discussions since it can drive novel ideas, incentivize evaluation of the proposed ideas, and avoid echo chambers.
Approach: They propose to use human-annotated agreement labels to estimate personal values and to include value information in agreement prediction to improve performance.
Outcome: The proposed models show that dissimilarity of value profiles correlates with disagreement in specific cases and that including value information in agreement prediction improves performance.
Language Models Lack Temporal Generalization and Bigger is Not Better (2025.findings-acl)

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Challenge: 450 encoder models are fine-tuned on 15 data splits on a task to detect events in Early Modern Dutch archival texts.
Approach: They propose to fine tune six encoder models that have been pretrained with very different data on a task in Early Modern Dutch archival texts.
Outcome: The proposed model is fine tuned with 5 seeds on 15 different data splits and reaches highest F1 performance.
Annotating Perspectives on Vaccination (2020.lrec-1)

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Challenge: Vaccination corpus is a corpus of texts related to the online vaccination debate . it contains documents from the Internet which reflect different views on vaccinations .
Approach: They present a corpus of texts related to the online vaccination debate annotated with perspectives about attribution, claims and opinions.
Outcome: The Vaccination Corpus contains 294 documents from the Internet which reflect different views on vaccinations.
Reasoning about Ambiguous Definite Descriptions (2023.findings-emnlp)

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Challenge: Existing resources to evaluate reasoning are not well suited to investigate the capability of resolving ambiguities by explicit reasoning.
Approach: They propose to use ambiguous definite descriptions to create a benchmark dataset which requires models to resolve ambiguity by explicit reasoning.
Outcome: The proposed model includes all information required to resolve the ambiguity in the prompt, which means a model does not require anything but reasoning to do well.
Scoring and Classifying Implicit Positive Interpretations: A Challenge of Class Imbalance (C18-1)

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Challenge: a reimplementation of a system on detecting implicit positive meaning from negated statements is reported . a baseline taking the mean score or most frequent class is hard to beat because of class imbalance in the dataset.
Approach: They propose a system to detect implicit positive meaning from negated statements . they convert the scores into classes and report their results on regression and classification tasks .
Outcome: The proposed system is hard to beat because of class imbalance in the dataset.
Systematic Study of Long Tail Phenomena in Entity Linking (C18-1)

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Challenge: Existing systems for entity linking are based on frequent 'head' cases, while performance drops when moving towards rare 'long tail' entities.
Approach: They propose to use a long tail to investigate the properties of entity linking datasets.
Outcome: The proposed systems overfit to popular/frequent and non-ambiguous cases and find the most difficult cases among the infrequent candidates of ambiguous forms.
An Empirical Analysis of Diversity in Argument Summarization (2024.eacl-long)

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Challenge: Current methods for summarizing arguments miss an important aspect of diversity . authors examine three aspects of diversity in argument summarization .
Approach: They propose three aspects of diversity that are important for accommodating multiple perspectives.
Outcome: The proposed models lack the diversity of opinions, sources, and annotators.
A Deep Dive into Word Sense Disambiguation with LSTM (C18-1)

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Challenge: LSTM-based language models have been shown effective in Word Sense Disambiguation (WSD) but neither the training data nor the source code was released.
Approach: They propose to use LSTM-based language models to perform Word Sense Disambiguation (WSD) using openly available datasets and software.
Outcome: The proposed method returned state-of-the-art performance in several benchmarks, but neither the training data nor the source code were released.
Modeling Dutch Medical Texts for Detecting Functional Categories and Levels of COVID-19 Patients (2022.lrec-1)

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Challenge: Electronic Health Records contain a lot of information in natural language that is not expressed in structured clinical data.
Approach: They propose a Dutch language model that can determine the functional level of patients according to a WHO coding framework.
Outcome: The proposed model can determine the functional level of patients according to a WHO coding framework.
Would you describe a leopard as yellow? Evaluating crowd-annotations with justified and informative disagreement (2020.coling-main)

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Challenge: Existing evaluation methods rely on agreement between annotators, which implies a single correct interpretation.
Approach: They propose an agreement-independent quality metric based on answer-coherence to evaluate on expected disagreement.
Outcome: The proposed model shows that agreement is the most important indicator of quality in semantic annotation tasks.
Don’t Annotate, but Validate: a Data-to-Text Method for Capturing Event Data (L18-1)

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Challenge: Existing methods to create event data are limited by ambiguity and variation in the data.
Approach: They propose a method to obtain large volumes of text corpora with event data . they use a tool to annotate texts and enrich the reference texts with event coreference annotations.
Outcome: The proposed method obtains large volumes of high-quality text corpora with event data . the data obtained with this method have high precision and at a large scale .
Unknown Script: Impact of Script on Cross-Lingual Transfer (2024.naacl-srw)

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Challenge: Existing models for high-resource languages are not available for all languages, and the vast majority of the world's languages are excluded from these models.
Approach: They propose to use pre-trained models to analyze the effect of the target language and its script on cross-lingual transfer.
Outcome: The proposed model is based on six models pre-trained on NER and POS tasks in the original script and romanized version.
The Circumstantial Event Ontology (CEO) and ECB+/CEO: an Ontology and Corpus for Implicit Causal Relations between Events (L18-1)

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Challenge: a new ontology for calamity events models semantic circumstantial relations between event classes . a circumstancial relation makes clear "why" something happened, without necessarily predicting it.
Approach: They propose a circumstantial event ontology that models semantic circumstancial relations between event classes . they propose ECB+ annotated corpus for circumstantal relations and a meta model .
Outcome: The proposed model captures that the change yielded by one event explains to people the happening of the next event when observed.
Introducing Frege to Fillmore: A FrameNet Dataset that Captures both Sense and Reference (2022.lrec-1)

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Challenge: a widely supported claim in the fields of semantics and philosophy is that meaning arises from the combination of sense and reference.
Approach: They propose a tool that facilitates both referential- and frame annotations of language-independent corpora.
Outcome: The Dutch FrameNet annotation tool facilitates both referential- and frame annotations of language-independent corpora.
Resource Interoperability for Sustainable Benchmarking: The Case of Events (L18-1)

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Challenge: Despite efforts to improve interoperability, there are still problems with benchmark corpora that are hampered by too laborious conversion steps.
Approach: They assess aspects of interoperability at the document-level across 20 annotated corpora and compare their compatibility and consistency across the corpors.
Outcome: The proposed framework enables the analysis of document intersections between the corpora and shows their compatibility and consistency across the corpus.
Large-scale Cross-lingual Language Resources for Referencing and Framing (2020.lrec-1)

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Challenge: Existing corpora that capture language but do not represent actual situations hinder development of systems to resolve cross-document coreference.
Approach: They introduce the concept of cross-lingual referential corpora and propose a framework to analyze framing . they expect to capture larger variation in framation compared to traditional approaches .
Outcome: The proposed project will analyze the framing of incidents in different languages and texts . it expects to capture larger variation in framation compared to traditional approaches .
Efficiently and Thoroughly Anonymizing a Transformer Language Model for Dutch Electronic Health Records: a Two-Step Method (2022.lrec-1)

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Challenge: Neural Networks (NNs) are used to model large amounts of data, such as text data, and have shown to be very useful for language modelling.
Approach: They propose to use a Dutch language model for hospital notes to anonymize a model trained on large amounts of data and publish it online.
Outcome: The proposed method predicts a name-like token 0.2% of the time, compared to the original training data.

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