Search Results for author: Emiel Krahmer

Found 40 papers, 8 papers with code

Gradations of Error Severity in Automatic Image Descriptions

no code implementations INLG (ACL) 2020 Emiel van Miltenburg, Wei-Ting Lu, Emiel Krahmer, Albert Gatt, Guanyi Chen, Lin Li, Kees Van Deemter

Because our manipulated descriptions form minimal pairs with the reference descriptions, we are able to assess the impact of different kinds of errors on the perceived quality of the descriptions.

Evaluating Task-oriented Dialogue Systems: A Systematic Review of Measures, Constructs and their Operationalisations

no code implementations21 Dec 2023 Anouck Braggaar, Christine Liebrecht, Emiel van Miltenburg, Emiel Krahmer

This review gives an extensive overview of evaluation methods for task-oriented dialogue systems, paying special attention to practical applications of dialogue systems, for example for customer service.

Task-Oriented Dialogue Systems

Neural Data-to-Text Generation Based on Small Datasets: Comparing the Added Value of Two Semi-Supervised Learning Approaches on Top of a Large Language Model

no code implementations14 Jul 2022 Chris van der Lee, Thiago castro Ferreira, Chris Emmery, Travis Wiltshire, Emiel Krahmer

In terms of output quality, extending the training set of a data-to-text system with a language model using the pseudo-labeling approach did increase text quality scores, but the data augmentation approach yielded similar scores to the system without training set extension.

Data Augmentation Data-to-Text Generation +2

Preregistering NLP Research

no code implementations NAACL 2021 Emiel van Miltenburg, Chris van der Lee, Emiel Krahmer

Preregistration refers to the practice of specifying what you are going to do, and what you expect to find in your study, before carrying out the study.

Surface Realization Shared Task 2019 (MSR19): The Team 6 Approach

no code implementations WS 2019 Thiago Castro Ferreira, Emiel Krahmer

This study describes the approach developed by the Tilburg University team to the shallow track of the Multilingual Surface Realization Shared Task 2019 (SR{'}19) (Mille et al., 2019).

Machine Translation Translation

A Personalized Data-to-Text Support Tool for Cancer Patients

no code implementations WS 2019 Saar Hommes, Chris van der Lee, Felix Clouth, Jeroen Vermunt, X Verbeek, er, Emiel Krahmer

In this paper, we present a novel data-to-text system for cancer patients, providing information on quality of life implications after treatment, which can be embedded in the context of shared decision making.

Decision Making

Neural data-to-text generation: A comparison between pipeline and end-to-end architectures

1 code implementation IJCNLP 2019 Thiago Castro Ferreira, Chris van der Lee, Emiel van Miltenburg, Emiel Krahmer

In contrast, recent neural models for data-to-text generation have been proposed as end-to-end approaches, where the non-linguistic input is rendered in natural language with much less explicit intermediate representations in-between.

Data-to-Text Generation Decoder

Context-sensitive Natural Language Generation for robot-assisted second language tutoring

no code implementations WS 2018 Bram Willemsen, Jan de Wit, Emiel Krahmer, Mirjam de Haas, Paul Vogt

The L2TOR ITS is developed for the purpose of investigating the efficacy of robot-assisted second language tutoring in early childhood.

Text Generation

Enriching the WebNLG corpus

1 code implementation WS 2018 Thiago Castro Ferreira, Diego Moussallem, Emiel Krahmer, S Wubben, er

This paper describes the enrichment of WebNLG corpus (Gardent et al., 2017a, b), with the aim to further extend its usefulness as a resource for evaluating common NLG tasks, including Discourse Ordering, Lexicalization and Referring Expression Generation.

Machine Translation Referring Expression +3

Making effective use of healthcare data using data-to-text technology

no code implementations10 Aug 2018 Steffen Pauws, Albert Gatt, Emiel Krahmer, Ehud Reiter

Financial reports are produced to assess healthcare organizations on some key performance indicators to steer their healthcare delivery.

DIDEC: The Dutch Image Description and Eye-tracking Corpus

no code implementations COLING 2018 Emiel van Miltenburg, {\'A}kos K{\'a}d{\'a}r, Ruud Koolen, Emiel Krahmer

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.

Specificity Task 2

Evaluating the text quality, human likeness and tailoring component of PASS: A Dutch data-to-text system for soccer

no code implementations COLING 2018 Chris van der Lee, Bart Verduijn, Emiel Krahmer, S Wubben, er

We present an evaluation of PASS, a data-to-text system that generates Dutch soccer reports from match statistics which are automatically tailored towards fans of one club or the other.

Text Generation

Varying image description tasks: spoken versus written descriptions

1 code implementation COLING 2018 Emiel van Miltenburg, Ruud Koolen, Emiel Krahmer

Automatic image description systems are commonly trained and evaluated on written image descriptions.

Surface Realization Shared Task 2018 (SR18): The Tilburg University Approach

1 code implementation WS 2018 Thiago Castro Ferreira, S Wubben, er, Emiel Krahmer

This study describes the approach developed by the Tilburg University team to the shallow task of the Multilingual Surface Realization Shared Task 2018 (SR18).

Machine Translation Translation

NeuralREG: An end-to-end approach to referring expression generation

1 code implementation ACL 2018 Thiago Castro Ferreira, Diego Moussallem, Ákos Kádár, Sander Wubben, Emiel Krahmer

Traditionally, Referring Expression Generation (REG) models first decide on the form and then on the content of references to discourse entities in text, typically relying on features such as salience and grammatical function.

Referring Expression Referring expression generation

Linguistic realisation as machine translation: Comparing different MT models for AMR-to-text generation

no code implementations WS 2017 Thiago Castro Ferreira, Iacer Calixto, S Wubben, er, Emiel Krahmer

In this paper, we study AMR-to-text generation, framing it as a translation task and comparing two different MT approaches (Phrase-based and Neural MT).

AMR-to-Text Generation Machine Translation +2

Generating flexible proper name references in text: Data, models and evaluation

no code implementations EACL 2017 Thiago Castro Ferreira, Emiel Krahmer, S Wubben, er

The model relies on the REGnames corpus, a dataset with 53, 102 proper name references to 1, 000 people in different discourse contexts.

Text Generation

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

no code implementations29 Mar 2017 Albert Gatt, Emiel Krahmer

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input.

nlg evaluation Text Generation

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