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View article: Intrinsic Task-based Evaluation for Referring Expression Generation
Intrinsic Task-based Evaluation for Referring Expression Generation Open
Recently, a human evaluation study of Referring Expression Generation (REG) models had an unexpected conclusion: on \textsc{webnlg}, Referring Expressions (REs) generated by the state-of-the-art neural models were not only indistinguishabl…
View article: Models of reference production: How do they withstand the test of time?
Models of reference production: How do they withstand the test of time? Open
In recent years, many NLP studies have focused solely on performance improvement. In this work, we focus on the linguistic and scientific aspects of NLP. We use the task of generating referring expressions in context (REG-in-context) as a …
View article: Models of reference production: How do they withstand the test of time?
Models of reference production: How do they withstand the test of time? Open
In recent years, many NLP studies have focused solely on performance improvement. In this work, we focus on the linguistic and scientific aspects of NLP. We use the task of generating referring expressions in context (REG-in-context) as a …
View article: Multi-layered Annotation of Conversation-like Narratives in German
Multi-layered Annotation of Conversation-like Narratives in German Open
This work presents two corpora based on excerpts from two novels with an informal narration style in German. We performed fine-grained multi-layer annotations of animate referents, assigning local and global prominence-lending features to …
View article: Neural referential form selection: Generalisability and interpretability
Neural referential form selection: Generalisability and interpretability Open
In recent years, a range of Neural Referring Expression Generation (REG) systems have been built and they have often achieved encouraging results. However, these models are often thought to lack transparency and generality. Firstly, it is …
View article: Assessing Neural Referential Form Selectors on a Realistic Multilingual Dataset
Assessing Neural Referential Form Selectors on a Realistic Multilingual Dataset Open
Previous work on Neural Referring Expression Generation (REG) all uses WebNLG, an English dataset that has been shown to reflect a very limited range of referring expression (RE) use. To tackle this issue, we build a dataset based on the O…
View article: Non-neural Models Matter: a Re-evaluation of Neural Referring Expression Generation Systems
Non-neural Models Matter: a Re-evaluation of Neural Referring Expression Generation Systems Open
In recent years, neural models have often outperformed rule-based and classic Machine Learning approaches in NLG. These classic approaches are now often disregarded, for example when new neural models are evaluated. We argue that they shou…
View article: What can Neural Referential Form Selectors Learn?
What can Neural Referential Form Selectors Learn? Open
Despite achieving encouraging results, neural Referring Expression Generation models are often thought to lack transparency. We probed neural Referential Form Selection (RFS) models to find out to what extent the linguistic features influe…
View article: What can Neural Referential Form Selectors Learn?
What can Neural Referential Form Selectors Learn? Open
Despite achieving encouraging results, neural Referring Expression Generation models are often thought to lack transparency. We probed neural Referential Form Selection (RFS) models to find out to what extent the linguistic features influe…
View article: What can Neural Referential Form Selectors Learn?
What can Neural Referential Form Selectors Learn? Open
Despite achieving encouraging results, neural Referring Expression Generation models are often thought to lack transparency. We probed neural Referential Form Selection (RFS) models to find out to what extent the linguistic features influe…
View article: A Linguistic Perspective on Reference: Choosing a Feature Set for Generating Referring Expressions in Context
A Linguistic Perspective on Reference: Choosing a Feature Set for Generating Referring Expressions in Context Open
This paper reports on a structured evaluation of feature-based Machine Learning algorithms for selecting the form of a referring expression in discourse context. Based on this evaluation, we selected seven feature sets from the literature,…
View article: Computational Interpretations of Recency for the Choice of Referring Expressions in Discourse
Computational Interpretations of Recency for the Choice of Referring Expressions in Discourse Open
First, we discuss the most common linguistic perspectives on the concept of recency and propose a taxonomy of recency metrics employed in Machine Learning studies for choosing the form of referring expressions in discourse context. We then…
View article: A stand-off XML-TEI representation of reference annotation
A stand-off XML-TEI representation of reference annotation Open
In this poster, we present an XML-TEI conformant stand-off representation of reference in discourse, building on the seminal work
carried out in the MATE project (Poesio, Bruneseaux & Romary 1999) and the earlier proposal on a reference a…