Neural Coreference Resolution for Dutch Parliamentary Documents with the DutchParliament Dataset

The task of coreference resolution concerns the clustering of words and phrases referring to the same entity in text, either in the same document or across multiple documents. The task is challenging, as it concerns elements of named entity recognition and reading comprehension, as well as others. In this paper, we introduce DutchParliament, a new Dutch coreference resolution dataset obtained through the manual annotation of 74 government debates, expanded with a domain-specific class. In contrast to existing datasets, which are often composed of news articles, blogs or other documents, the debates in DutchParliament are transcriptions of speech, and therefore offer a unique structure and way of referencing compared to other datasets. By constructing and releasing this dataset, we hope to facilitate the research on coreference resolution in niche domains, with different characteristics than traditional datasets. The DutchParliament dataset was compared to SoNaR-1 and RiddleCoref, two other existing Dutch coreference resolution corpora, to highlight its particularities and differences from existing datasets. Furthermore, two coreference resolution models for Dutch, the rule-based DutchCoref model and the neural e2eDutch model, were evaluated on the DutchParliament dataset to examine their performance on the DutchParliament dataset. It was found that the characteristics of the DutchParliament dataset are quite different from that of the other two datasets, although the performance of the e2eDutch model does not seem to be significantly affected by this. Furthermore, experiments were conducted by utilizing the metadata present in the DutchParliament corpus to improve the performance of the e2eDutch model. The results indicate that the addition of available metadata about speakers has a beneficial effect on the performance of the model, although the addition of the gender of speakers seems to have a limited effect.