Papers by Matthijs Westera
TED-Q: TED Talks and the Questions they Evoke (2020.lrec-1)
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| Challenge: | Evoked questions represent a hitherto unexplored type of linguistic data, promising to open up important new lines of research. |
| Approach: | They propose a method to annotate TED-talks with the questions they evoke and, where available, the answers to these questions. |
| Outcome: | The proposed method is designed to scale up, relying on crowdsourcing by non-expert annotators, with its utility for Natural Language Processing in mind. |
Humans Meet Models on Object Naming: A New Dataset and Analysis (2020.coling-main)
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| Challenge: | Existing object naming datasets that use only images with a bounding box are noisy . a human-like model behavior is not stable across domains, a study finds . |
| Approach: | They use MN v2 to verify object naming datasets with dozens of valid names per object . they find that human-like model behavior is not stable across domains . |
| Outcome: | The proposed model confuses people and clothing objects more frequently than humans do. |
What do Entity-Centric Models Learn? Insights from Entity Linking in Multi-Party Dialogue (N19-1)
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| Challenge: | a recent study suggests that models that incorporate a bias towards learning entity representations are not effective at modeling entities. |
| Approach: | They propose to use two entity-centric models for a referential task . they show they outperform the state of the art and do better on lower frequency entities . |
| Outcome: | The proposed models outperform the state of the art on a referential task . they do better on lower frequency entities than a counterpart model not entity-centric . |
Similarity or deeper understanding? Analyzing the TED-Q dataset of evoked questions (2020.coling-main)
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| Challenge: | TED-Q datasets are annotated with the questions they implicitly evoke, based on a dataset of TED talks . we test whether relation between a discourse and questions it evokes is one of similarity or association . |
| Approach: | They construct a binary classification task from TED-Q and fit a BERT-based classifier alongside models based on different notions of similarity. |
| Outcome: | The proposed classifier outperforms similarity-based models in the TED-Q dataset. |