Papers by Maximilian Nickel
Learning Visually Grounded Sentence Representations (N18-1)
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| Challenge: | Unsupervised sentence representation models suffer from the grounding problem because of lack of association between symbols and external information. |
| Approach: | They train a sentence encoder to predict image features of a caption and use them as sentence representations. |
| Outcome: | The proposed model improves on word embeddings and word representations on standard benchmarks. |
Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings (P19-1)
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| Challenge: | Using hyperbolic embeddings, we can infer concept hierarchies from distributional contexts while also being able to predict missing “is-a”-relationships and correct wrong extractions. |
| Approach: | They propose a method combining hyperbolic embeddings and Hearst patterns to set appropriate constraints for inferring “is-a” relationships from large text corpora and improve taxonomic consistency. |
| Outcome: | The proposed method achieves state-of-the-art performance on a variety of hypernymy benchmarks. |
Revisiting the Evaluation of Theory of Mind through Question Answering (D19-1)
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| Challenge: | Existing benchmarks for theory of mind are flawed due to dataset biases . evaluators have been using the Sally-Anne test to infer false beliefs in others . |
| Approach: | They propose to use question answering to evaluate theory of mind . they propose to explicitly control for data regularities via a careful examination of the answer space . |
| Outcome: | The proposed evaluation protocol and dataset control for data regularities via a careful examination of the answer space. |
Hearst Patterns Revisited: Automatic Hypernym Detection from Large Text Corpora (P18-2)
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| Challenge: | a well-known problem of Hearst-like patterns is their extreme sparsity. |
| Approach: | They propose to use pattern-based and distributional methods to perform unsupervised hypernym detection. |
| Outcome: | The proposed method outperforms distributional methods on hypernymy tasks. |