Papers by Philip Schulz
A Stochastic Decoder for Neural Machine Translation (P18-1)
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| Challenge: | Neural machine translation models do not account for local lexical and syntactic variation in parallel corpora. |
| Approach: | They propose a deep generative model of machine translation which incorporates a chain of latent variables to account for local lexical and syntactic variation in parallel corpora. |
| Outcome: | The proposed model consistently improves over strong baselines on several different language pairs. |
Unsupervised Cross-Lingual Transfer of Structured Predictors without Source Data (2022.naacl-main)
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| Challenge: | Recent successes of NLP systems require large amounts of labelled data for structured prediction tasks. |
| Approach: | They propose a method for unsupervised transfer from multiple input models for structured prediction using a cross-lingual setup. |
| Outcome: | The proposed method produces less noisy labels for the distant supervision. |
Variational Inference and Deep Generative Models (P18-5)
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| Challenge: | Unsupervised and semi-supervised learning has been addressed scarcely in NLP . this tutorial provides an introduction to variational inference followed by an example-driven discussion of how to use variational methods for training DGMs. |
| Approach: | This tutorial provides an introduction to variational inference followed by an example-driven discussion of how to use variational methods for training DGMs. |
| Outcome: | This tutorial provides an introduction to variational inference followed by an example-driven discussion of how to use variational methods for training DGMs. |
Grounding learning of modifier dynamics: An application to color naming (D19-1)
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| Challenge: | Existing models for grounding are unable to understand modified color expressions, such as “light blue”. |
| Approach: | They propose a model that learns more complex transformations in RGB space and a hard ensemble model that selects a color space depending on the modifier-color pair. |
| Outcome: | The proposed model performs better in the HSV color space than the state-of-the-art model. |
PPT: Parsimonious Parser Transfer for Unsupervised Cross-Lingual Adaptation (2021.eacl-main)
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| Challenge: | Existing methods for cross-lingual transfer use implicit supervision to parse low-resource languages without explicit supervision. |
| Approach: | They propose a method for unsupervised cross-lingual transfer that uses their output as implicit supervision as part of self-training on unlabelled text in the target language. |
| Outcome: | The proposed method improves over state-of-the-art models on both distant and nearby languages, despite being conceptually simpler. |