Papers by Tsvetomila Mihaylova
Understanding the Mechanics of SPIGOT: Surrogate Gradients for Latent Structure Learning (2020.emnlp-main)
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| Challenge: | Latent structure models can mitigate the error propagation and annotation bottleneck in pipeline systems, while uncovering linguistic insights about the data. |
| Approach: | They propose a latent structure model with a pullback of the downstream learning objective. |
| Outcome: | The proposed model outperforms the known and proposed model in the same family and yields new insights for practitioners and revealing intriguing failure cases. |
Latent Structure Models for Natural Language Processing (P19-4)
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| Challenge: | Latent structure models are a powerful tool for compositional data modeling and pipelines. |
| Approach: | This tutorial will cover recent advances in discrete latent structure models . it will discuss their motivation, potential, and limitations . |
| Outcome: | This tutorial will cover recent advances in discrete latent structure models . it will discuss their motivation, potential, and limitations . |
Scheduled Sampling for Transformers (P19-2)
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| Challenge: | Existing studies show that scheduled sampling can be applied to recurrent neural networks to avoid exposure bias. |
| Approach: | They propose to use teacher forced embeddings and model predictions to avoid exposure bias in sequence-to-sequence generation. |
| Outcome: | The proposed technique achieves performance close to a teacher-forcing baseline on two language pairs and is promising for future research. |