Papers by Urvashi Khandelwal
Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context (P18-1)
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| Challenge: | Recent studies have shed light on the information encoded by long-term memory networks. |
| Approach: | They propose to use a neural caching model to model the role of context in an LSTM LM . they analyze the increase in perplexity when prior context words are shuffled, replaced, or dropped . |
| Outcome: | The proposed model is highly sensitive to the order of words within the most recent sentence, but ignores word order in the long-range context, suggesting the distant past is modeled only as a rough semantic field or topic. |
BAM! Born-Again Multi-Task Networks for Natural Language Understanding (P19-1)
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| Challenge: | Existing methods to train multi-task neural networks outperform or even match their single-task counterparts are difficult to implement. |
| Approach: | They propose a method that uses knowledge distillation to train multi-task neural networks that outperform or even match their single-task counterparts. |
| Outcome: | The proposed method outperforms or matches single-task neural networks on the GLUE benchmark. |
With Little Power Comes Great Responsibility (2020.emnlp-main)
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| Challenge: | Underpowered experiments make it more difficult to discern the difference between statistical noise and meaningful model improvements and increase the chances of exaggerated findings. |
| Approach: | They characterize typical statistical power for a variety of settings and characterize it by a set of existing NLP papers and datasets. |
| Outcome: | The authors characterize typical power for a variety of settings and find it common in the literature. |