Papers with down-sampling
Learning Better Internal Structure of Words for Sequence Labeling (D18-1)
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| Challenge: | a gap exists between methods for learning representations of sentences and words . authors propose a convolutional neural architecture with no down-sampling for learning words based on character embeddings . |
| Approach: | They propose a funnel-shaped wide convolutional neural architecture with no down-sampling for learning words' internal structure. |
| Outcome: | The proposed model outperforms other character embedding models on six sequence labeling datasets. |
Evaluating Topic Model on Asymmetric and Multi-Domain Financial Corpus (2024.lrec-main)
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| Challenge: | Recent research attempts to quantify the exposure of market assets to various risks from text and how assets react if the risk materializes itself. |
| Approach: | They propose two new metrics to evaluate the behavior of different types of topic models with respect to pitfalls previously mentioned about document risk distribution extraction. |
| Outcome: | The proposed models can be used to extract unbiased risk information from financial domain data and correct coherence imbalances. |
HypoNLI: Exploring the Artificial Patterns of Hypothesis-only Bias in Natural Language Inference (2020.lrec-1)
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| Challenge: | Recent studies have shown that for models trained on datasets for natural language inference (NLI), it is possible to make correct predictions by looking at the hypothesis while completely ignoring the premise. |
| Approach: | They propose to derive adversarial examples in terms of the hypothesis-only bias and explore eligible ways to mitigate such bias. |
| Outcome: | The proposed models can be used to mitigate the hypothesis-only bias by using down-sampling and adversarial training. |