Papers with clustering problem
A Grounded Unsupervised Universal Part-of-Speech Tagger for Low-Resource Languages (N19-1)
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| Challenge: | Unsupervised part of speech (POS) tagging is often framed as a clustering problem, but taggers need to ground their clusters as well. |
| Approach: | They propose an approach for low-resource unsupervised part of speech (POS) tagging that yields fully grounded output and requires no labeled training data. |
| Outcome: | The proposed method achieves reasonable performance across languages, including Sinhalese and Kinyarwanda, with no labeled training data. |
Generating Diverse and High-Quality Texts by Minimum Bayes Risk Decoding (2024.findings-acl)
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| Challenge: | Existing decoding algorithms to generate diverse outputs are based on beam search or random sampling, thus their output quality is capped by these underlying decoding methods. |
| Approach: | They propose to add a diversity penalty to MBR decoding and a clustering problem to create diversity-promoting decoding algorithms by enforcing diversity objectives. |
| Outcome: | The proposed method achieves a better trade-off than the diverse beam search and sampling algorithms overall. |