Kerria Pang-Naylor, Shivani Manivasagan, Aitong Zhong, Mehak Garg, Nicholas Mondello, Blake Buckner, Jonathan P. Chang, Khyati Mahajan, Masoud Hashemi, Fabio Casati
| Challenge: | Unsupervised text clustering is unlikely to produce groupings that work across use cases . authors present techniques to effectively control text embeddings with minimal human input . |
| Approach: | They propose techniques to control text embeddings with minimal human input . they evaluate clustering performance for datasets with multiple independent labels . |
| Outcome: | The proposed techniques improve clustering for one perspective or use case, but at a tradeoff in performance for another use case. |
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