Papers by Karo Moilanen
Pointing to Select: A Fast Pointer-LSTM for Long Text Classification (2020.coling-main)
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| Challenge: | Existing methods to skip irrelevant words in text processing are slow and vanishing gradients can cause slow inference and a loss of coherence. |
| Approach: | They propose a pointer network-based LSTM framework which can change skip rates during inference. |
| Outcome: | The proposed model is 1.1x3.5x faster than the standard LSTM framework and more accurate than Leap-LSTM at high skip rates. |
Topic Modeling With Topological Data Analysis (2022.emnlp-main)
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| Challenge: | Recent topic modelling approaches that use clustering on word, token or document embeddings can ex-tract coherent topics. |
| Approach: | They propose an unsupervised topic mod-elling method which uses TopologicalData Analysis to extract a topologicalskeleton of the manifold upon which word embeddings lie. |
| Outcome: | The proposed method performs on par with a baseline and can construct a network of coherent topics with meaningful relationships between them. |