Papers by Amandla Mabona
Neural Generative Rhetorical Structure Parsing (D19-1)
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| Challenge: | Existing approaches to RST parsing use discriminative models, but these are less sample efficient and datasets are typically small. |
| Approach: | They propose a document-level RNN grammar with a bottom-up traversal order and a beam search algorithm that keeps track of both structure-and word-generating actions without exhibit-ing this branching bias. |
| Outcome: | The proposed model outperforms a discriminative model with the same features by 2.6 F1points and achieves comparable performance to the state-of-the-art. |
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. |