Papers by Iryna Haponchyk
Supervised Neural Clustering via Latent Structured Output Learning: Application to Question Intents (2021.naacl-main)
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| Challenge: | Recent work on structured prediction has produced very effective supervised clustering algorithms using linear classifiers. |
| Approach: | They propose to use latent structured prediction loss and Transformer models to approach supervised clustering. |
| Outcome: | The proposed approach outperforms the state-of-the-art in recreating intents from public question corpora. |
A Study of Latent Structured Prediction Approaches to Passage Reranking (N19-1)
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| Challenge: | a structured output framework is useful for learning to rank problems . current approaches for answer sentence reranking are mostly based on pairwise ranking signals or simple binary classification. |
| Approach: | They propose a structured output approach which regards rankings as latent variables . they propose an inference procedure to find the max-violating ranking based on decomposition of the corresponding loss. |
| Outcome: | The proposed approach solves the optimization problem on WikiQA and TREC13 datasets. |
Supervised Clustering of Questions into Intents for Dialog System Applications (D18-1)
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| Challenge: | Existing methods for detecting intents in text are task-specific and costly . current methods focus on manually analyzing user questions and creating a taxonomy of intents to be attached to the appropriate actions. |
| Approach: | They propose a model for automatically clustering questions into user intents to help design tasks . they use powerful semantic classifiers and supervised clustering methods based on structured output . |
| Outcome: | The proposed model improves on two intent clustering corpora on two languages/domains. |