Summary of Tree-averaging Algorithms For Ensemble-based Unsupervised Discontinuous Constituency Parsing, by Behzad Shayegh et al.
Tree-Averaging Algorithms for Ensemble-Based Unsupervised Discontinuous Constituency Parsing
by Behzad Shayegh, Yuqiao Wen, Lili Mou
First submitted to arxiv on: 29 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed model tackles unsupervised discontinuous constituency parsing, addressing the high variance in performance observed in previous literature. By building an ensemble of different runs of a existing parser and averaging predicted trees, the model aims to stabilize and boost performance. A comprehensive computational complexity analysis is provided, including P and NP-complete setups for tree averaging under different binarity and continuity scenarios. An efficient exact algorithm is developed to tackle the task, which is reasonable in terms of run-time. Results on three datasets show the method outperforming all baselines in all metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We’re trying to make a computer program better at understanding how words are related in sentences without being taught beforehand. The current best model is very good but can be quite different each time it’s used, so we want to make it more consistent and accurate. To do this, we’re going to combine the results of many runs of the same program together. We’ve analyzed how long this will take using math, and developed a special way for our computer to calculate the answers quickly. When we tested our method on different groups of sentences, it did better than other approaches in all areas. |
Keywords
* Artificial intelligence * Parsing * Unsupervised
