An empirical study of compound PCFGs
| Authors |
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|---|---|
| Publication date | 2021 |
| Host editors |
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| Book title | The Second Workshop on Domain Adaptation for NLP |
| Book subtitle | Adap-NLP 2021 : Proceedings of the Workshop : April 20, 2021 |
| ISBN (electronic) |
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| Event | 2nd Workshop on Domain Adaptation for NLP, Adapt-NLP 2021 |
| Pages (from-to) | 166-171 |
| Number of pages | 6 |
| Publisher | Stroudsburg, PA: Association for Computational Linguistics |
| Organisations |
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| Abstract |
Compound probabilistic context-free grammars (C-PCFGs) have recently established a new state of the art for phrase-structure grammar induction. However, due to the high time-complexity of chart-based representation and inference, it is difficult to investigate them comprehensively. In this work, we rely on a fast implementation of C-PCFGs to conduct evaluation complementary to that of Kim et al. (2019). We highlight three key findings: (1) C-PCFGs are data-efficient, (2) C-PCFGs make the best use of global sentence-level information in preterminal rule probabilities, and (3) the best configurations of C-PCFGs on English do not always generalize to morphology-rich languages. |
| Document type | Conference contribution |
| Language | English |
| Published at | https://aclanthology.org/2021.adaptnlp-1.17 |
| Other links | https://github.com/zhaoyanpeng/xcfg https://www.scopus.com/pages/publications/85115691535 |
| Downloads |
2021.adaptnlp-1.17
(Final published version)
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