| Challenge: | a range of pre-training conditions can be used for constituency parsing, but large model sizes make it expensive to train separate models for each language. |
| Approach: | They compare the benefits of no pre-training, fastText, ELMo, and BERT for English . they also find that pre- training is beneficial across all 11 languages tested . |
| Outcome: | The proposed model outperforms fastText, ELMo, and BERT for English . but large model sizes make it expensive to train separate models for each language . |
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| Challenge: | Constituency Parse Extraction from Pre-trained Language Models (CPE-PLM) is a new paradigm that attempts to induce constituency parse trees based on the internal knowledge of pre-tried language models. |
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Multilingual Chart-based Constituency Parse Extraction from Pre-trained Language Models (2021.findings-emnlp)
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| Challenge: | Existing methods for extracting complete (binary) parses from pre-trained language models are expensive and time-consuming. |
| Approach: | They propose a chart-based method and an effective top-K ensemble technique to extractbinary parses from PLMs. |
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LLM-enhanced Self-training for Cross-domain Constituency Parsing (2023.emnlp-main)
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| Challenge: | Existing approaches to self-training rely on limited and potentially low-quality raw corpora. |
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An Empirical Comparison of Unsupervised Constituency Parsing Methods (2020.acl-main)
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| Challenge: | Existing methods for unsupervised constituency parsing are inconsistent due to data preprocessing, lexicalization, and evaluation metrics. |
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Can Monolingual Pretrained Models Help Cross-Lingual Classification? (2020.aacl-main)
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| Challenge: | Multilingual pretrained language models have shown impressive results for cross-lingual transfer, but due to the constant model capacity, multilingual pre-training usually lags behind the monolingual competitors. |
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Give your Text Representation Models some Love: the Case for Basque (2020.lrec-1)
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Rodrigo Agerri, Iñaki San Vicente, Jon Ander Campos, Ander Barrena, Xabier Saralegi, Aitor Soroa, Eneko Agirre
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Go Simple and Pre-Train on Domain-Specific Corpora: On the Role of Training Data for Text Classification (2020.coling-main)
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| Challenge: | Recent results show that deep neural networks using contextual embeddings outperform non-contextual embedders on a majority of text classification tasks. |
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An Empirical Study of Building a Strong Baseline for Constituency Parsing (P18-2)
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| Challenge: | Sequence-to-sequence models have been used for natural language generation tasks such as machine translation and summarization. |
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| Outcome: | The proposed model outperforms existing models in natural language generation tasks without any explicit task-specific knowledge or architecture of constituent parsing. |
Meeting the Needs of Low-Resource Languages: The Value of Automatic Alignments via Pretrained Models (2023.eacl-main)
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Abteen Ebrahimi, Arya D. McCarthy, Arturo Oncevay, John E. Ortega, Luis Chiruzzo, Gustavo Giménez-Lugo, Rolando Coto-Solano, Katharina Kann
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