Papers with parsing tasks
Multitask Parsing Across Semantic Representations (P18-1)
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| Challenge: | UCCA parsing is a test case for multitask learning, with auxiliary tasks AMR, SDP and Universal Dependencies (UD) . Semantic parsers have arguably yet to reach their full potential due to the limited amount of semantically annotated training data. |
| Approach: | They propose a general transition-based parser that can parse UCCA, AMR, SDP and Universal Dependencies (UD) they use a transition-driven learning architecture and a uniform transition-basic learning architecture to train the parsers. |
| Outcome: | The proposed parser improves UCCA, AMR, SDP and Universal Dependencies (UD) parsing over training in English, German and French. |
Transition-based Parsing with Stack-Transformers (2020.findings-emnlp)
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| Challenge: | Existing parsing systems use local or global models of the parser state to improve performance. |
| Approach: | They propose to modify the sequence-to-sequence Transformer to model global or local parser states in transition-based parsing. |
| Outcome: | The proposed model significantly improves performance on dependency and Abstract Meaning Representation (AMR) parsing tasks. |
Hierarchical Pointer Net Parsing (D19-1)
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| Challenge: | Existing approaches to parsing are greedy transition-based and globally optimized . however, the decision-making process is based on local information, causing error propagation to subsequent steps. |
| Approach: | They propose hierarchical pointer network parsers and apply them to dependency and sentence-level discourse parsing tasks. |
| Outcome: | The proposed method outperforms existing methods and sets new state-of-the-art methods on benchmark datasets. |
A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages (2020.acl-main)
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| Challenge: | a recent trend in neural NLP has been the introduction of feature-based and fine-tuning methods . we train monolingual contextualized word embeddings for five mid-resource languages . |
| Approach: | They use common Crawl corpus to train monolingual contextualized word embeddings . they compare performance of OSCAR-based and Wikipedia-based embeddables on part-of-speech tasks . |
| Outcome: | The results show that OSCAR-based and Wikipedia-based embeddings perform better than Wikipedia-style embedders on part-of-speech tagging and parsing tasks. |
Head-Driven Phrase Structure Grammar Parsing on Penn Treebank (P19-1)
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| Challenge: | Head-driven phrase structure grammars have a uniform formalism representing rich contextual syntactic and even semantic meanings. |
| Approach: | They propose to integrate constituent and dependency formal representations into head-driven phrase structure. |
| Outcome: | The proposed parser achieves state-of-the-art performance on Penn Treebank and Chinese Penn TreeBank. |
Eliciting Knowledge from Experts: Automatic Transcript Parsing for Cognitive Task Analysis (P19-1)
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| Challenge: | Cognitive task analysis (CTA) is a type of analysis used to elicit and represent the knowledge and thought processes of domain experts. |
| Approach: | They propose a weakly-supervised framework for automated CTA transcript parsing . they partition the parser process into a sequence labeling task and a text span-pair relation extraction task with distant supervision from human-curated protocol files. |
| Outcome: | The proposed framework reduces human labor and scales the task to a small scale. |
Multilingual Neural RST Discourse Parsing (2020.coling-main)
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| Challenge: | Existing studies on text discourse parsing for English are limited due to the lack of annotated data. |
| Approach: | They propose to use multilingual vector representations and segment-level translation to establish a neural, cross-lingual discourse parser. |
| Outcome: | The proposed model achieves state-of-the-art on cross-lingual, document-level discourse parsing on all sub-tasks. |