Papers by Michalina Strzyz
A Unifying Theory of Transition-based and Sequence Labeling Parsing (2020.coling-main)
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| Challenge: | Existing parsers that read sentences from left to right are not learning to parse them. |
| Approach: | They propose a mapping from transition-based parsing algorithms that read sentences from left to right to sequence labeling encodings of syntactic trees. |
| Outcome: | The proposed algorithms are learnable and comparable to existing encodings. |
Viable Dependency Parsing as Sequence Labeling (N19-1)
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| Challenge: | Existing work on dependency parsing by sequence labeling suggested that it was impractical. |
| Approach: | They propose to use dependency trees as sequence labels to obtain fast and accurate parsers using a conventional BILSTM-based model. |
| Outcome: | The proposed models are conceptually simple, not needing traditional parsing algorithms or auxiliary structures, and provide a good speed-accuracy tradeoff, with results competitive with more complex approaches. |
Bracketing Encodings for 2-Planar Dependency Parsing (2020.coling-main)
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| Challenge: | Existing bracketing-based encodings only handle a mild extension of projective trees . encodes that encode arcs in a given plane provide almost total coverage of crossing arc . |
| Approach: | They propose a bracketing-based encoding that can be used to represent any 2-planar dependency tree over a sentence of length n as a sequence of n labels. |
| Outcome: | The proposed method improves over existing bracketing encodings in non-projective treebanks while achieving similar speed. |
Towards Making a Dependency Parser See (D19-1)
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| Challenge: | Eye trackers and gaze features collected from them have been recently applied to natural language processing (NLP) tasks such as part-of-speech tagging. |
| Approach: | They propose to leverage eye-tracking data in an RNN dependency parser when no aggregated or token-level gaze features are used at inference time. |
| Outcome: | The proposed model can be used to improve performance on non-gazed treebanks. |
Sequence Labeling Parsing by Learning across Representations (P19-1)
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| Challenge: | Constituency and dependency parsing are the main abstractions for representing syntactic structure of sentences . constituency parsers are considered disjointed tasks, and their improvements have been obtained separately. |
| Approach: | They propose to add auxiliary loss to constituency parsing paradigms and explore a model that parses both paradigms at no cost. |
| Outcome: | The proposed model outperforms single-task models by 1.05 F1 points and 0.62 UAS points for constituency parsing and dependency parsers. |