Probing for Labeled Dependency Trees
Max M\"uller-Eberstein, Rob van der Goot, Barbara Plank

TL;DR
This paper introduces DepProbe, a lightweight linear probe capable of extracting labeled, directed dependency trees from embeddings, outperforming previous methods in transfer language selection across multiple languages.
Contribution
DepProbe is a novel, efficient linear probe that captures full dependency parsing tasks, including labels and directions, from embeddings, enhancing transfer learning and analysis.
Findings
DepProbe accurately identifies the best source treebank 94% of the time across 13 languages.
It outperforms existing baselines and prior work in transfer language selection.
Analysis shows the importance of task-specific subspaces and non-linear parametrization in parsing.
Abstract
Probing has become an important tool for analyzing representations in Natural Language Processing (NLP). For graphical NLP tasks such as dependency parsing, linear probes are currently limited to extracting undirected or unlabeled parse trees which do not capture the full task. This work introduces DepProbe, a linear probe which can extract labeled and directed dependency parse trees from embeddings while using fewer parameters and compute than prior methods. Leveraging its full task coverage and lightweight parametrization, we investigate its predictive power for selecting the best transfer language for training a full biaffine attention parser. Across 13 languages, our proposed method identifies the best source treebank 94% of the time, outperforming competitive baselines and prior work. Finally, we analyze the informativeness of task-specific subspaces in contextual embeddings as…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsNatural Language Processing Techniques · Topic Modeling · Software Engineering Research
