Proceedings of the Second Workshop on Universal Dependencies (UDW 2018)
Bibliographic record
Abstract
These proceedings include the program and papers that are presented at the second workshop on Universal Dependencies, held in conjunction with EMNLP in Brussels (Belgium) on November 1, 2018.Universal Dependencies (UD) is a framework for cross-linguistically consistent treebank annotation that has so far been applied to over 70 languages (http://universaldependencies.org/).The framework is aiming to capture similarities as well as idiosyncrasies among typologically different languages (e.g., morphologically rich languages, pro-drop languages, and languages featuring clitic doubling).The goal in developing UD was not only to support comparative evaluation and cross-lingual learning but also to facilitate multilingual natural language processing and enable comparative linguistic studies.After a successful first UD workshop at NoDaLiDa in Gothenburg last year, we decided to continue to bring together researchers working on UD, to reflect on the theory and practice of UD, its use in research and development, and its future goals and challenges.We received 39 submissions of which 26 were accepted.Submissions covered several topics: some papers describe treebank conversion or creation, while others target specific linguistic constructions and which analysis to adopt, sometimes with critiques of the choices made in UD; some papers exploit UD resources for cross-linguistic and psycholinguistic analysis, or for parsing, and others discuss the relation of UD to different frameworks.We are honored to have two invited speakers: Barbara Plank (Computer Science Department, IT University of Copenhagen), with a talk on "Learning χ 2 -Natural Language Processing Across Languages and Domains", and Dag Haug (Department of Philosophy, Classics, History of Arts and Ideas, University of Oslo), speaking about "Glue semantics for UD".Our invited speakers target different aspects of UD in their work: Barbara Plank's talk is an instance of how UD facilitates cross-lingual learning and transfer for NLP components, whereas Dag Haug will address how UD and semantic formalisms can intersect.We are grateful to the program committee, who worked hard and on a tight schedule to review the submissions and provided authors with valuable feedback.We thank Google, Inc. for its sponsorship which made it possible to feature two invited talks.We also want to thank Jan Hajic
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.070 | 0.026 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".