Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts
Bibliographic record
Abstract
This year, as has been the tradition over the past few years, the call, submission, reviewing and selection of tutorials were coordinated jointly for multiple conferences: ACL, NAACL, COLING and EMNLP.We formed a review committee of 34 members, including the ACL tutorial chairs (Luciana Benotti (then), Naoaki Okazaki, and Marcos Zampieri), the NAACL tutorial chairs (Cecilia O. Alm, Yulia Tsetkov, and Miguel Ballesteros), the COLING tutorial chairs (Heng Ji, Hsin-Hsi Chen, and Lucia Donatelli), the EMNLP tutorial chairs (Samhaa R. El-Beltagy and Xipeng Qiu), and 23 external reviewers (see Program Committee for the full list).A reviewing process was organised so that each proposal received 3 reviews.The selection criteria included clarity and preparedness, novelty or timely character of the topic, instructors' experience, likely audience interest, open access of the tutorial instructional material, and diversity and inclusion.A total of 47 tutorial submissions were received, of which 8 were selected for presentation at ACL.We solicited two types of tutorials, namely cutting-edge themes and introductory themes.The 8 tutorials for ACL include 2 introductory tutorials and 6 cutting-edge tutorials.The introductory tutorials are dedicated to deep neural networks and reproducibility in NLP.The cutting-edge discussions address knowledge-augmented methods, non-autoregressive sequence generation, learning with limited data, zero-and few-shot learning with pretrained language models, vision-language pretraining, and multilingual task-oriented dialogue.We would like to thank the tutorial authors for their contributions and flexibility while organising the conference in the hybrid mode.We are also grateful to the 23 external reviewers for their generous help in the decision process.Our thanks go to the conference organizers for effective collaboration, and in particular to the general chair Bernardo Magnini, the publication chair Danilo Croce, the handbook chair Marco Polignano, and the authors of aclpub2.Finally, special thanks go to Luciana Benotti, who worked hard as a tutorial chair of ACL especially maintaining the reviewing process (including the administrative work with OpenReview) but later resigned from this position when she was elected to the NAACL executive board as the NAACL chair for 2022.We hope you enjoy the tutorials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.162 | 0.136 |
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".