Proceedings of the ACL Workshop on Building and Using Parallel Texts
Bibliographic record
Abstract
The ACL 2005 Workshop on Building and Using Parallel Texts: Data-Driven Machine Translation and Beyond, took place onWednesday, June 29 and Thursday, June 30 in Ann Arbor Michigan, immediately following the 43rd Annual Meeting of the Association for Computational Linguistics. This workshop represented a merger of two workshops that were originally proposed as independent events. Joel Martin, Rada Mihalcea, and Ted Pedersen had proposed a workshop on Building and Using Parallel Texts for Languages with Scarce Resources, which was intended as a follow-up event to the NAACL 2003 Workshop on Parallel Text that had been organized by Mihalcea and Pedersen. At the same time, Philipp Koehn and Christof Monz had proposed a workshop on Exploiting Parallel Texts for Statistical Machine Translation, featuring a shared task on Phrase Based Machine Translation. Given the close relationship between the two proposed topics, the idea of a merger was quickly embraced by all concerned. It was agreed that the workshop would have two tracks, one regarding Parallel Texts for Languages with Scarce Resources (Track 1), and the other focused on Statistical Machine Translation (Track 2). Prior to the workshop, in addition to soliciting relevant papers for review and possible presentation, the organizers of both tracks conducted shared tasks that brought together systems for an evaluation on previously unseen data. Track 1 featured a Word Alignment shared task, where the object was to align parallel text in one or more of the following langauge pairs: Inuktitut-English, Romanian-English, and Hindi-English. Track 2 carried out a shared task on Phrase Based Statistical Machine Translation, where eleven participating teams competed to build machine translation systems for French-English, Spanish-English, German-English, and Finnish-English. The results of the shared tasks were announced at the workshop, and these proceedings also include an overview paper for each shared task that summarizes the results, as well as provides information about the data used and any procedures that were followed in conducting or scoring the task. In addition, there are short papers from each participating team for each shared task that describe their underlying system in some detail. Wednesday, June 29 was dedicated to Track 1. It featured an invited talk by Mike Maxwell of the Linguistic Data Consortium, eight long paper presentations relevant to the topic of building and using parallel texts for languages with scarce resources, six short paper presentations describing systems that participated in the Word Alignment shared task (four additional short papers are included in the proceedings), a shared task overview, and a panel discussion about lessons learned from the shared task. Track 2 was featured on Thursday, June 30. It included an invited talk by Franz Josef Och of Google, six long paper presentations, a shared task overview, and nine shared task system descriptions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".