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| "paper_id": "K17-2000", |
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| "date_generated": "2023-01-19T07:08:13.857308Z" |
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| "abstract": [], |
| "body_text": [ |
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| "text": "This volume contains the system description papers associated with the CoNLL-SIGMORPHON shared task in morphological reinflection held at CoNLL 2017. This is the first time a CoNLL shared task has directly addressed the learning of morphology from examples-a fundamental task in NLP where good solutions promise to benefit many downstream tasks. Moreover, models that learn complex morphological patterns from example data are also of significant linguistic interest.", |
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| "section": "Preface", |
| "sec_num": null |
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| { |
| "text": "To support the task, we collected and curated data from 52 languages, forming a typologically and genealogically diverse data set against which to evaluate performance of the systems. We divided the learning challenge into two sub-tasks: (1) learning to inflect nouns, adjectives, and verbs from their lemmata (citation forms) into a desired target form, and (2) completing partially filled inflection tables or paradigms. Both of these tasks have been discussed in the NLP and linguistics literature. Participants were further asked to complete each sub-task under a variety of different data conditions.", |
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| "section": "Preface", |
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| "text": "A total of 12 teams with members from 15 institutions participated in the shared task with a total of 27 system submissions. Of these, 11 submitted system description papers, which are included here. Consistent with last year's SIGMORPHON 2016 shared task results, neural network models performed very well in each data condition, including with a very low-resource training set. Another noteworthy aspect of the results is formed by the various biasing and data augmentation solutions that the different teams exploited to yield good performance with scarce examples.", |
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| "section": "Preface", |
| "sec_num": null |
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| { |
| "text": "The creation of several components in the shared task received support from DARPA I20 in the program Low Resource Languages for Emergent Incidents (LORELEI). We wish to thank Google for sponsoring an award given to the strongest overall system(s) and the organizers of CoNLL 2017 for their help. We also want to thank the participants and other members of the community who often provided thoughtful commentary on the data and the task itself.", |
| "cite_spans": [], |
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| "section": "Preface", |
| "sec_num": null |
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| { |
| "text": "We hope the data sets, which are now available, will serve as a useful resource to develop further techniques and research into morphological learning.", |
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| "section": "Preface", |
| "sec_num": null |
| }, |
| { |
| "text": "Mans Hulden, on behalf of the shared task organizers June 2017 Boulder, CO", |
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| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Preface", |
| "sec_num": null |
| } |
| ], |
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