Don't understand a measure? Learn it: Structured Prediction for Coreference Resolution optimizing its measures
<p dir="ltr">An interesting aspect of structured prediction is the evaluation of an output structure against the gold standard. Especially in the loss-augmented setting, the need of finding the max-violating constraint has severely limited the expressivity of effective loss functions...
محفوظ في:
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| مؤلفون آخرون: | |
| منشور في: |
2017
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| الموضوعات: | |
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إضافة وسم
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| _version_ | 1864513557090009088 |
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| author | Iryna Haponchyk (19691701) |
| author2 | Alessandro Moschitti (19691683) |
| author2_role | author |
| author_facet | Iryna Haponchyk (19691701) Alessandro Moschitti (19691683) |
| author_role | author |
| dc.creator.none.fl_str_mv | Iryna Haponchyk (19691701) Alessandro Moschitti (19691683) |
| dc.date.none.fl_str_mv | 2017-07-30T06:00:00Z |
| dc.identifier.none.fl_str_mv | 10.18653/v1/p17-1094 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/conference_contribution/Don_t_understand_a_measure_Learn_it_Structured_Prediction__for_Coreference_Resolution_optimizing_its_measures/27050743 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Machine learning Language, communication and culture Linguistics Output Structure Evaluation Gold Standard Loss-Augmented Setting Max-Violating Constraint Expressive Loss Functions Coreference Resolution |
| dc.title.none.fl_str_mv | Don't understand a measure? Learn it: Structured Prediction for Coreference Resolution optimizing its measures |
| dc.type.none.fl_str_mv | Text Conference contribution info:eu-repo/semantics/publishedVersion text conference object |
| description | <p dir="ltr">An interesting aspect of structured prediction is the evaluation of an output structure against the gold standard. Especially in the loss-augmented setting, the need of finding the max-violating constraint has severely limited the expressivity of effective loss functions. In this paper, we trade off exact computation for enabling the use and study of more complex loss functions for coreference resolution. Most interestingly, we show that such functions can be (i) automatically learned also from controversial but commonly accepted coreference measures, e.g., MELA, and (ii) successfully used in learning algorithms. The accurate model comparison on the standard CoNLL-2012 setting shows the benefit of more expressive loss functions.</p><h2>Other Information</h2><p dir="ltr">Published in: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See conference contribution on publisher's website: <a href="https://dx.doi.org/10.18653/v1/p17-1094" target="_blank">https://dx.doi.org/10.18653/v1/p17-1094</a></p><p dir="ltr">Conference information: 55th Annual Meeting of the Association for Computational Linguistics (Short Papers), pages 518–523 Vancouver, Canada, July 30 - August 4, 2017</p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_8024f94118363657924658425ee450e2 |
| identifier_str_mv | 10.18653/v1/p17-1094 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/27050743 |
| publishDate | 2017 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Don't understand a measure? Learn it: Structured Prediction for Coreference Resolution optimizing its measuresIryna Haponchyk (19691701)Alessandro Moschitti (19691683)Information and computing sciencesArtificial intelligenceMachine learningLanguage, communication and cultureLinguisticsOutput Structure EvaluationGold StandardLoss-Augmented SettingMax-Violating ConstraintExpressive Loss FunctionsCoreference Resolution<p dir="ltr">An interesting aspect of structured prediction is the evaluation of an output structure against the gold standard. Especially in the loss-augmented setting, the need of finding the max-violating constraint has severely limited the expressivity of effective loss functions. In this paper, we trade off exact computation for enabling the use and study of more complex loss functions for coreference resolution. Most interestingly, we show that such functions can be (i) automatically learned also from controversial but commonly accepted coreference measures, e.g., MELA, and (ii) successfully used in learning algorithms. The accurate model comparison on the standard CoNLL-2012 setting shows the benefit of more expressive loss functions.</p><h2>Other Information</h2><p dir="ltr">Published in: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See conference contribution on publisher's website: <a href="https://dx.doi.org/10.18653/v1/p17-1094" target="_blank">https://dx.doi.org/10.18653/v1/p17-1094</a></p><p dir="ltr">Conference information: 55th Annual Meeting of the Association for Computational Linguistics (Short Papers), pages 518–523 Vancouver, Canada, July 30 - August 4, 2017</p>2017-07-30T06:00:00ZTextConference contributioninfo:eu-repo/semantics/publishedVersiontextconference object10.18653/v1/p17-1094https://figshare.com/articles/conference_contribution/Don_t_understand_a_measure_Learn_it_Structured_Prediction__for_Coreference_Resolution_optimizing_its_measures/27050743CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/270507432017-07-30T06:00:00Z |
| spellingShingle | Don't understand a measure? Learn it: Structured Prediction for Coreference Resolution optimizing its measures Iryna Haponchyk (19691701) Information and computing sciences Artificial intelligence Machine learning Language, communication and culture Linguistics Output Structure Evaluation Gold Standard Loss-Augmented Setting Max-Violating Constraint Expressive Loss Functions Coreference Resolution |
| status_str | publishedVersion |
| title | Don't understand a measure? Learn it: Structured Prediction for Coreference Resolution optimizing its measures |
| title_full | Don't understand a measure? Learn it: Structured Prediction for Coreference Resolution optimizing its measures |
| title_fullStr | Don't understand a measure? Learn it: Structured Prediction for Coreference Resolution optimizing its measures |
| title_full_unstemmed | Don't understand a measure? Learn it: Structured Prediction for Coreference Resolution optimizing its measures |
| title_short | Don't understand a measure? Learn it: Structured Prediction for Coreference Resolution optimizing its measures |
| title_sort | Don't understand a measure? Learn it: Structured Prediction for Coreference Resolution optimizing its measures |
| topic | Information and computing sciences Artificial intelligence Machine learning Language, communication and culture Linguistics Output Structure Evaluation Gold Standard Loss-Augmented Setting Max-Violating Constraint Expressive Loss Functions Coreference Resolution |