Difference between revisions of "Fall 2022 Schedule"

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| 2.10.21 || Martha Palmer SCIL UMR  practice talk
 
| 2.10.21 || Martha Palmer SCIL UMR  practice talk
 
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| 2.17.21 || ACL paper discussion, led by Jon Cai & Sarah Moeller
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| 2.17.21 || Cancelled because of Wellness Day
 
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| 2.24.21 || Sarah Moeller practice talk
 
| 2.24.21 || Sarah Moeller practice talk
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Active learning (AL) uses a data selection algorithm to select useful training samples to minimize annotation cost. This is now an essential tool for building low-resource syntactic analyzers such as part-of-speech (POS) taggers. Existing AL heuristics are generally designed on the principle of selecting uncertain yet representative training instances, where annotating these instances may reduce a large number of errors. However, in an empirical study across six typologically diverse languages (German, Swedish, Galician, North Sami, Persian, and Ukrainian), we found the surprising result that even in an oracle scenario where we know the true uncertainty of predictions, these current heuristics are far from optimal. Based on this analysis, we pose the problem of AL as selecting instances which maximally reduce the confusion between particular pairs of output tags. Extensive experimentation on the aforementioned languages shows that our proposed AL strategy outperforms other AL strategies by a significant margin. We also present auxiliary results demonstrating the importance of proper calibration of models, which we ensure through cross-view training, and analysis demonstrating how our proposed strategy selects examples that more closely follow the oracle data distribution.
 
Active learning (AL) uses a data selection algorithm to select useful training samples to minimize annotation cost. This is now an essential tool for building low-resource syntactic analyzers such as part-of-speech (POS) taggers. Existing AL heuristics are generally designed on the principle of selecting uncertain yet representative training instances, where annotating these instances may reduce a large number of errors. However, in an empirical study across six typologically diverse languages (German, Swedish, Galician, North Sami, Persian, and Ukrainian), we found the surprising result that even in an oracle scenario where we know the true uncertainty of predictions, these current heuristics are far from optimal. Based on this analysis, we pose the problem of AL as selecting instances which maximally reduce the confusion between particular pairs of output tags. Extensive experimentation on the aforementioned languages shows that our proposed AL strategy outperforms other AL strategies by a significant margin. We also present auxiliary results demonstrating the importance of proper calibration of models, which we ensure through cross-view training, and analysis demonstrating how our proposed strategy selects examples that more closely follow the oracle data distribution.
 
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|- style="border-top: 2px solid DarkGray;"
| 3.17.21 || Conflict with DARPA KAIROS PI Meeting
+
| 3.17.21 || ACL paper discussion, led by Jon Cai & Sarah Moeller
 +
(Conflict with DARPA KAIROS PI Meeting, no Martha, Susan, Piyush, Akanksha or Ghazaleh)
 
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| 3.24.21 || Skatje Meyers proposal
 
| 3.24.21 || Skatje Meyers proposal

Revision as of 14:48, 14 February 2021

Date Title
3.25.20 Happy New Year!
1.27.21 Planning, Zihan Wang, Extending Multilingual BERT to Low-Resource Languages
2.3.21 cancelled because of DARPA AIDA PI Meeting conflict
2.10.21 Martha Palmer SCIL UMR practice talk
2.17.21 Cancelled because of Wellness Day
2.24.21 Sarah Moeller practice talk
3.3.21 Clayton Lewis: Garfinkel and NLP - a discussion of challenges for Natural Language Understanding
3.10.21 guest of Alexis Palmer, Antonis Anastasapolous, [1]

Reducing Confusion in Active Learning [2]

Active learning (AL) uses a data selection algorithm to select useful training samples to minimize annotation cost. This is now an essential tool for building low-resource syntactic analyzers such as part-of-speech (POS) taggers. Existing AL heuristics are generally designed on the principle of selecting uncertain yet representative training instances, where annotating these instances may reduce a large number of errors. However, in an empirical study across six typologically diverse languages (German, Swedish, Galician, North Sami, Persian, and Ukrainian), we found the surprising result that even in an oracle scenario where we know the true uncertainty of predictions, these current heuristics are far from optimal. Based on this analysis, we pose the problem of AL as selecting instances which maximally reduce the confusion between particular pairs of output tags. Extensive experimentation on the aforementioned languages shows that our proposed AL strategy outperforms other AL strategies by a significant margin. We also present auxiliary results demonstrating the importance of proper calibration of models, which we ensure through cross-view training, and analysis demonstrating how our proposed strategy selects examples that more closely follow the oracle data distribution.

3.17.21 ACL paper discussion, led by Jon Cai & Sarah Moeller

(Conflict with DARPA KAIROS PI Meeting, no Martha, Susan, Piyush, Akanksha or Ghazaleh)

3.24.21 Skatje Meyers proposal
3.31.21 Capstone Projects
4.7.21
4.14.21
4.21.21 Rehan Ahmed proposal
4.28.21 Abhidip Bhattacharyya proposal
5.05.21

Past Schedules