Difference between revisions of "Fall 2022 Schedule"

From CompSemWiki
Jump to navigationJump to search
Line 20: Line 20:
 
| 3.3.21 || Clayton Lewis: Garfinkel and NLP
 
| 3.3.21 || Clayton Lewis: Garfinkel and NLP
 
|- style="border-top: 2px solid DarkGray;"
 
|- style="border-top: 2px solid DarkGray;"
| 3.10.21 || guest of Alexis Palmer
+
| 3.10.21 || guest of Alexis Palmer, Antonis Anastasapolous, [https://arxiv.org/abs/2011.00767]
 +
Reducing Confusion in Active Learning
 +
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.
 
|- style="border-top: 2px solid DarkGray;"
 
|- style="border-top: 2px solid DarkGray;"
 
| 3.17.21 ||  
 
| 3.17.21 ||  

Revision as of 13:54, 8 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 Clayton Lewis will lead a discussion of challenges for Natural Language Understanding
2.10.21 Martha Palmer SCIL UMR practice talk
2.17.21 ACL paper discussion, led by Jon Cai & Sarah Moeller
2.24.21 Sarah Moeller practice talk
3.3.21 Clayton Lewis: Garfinkel and NLP
3.10.21 guest of Alexis Palmer, Antonis Anastasapolous, [1]

Reducing Confusion in Active Learning 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
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