Adapting to the Long Tail: A Meta-Analysis of Transfer Learning Research for Language Understanding Tasks
AbstractNatural language understanding (NLU) has made massive progress driven by large benchmarks, but benchmarks often leave a long tail of infrequent phenomena underrepresented. We reflect on the question: Have transfer learning methods sufficiently addressed the poor performance o...
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| Format: | Article |
| Language: | English |
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The MIT Press
2022-01-01
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| Series: | Transactions of the Association for Computational Linguistics |
| Online Access: | https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00500/112917/Adapting-to-the-Long-Tail-A-Meta-Analysis-of |
| _version_ | 1797990192911482880 |
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| author | Aakanksha Naik Jill Lehman Carolyn Rosé |
| author_facet | Aakanksha Naik Jill Lehman Carolyn Rosé |
| author_sort | Aakanksha Naik |
| collection | DOAJ |
| description |
AbstractNatural language understanding (NLU) has made massive progress driven by large benchmarks, but benchmarks often leave a long tail of infrequent phenomena underrepresented. We reflect on the question: Have transfer learning methods sufficiently addressed the poor performance of benchmark-trained models on the long tail? We conceptualize the long tail using macro-level dimensions (underrepresented genres, topics, etc.), and perform a qualitative meta-analysis of 100 representative papers on transfer learning research for NLU. Our analysis asks three questions: (i) Which long tail dimensions do transfer learning studies target? (ii) Which properties of adaptation methods help improve performance on the long tail? (iii) Which methodological gaps have greatest negative impact on long tail performance? Our answers highlight major avenues for future research in transfer learning for the long tail. Lastly, using our meta-analysis framework, we perform a case study comparing the performance of various adaptation methods on clinical narratives, which provides interesting insights that may enable us to make progress along these future avenues. |
| first_indexed | 2024-04-11T08:32:39Z |
| format | Article |
| id | doaj.art-b730e47c0c7a46d9abdb3729ba3a07f6 |
| institution | Directory Open Access Journal |
| issn | 2307-387X |
| language | English |
| last_indexed | 2024-04-11T08:32:39Z |
| publishDate | 2022-01-01 |
| publisher | The MIT Press |
| record_format | Article |
| series | Transactions of the Association for Computational Linguistics |
| spelling | doaj.art-b730e47c0c7a46d9abdb3729ba3a07f62022-12-22T04:34:27ZengThe MIT PressTransactions of the Association for Computational Linguistics2307-387X2022-01-011095698010.1162/tacl_a_00500Adapting to the Long Tail: A Meta-Analysis of Transfer Learning Research for Language Understanding TasksAakanksha Naik0Jill Lehman1Carolyn Rosé2Language Technologies Institute, Carnegie Mellon University, USA. anaik@andrew.cmu.eduHuman-Computer Interaction Institute, Carnegie Mellon University, USA. jfl@andrew.cmu.eduLanguage Technologies Institute, Carnegie Mellon University, USA AbstractNatural language understanding (NLU) has made massive progress driven by large benchmarks, but benchmarks often leave a long tail of infrequent phenomena underrepresented. We reflect on the question: Have transfer learning methods sufficiently addressed the poor performance of benchmark-trained models on the long tail? We conceptualize the long tail using macro-level dimensions (underrepresented genres, topics, etc.), and perform a qualitative meta-analysis of 100 representative papers on transfer learning research for NLU. Our analysis asks three questions: (i) Which long tail dimensions do transfer learning studies target? (ii) Which properties of adaptation methods help improve performance on the long tail? (iii) Which methodological gaps have greatest negative impact on long tail performance? Our answers highlight major avenues for future research in transfer learning for the long tail. Lastly, using our meta-analysis framework, we perform a case study comparing the performance of various adaptation methods on clinical narratives, which provides interesting insights that may enable us to make progress along these future avenues.https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00500/112917/Adapting-to-the-Long-Tail-A-Meta-Analysis-of |
| spellingShingle | Aakanksha Naik Jill Lehman Carolyn Rosé Adapting to the Long Tail: A Meta-Analysis of Transfer Learning Research for Language Understanding Tasks Transactions of the Association for Computational Linguistics |
| title | Adapting to the Long Tail: A Meta-Analysis of Transfer Learning Research for Language Understanding Tasks |
| title_full | Adapting to the Long Tail: A Meta-Analysis of Transfer Learning Research for Language Understanding Tasks |
| title_fullStr | Adapting to the Long Tail: A Meta-Analysis of Transfer Learning Research for Language Understanding Tasks |
| title_full_unstemmed | Adapting to the Long Tail: A Meta-Analysis of Transfer Learning Research for Language Understanding Tasks |
| title_short | Adapting to the Long Tail: A Meta-Analysis of Transfer Learning Research for Language Understanding Tasks |
| title_sort | adapting to the long tail a meta analysis of transfer learning research for language understanding tasks |
| url | https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00500/112917/Adapting-to-the-Long-Tail-A-Meta-Analysis-of |
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