Deep Learning and Sociophonetics: Automatic Coding of Rhoticity Using Neural Networks (N19-3)
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| Challenge: | Automated extraction methods for vowels are available, but coding rhoticity has lagged behind. |
| Approach: | They use Neural Networks/Deep Learning to train a model on 208 speakers in Boston . they find that there is no reliable method for classifying r-dropping . |
| Outcome: | The proposed method trains a model on 208 speakers in Boston, Massachusetts. |
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| Challenge: | This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development, cutting- edge deep learning models, and highlights from recent research on using NLI to understand capabilities and limits of deep learning for language understanding and reasoning. |
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Findings of the Association for Computational Linguistics: EMNLP 2020 (2020.findings-emnlp)
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| Challenge: | . - (EN) |
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Do Neural Language Models Overcome Reporting Bias? (2020.coling-main)
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Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorials (N19-5)
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| Challenge: | NAACL-HLT 2019 tutorials session is organized to give conference attendees a comprehensive introduction to a topic of importance drawn from our rapidly growing and changing research field from expert researchers. |
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Findings of the Association for Computational Linguistics: EMNLP 2025 (2025.findings-emnlp)
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It’s Morphin’ Time! Combating Linguistic Discrimination with Inflectional Perturbations (2020.acl-main)
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| Challenge: | Existing work on societal bias in NLP focuses on race and gender . linguistic background is a unique attribute that has been largely ignored in the field . |
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