Papers by Ananya Ganesh
What Would a Teacher Do? Predicting Future Talk Moves (2021.findings-acl)
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| Challenge: | Recent advances in natural language processing (NLP) have the ability to transform how classroom learning takes place. |
| Approach: | They propose a task that uses the academically productive talk framework to learn strategies that make for the best learning experience. |
| Outcome: | The proposed task outperforms baselines on academically productive talk (FTMP) and shows that it outperformed human performance on FTMP. |
Mind the Gap between the Application Track and the Real World (2023.acl-short)
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| Challenge: | Recent advances in NLP have led to a rise in inter-disciplinary and application-oriented research. |
| Approach: | They examine the relationship between motivations described in NLP papers and models and evaluations which comprise the proposed solution. |
| Outcome: | The proposed solution improves educational dialog understanding system when used in a realistic classroom environment. |
CHIA: CHoosing Instances to Annotate for Machine Translation (2022.findings-emnlp)
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| Challenge: | Neural machine translation systems perform poorly on low-resource language pairs, for which large-scale parallel data is unavailable. |
| Approach: | They propose a method for selecting instances to annotate for machine translation using existing multi-way parallel datasets. |
| Outcome: | The proposed method outperforms unsupervised methods on 20 languages and a multi-way parallel dataset on high-resource languages. |
Energy and Policy Considerations for Deep Learning in NLP (P19-1)
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| Challenge: | Recent advances in hardware and methodology for training neural networks have enabled significant accuracy improvements across many NLP tasks. |
| Approach: | They quantify the approximate financial and environmental costs of training neural network models . they propose actionable recommendations to reduce costs and improve equity in NLP research . |
| Outcome: | The proposed recommendations address the cost and environmental costs of training neural networks for NLP. |
Don’t Rule Out Monolingual Speakers: A Method For Crowdsourcing Machine Translation Data (2021.acl-short)
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| Challenge: | High-performing machine translation systems require large amounts of training data in the form of parallel sentences, and translators are difficult to find and expensive. |
| Approach: | They propose a data collection strategy which uses graphics interchange formats (GIFs) as a pivot to collect parallel sentences from monolingual annotators. |
| Outcome: | The proposed method collects parallel sentences from monolingual annotators in Hindi, Tamil and English. |