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Morphological Processing of Low-Resource Languages: Where We Are and What's Next ...
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Match the Script, Adapt if Multilingual: Analyzing the Effect of Multilingual Pretraining on Cross-lingual Transferability ...
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Don't Rule Out Monolingual Speakers: A Method For Crowdsourcing Machine Translation Data ...
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Findings of the LoResMT 2021 Shared Task on COVID and Sign Language for Low-resource Languages ...
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How to Adapt Your Pretrained Multilingual Model to 1600 Languages ...
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Findings of the AmericasNLP 2021 Shared Task on Open Machine Translation for Indigenous Languages of the Americas ...
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{PROST}: {P}hysical Reasoning about Objects through Space and Time ...
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Don't Rule Out Monolingual Speakers: A Method For Crowdsourcing Machine Translation Data ...
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What Would a Teacher Do? {P}redicting Future Talk Moves ...
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Abstract:
Read paper: https://www.aclanthology.org/2021.findings-acl.418 Abstract: Recent advances in natural language processing (NLP) have the ability to transform how classroom learning takes place. Combined with the increasing integration of technology in today's classrooms, NLP systems leveraging question answering and dialog processing techniques can serve as private tutors or participants in classroom discussions to increase student engagement and learning. To progress towards this goal, we use the classroom discourse framework of academically productive talk (APT) to learn strategies that make for the best learning experience. In this paper, we introduce a new task, called future talk move prediction (FTMP): it consists of predicting the next talk move -- an utterance strategy from APT -- given a conversation history with its corresponding talk moves. We further introduce a neural network model for this task, which outperforms multiple baselines by a large margin. Finally, we compare our model's performance on ...
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Keyword:
Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Neural Network; Semantics
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URL: https://underline.io/lecture/26509-what-would-a-teacher-doquestion-predicting-future-talk-moves https://dx.doi.org/10.48448/5nye-xk44
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How to Adapt Your Pretrained Multilingual Model to 1600 Languages ...
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AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages ...
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CLiMP: A Benchmark for Chinese Language Model Evaluation ...
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Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas
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In: Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas. Edited by: Mager, Manuel; Oncevay, Arturo; Rios, Annette; Meza Ruiz, Ivan Vladimir; Palmer, Alexis; Neubig, Graham; Kann, Katharina (2021). Online: Association for Computational Linguistics. (2021)
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Learning to Learn Morphological Inflection for Resource-Poor Languages ...
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Acquisition of Inflectional Morphology in Artificial Neural Networks With Prior Knowledge
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In: Proceedings of the Society for Computation in Linguistics (2020)
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Probing for Semantic Classes: Diagnosing the Meaning Content of Word Embeddings
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Probing for Semantic Classes: Diagnosing the Meaning Content of Word Embeddings ...
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Acquisition of Inflectional Morphology in Artificial Neural Networks With Prior Knowledge ...
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