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Quantifying the Effects of COVID-19 on Restaurant Reviews.

, , , , and . SocialNLP@NAACL, page 36-60. Association for Computational Linguistics, (2021)

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Efficient Machine Teaching Frameworks for Natural Language Processing. Columbia University, USA, (2022)Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks., , , , , , , , , and 25 other author(s). EMNLP, page 5085-5109. Association for Computational Linguistics, (2022)Weakly Supervised Attention Networks for Fine-Grained Opinion Mining and Public Health., , and . W-NUT@EMNLP, page 1-10. Association for Computational Linguistics, (2019)Detecting Foodborne Illness Complaints in Multiple Languages Using English Annotations Only., , , and . LOUHI@EMNLP, page 138-146. Association for Computational Linguistics, (2020)Item Recommendation with Variational Autoencoders and Heterogeneous Priors., , , , , and . DLRS@RecSys, page 10-14. ACM, (2018)Leveraging Just a Few Keywords for Fine-Grained Aspect Detection Through Weakly Supervised Co-Training., , and . EMNLP/IJCNLP (1), page 4610-4620. Association for Computational Linguistics, (2019)Quantifying the Effects of COVID-19 on Restaurant Reviews., , , , and . SocialNLP@NAACL, page 36-60. Association for Computational Linguistics, (2021)Self-Training with Weak Supervision., , , and . NAACL-HLT, page 845-863. Association for Computational Linguistics, (2021)Audio-Based Distributional Representations of Meaning Using a Fusion of Feature Encodings., , , , and . INTERSPEECH, page 3658-3662. ISCA, (2016)WALNUT: A Benchmark on Semi-weakly Supervised Learning for Natural Language Understanding., , , and . NAACL-HLT, page 873-899. Association for Computational Linguistics, (2022)