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ELI5: Long Form Question Answering.

, , , , , and . ACL (1), page 3558-3567. Association for Computational Linguistics, (2019)

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Training with Quantization Noise for Extreme Model Compression, , , , , , and . (2020)cite arxiv:2004.07320.Pay Less Attention with Lightweight and Dynamic Convolutions., , , , and . ICLR, OpenReview.net, (2019)Generative Question Answering: Learning to Answer the Whole Question., and . ICLR (Poster), OpenReview.net, (2019)Controllable Abstractive Summarization., , and . NMT@ACL, page 45-54. Association for Computational Linguistics, (2018)fairseq: A Fast, Extensible Toolkit for Sequence Modeling., , , , , , , and . NAACL-HLT (Demonstrations), page 48-53. Association for Computational Linguistics, (2019)A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation., , , , , , , , , and 35 other author(s). NAACL-HLT, page 3053-3070. Association for Computational Linguistics, (2022)Training with Quantization Noise for Extreme Model Compression., , , , , , and . ICLR, OpenReview.net, (2021)Hierarchical Neural Story Generation., , and . ACL (1), page 889-898. Association for Computational Linguistics, (2018)Do Explanations Help Users Detect Errors in Open-Domain QA? An Evaluation of Spoken vs. Visual Explanations., , , , , and . ACL/IJCNLP (Findings), volume ACL/IJCNLP 2021 of Findings of ACL, page 1103-1116. Association for Computational Linguistics, (2021)ELI5: Long Form Question Answering., , , , , and . ACL (1), page 3558-3567. Association for Computational Linguistics, (2019)