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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 7 of 7 1 ESTER: A Machine Reading Comprehension Dataset for Reasoning about Event Semantic Relations ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Baylon, Julia; Han, Rujun. - : Underline Science Inc., 2021 BASE Show details 2 Document-level Entity-based Extraction as Template Generation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Huang, Kung-Hsiang; Peng, Nanyun. - : Underline Science Inc., 2021 BASE Show details 3 Broaden the Vision: Geo-Diverse Visual Commonsense Reasoning ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Chang, Kai-Wei; Hu, Ziniu. - : Underline Science Inc., 2021 BASE Show details 4 ECONET: Effective Continual Pretraining of Language Models for Event Temporal Reasoning ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Han, Rujun; Peng, Nanyun. - : Underline Science Inc., 2021 BASE Show details 5 Improving Pre-trained Vision-and-Language Embeddings for Phrase Grounding ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Dou, Zi-Yi; Peng, Nanyun. - : Underline Science Inc., 2021 BASE Show details 6 Improving Zero-Shot Cross-Lingual Transfer Learning via Robust Training ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Ahmad, Wasi Uddin; Chang, Kai-Wei. - : Underline Science Inc., 2021 BASE Show details 7 AESOP: Paraphrase Generation with Adaptive Syntactic Control ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Ma, Xuezhe; Peng, Nanyun; Sun, Jiao. - : Underline Science Inc., 2021 Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.420/ Abstract: We propose to control paraphrase generation with carefully chosen target syntactic structures to generate more proper and higher-quality paraphrases. Our model, AESOP, leverages a pretrained language model and purposefully selected syntactical control via a retrieval-based selection module to generate fluent paraphrases. Experiments show that AESOP achieves state-of-the-art performances on semantic preservation and syntactic conformation on two benchmark datasets with ground-truth syntactic control from human-annotated exemplars. Moreover, with the retrieval-based target syntax selection module, AESOP generates paraphrases with even better qualities than the current best model using human-annotated target syntactic parses according to human evaluation. We further demonstrate the effectiveness of AESOP to improve classification models' robustness to syntactic perturbation by data augmentation on two GLUE tasks. ... Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing URL: https://underline.io/lecture/37911-aesop-paraphrase-generation-with-adaptive-syntactic-control https://dx.doi.org/10.48448/4d09-em33 BASE Hide details

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