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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; Huang, Kuan-Hao; Peng, Nanyun. - : Underline Science Inc., 2021 Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.126/ Abstract: Pre-trained multilingual language encoders, such as multilingual BERT and XLM-R, show great potential for zero-shot cross-lingual transfer. However, these multilingual encoders do not precisely align words and phrases across languages. Especially, learning alignments in the multilingual embedding space usually requires sentence-level or word-level parallel corpora, which are expensive to be obtained for low-resource languages. An alternative is to make the multilingual encoders more robust; when fine-tuning the encoder using downstream task, we train the encoder to tolerate noise in the contextual embedding spaces such that even if the representations of different languages are not aligned well, the model can still achieve good performance on zero-shot cross-lingual transfer. In this work, we propose a learning strategy for training robust models by drawing connections between adversarial examples and the failure cases of zero-shot ... Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Machine translation; Natural Language Processing URL: https://underline.io/lecture/37539-improving-zero-shot-cross-lingual-transfer-learning-via-robust-training https://dx.doi.org/10.48448/vaq7-mk54 BASE Hide details 7 AESOP: Paraphrase Generation with Adaptive Syntactic Control ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Ma, Xuezhe; Peng, Nanyun. - : Underline Science Inc., 2021 BASE Show details

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