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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 Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Gheini, Mozhdeh; May, Jonathan. - : Underline Science Inc., 2021 BASE Show details 2 RICA: Evaluating Robust Inference Capabilities Based on Commonsense Axioms ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Ho, Daniel; Khanna, Rahul. - : Underline Science Inc., 2021 BASE Show details 3 RockNER: A Simple Method to Create Adversarial Examples for Evaluating the Robustness of Named Entity Recognition Models ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Gao, Wenyang; Lin, Yuchen. - : 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 Discretized Integrated Gradients for Explaining Language Models ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Ren, Xiang; Sanyal, Soumya. - : Underline Science Inc., 2021 BASE Show details 6 Lawyers are Dishonest? Quantifying Representational Harms in Commonsense Knowledge Resources ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Galstyan, Aram; Mehrabi, Ninareh. - : Underline Science Inc., 2021 BASE Show details 7 Extract, Denoise and Enforce: Evaluating and Improving Concept Preservation for Text-to-Text Generation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Han, Jiawei; Mao, Yuning; Ren, Xiang. - : Underline Science Inc., 2021 Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.413/ Abstract: Prior studies on text-to-text generation typically assume that the model could figure out what to attend to in the input and what to include in the output via seq2seq learning, with only the parallel training data and no additional guidance. However, it remains unclear whether current models can preserve important concepts in the source input, as seq2seq learning does not have explicit focus on the concepts and commonly used evaluation metrics also treat concepts equally important as other tokens. In this paper, we present a systematic analysis that studies whether current seq2seq models, especially pre-trained language models, are good enough for preserving important input concepts and to what extent explicitly guiding generation with the concepts as lexical constraints is beneficial. We answer the above questions by conducting extensive analytical experiments on four representative text-to-text generation tasks. Based on the ... Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Text Generation URL: https://dx.doi.org/10.48448/zxwn-5w24 https://underline.io/lecture/37354-extract,-denoise-and-enforce-evaluating-and-improving-concept-preservation-for-text-to-text-generation BASE Hide details

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