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Generating Authentic Adversarial Examples beyond Meaning-preserving with Doubly Round-trip Translation ...
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Conditional Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation ...
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ConSLT: A Token-level Contrastive Framework for Sign Language Translation ...
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A Simple Multi-Modality Transfer Learning Baseline for Sign Language Translation ...
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Focus on the Target's Vocabulary: Masked Label Smoothing for Machine Translation ...
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USTC-NELSLIP at SemEval-2022 Task 11: Gazetteer-Adapted Integration Network for Multilingual Complex Named Entity Recognition ...
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Towards the Next 1000 Languages in Multilingual Machine Translation: Exploring the Synergy Between Supervised and Self-Supervised Learning ...
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GL-CLeF: A Global-Local Contrastive Learning Framework for Cross-lingual Spoken Language Understanding ...
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Delving Deeper into Cross-lingual Visual Question Answering ...
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Multi-Level Contrastive Learning for Cross-Lingual Alignment ...
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Cross-Lingual Text Classification with Multilingual Distillation and Zero-Shot-Aware Training ...
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HFL at SemEval-2022 Task 8: A Linguistics-inspired Regression Model with Data Augmentation for Multilingual News Similarity ...
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Controllable Natural Language Generation with Contrastive Prefixes ...
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Abstract:
To guide the generation of large pretrained language models (LM), previous work has focused on directly fine-tuning the language model or utilizing an attribute discriminator. In this work, we propose a novel lightweight framework for controllable GPT2 generation, which utilizes a set of small attribute-specific vectors, called prefixes, to steer natural language generation. Different from prefix-tuning, where each prefix is trained independently, we take the relationship among prefixes into consideration and train multiple prefixes simultaneously. We propose a novel supervised method and also an unsupervised method to train the prefixes for single-aspect control while the combination of these two methods can achieve multi-aspect control. Experimental results on both single-aspect and multi-aspect control show that our methods can guide generation towards the desired attributes while keeping high linguistic quality. ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/2202.13257 https://dx.doi.org/10.48550/arxiv.2202.13257
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SGL: Symbolic Goal Learning in a Hybrid, Modular Framework for Human Instruction Following ...
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Local-Global Context Aware Transformer for Language-Guided Video Segmentation ...
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Automatic Speech Recognition Datasets in Cantonese: A Survey and New Dataset ...
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