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1
Multi-perspective Coherent Reasoning for Helpfulness Prediction of Multimodal Reviews ...
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On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation ...
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3
Towards Generative Aspect-Based Sentiment Analysis ...
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4
Argument Pair Extraction via Attention-guided Multi-Layer Multi-Cross Encoding ...
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Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction ...
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MulDA: A Multilingual Data Augmentation Framework for Low-Resource Cross-Lingual NER ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.453 Abstract: Named Entity Recognition (NER) for low-resource languages is a both practical and challenging research problem. This paper addresses zero-shot transfer for cross-lingual NER, especially when the amount of source-language training data is also limited. The paper first proposes a simple but effective labeled sequence translation method to translate source-language training data to target languages and avoids problems such as word order change and entity span determination. With the source-language data as well as the translated data, a generation-based multilingual data augmentation method is introduced to further increase diversity by generating synthetic labeled data in multiple languages. These augmented data enable the language model based NER models to generalize better with both the language-specific features from the target-language synthetic data and the language-independent features from multilingual synthetic data. An extensive set ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://underline.io/lecture/25894-mulda-a-multilingual-data-augmentation-framework-for-low-resource-cross-lingual-ner
https://dx.doi.org/10.48448/rt5q-3e62
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