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1
Enhancing Cross-lingual Prompting with Mask Token Augmentation ...
Zhou, Meng; Li, Xin; Jiang, Yue. - : arXiv, 2022
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2
Cross-lingual Aspect-based Sentiment Analysis with Aspect Term Code-Switching ...
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3
Towards Multi-Sense Cross-Lingual Alignment of Contextual Embeddings ...
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4
Knowledge Based Multilingual Language Model ...
Liu, Linlin; Li, Xin; He, Ruidan. - : arXiv, 2021
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5
MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER ...
Zhou, Ran; Li, Xin; He, Ruidan. - : arXiv, 2021
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6
Multilingual AMR Parsing with Noisy Knowledge Distillation ...
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7
GlobalWoZ: Globalizing MultiWoZ to Develop Multilingual Task-Oriented Dialogue Systems ...
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8
Multi-perspective Coherent Reasoning for Helpfulness Prediction of Multimodal Reviews ...
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9
On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation ...
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10
Towards Generative Aspect-Based Sentiment Analysis ...
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11
Argument Pair Extraction via Attention-guided Multi-Layer Multi-Cross Encoding ...
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12
Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction ...
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13
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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14
Unsupervised Cross-lingual Adaptation for Sequence Tagging and Beyond ...
Li, Xin; Bing, Lidong; Zhang, Wenxuan. - : arXiv, 2020
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15
Dynamic Topic Tracker for KB-to-Text Generation
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16
Transferable End-to-End Aspect-based Sentiment Analysis with Selective Adversarial Learning ...
Li, Zheng; Li, Xin; Wei, Ying. - : arXiv, 2019
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17
A knowledge regularized hierarchical approach for emotion cause analysis
Gui, Lin; Bing, Lidong; Xu, Ruifeng. - : Association for Computational Linguistics, 2019
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18
Neural Rating Regression with Abstractive Tips Generation for Recommendation ...
Li, Piji; Wang, Zihao; Ren, Zhaochun. - : arXiv, 2017
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19
Reader-Aware Multi-Document Summarization via Sparse Coding ...
Li, Piji; Bing, Lidong; Lam, Wai. - : arXiv, 2015
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20
Abstractive Multi-Document Summarization via Phrase Selection and Merging ...
Bing, Lidong; Li, Piji; Liao, Yi. - : arXiv, 2015
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