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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 ...
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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
Abstract: Recently, many KB-to-text generation tasks have been proposed to bridge the gap between knowledge bases and natural language by directly converting a group of knowledge base triples into human-readable sentences. However, most of the existing models suffer from the off-topic the problem, namely, the models are prone to generate some unrelated clauses that are somehow involved with certain input terms regardless of the given input data. This problem seriously degrades the quality of the generation results. In this paper, we propose a novel dynamic topic tracker for solving this problem. Different from existing models, our proposed model learns a global hidden representation for topics and recognizes the corresponding topic during each generation step. The recognized topic is used as additional information to guide the generation process and thus alleviates the off-topic problem. The experimental results show that our proposed model can enhance the performance of sentence generation and the off-topic problem is significantly mitigated.
URL: http://repository.essex.ac.uk/28980/
http://repository.essex.ac.uk/28980/1/coling2020.pdf
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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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