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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
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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 ...
Abstract: We propose an abstraction-based multi-document summarization framework that can construct new sentences by exploring more fine-grained syntactic units than sentences, namely, noun/verb phrases. Different from existing abstraction-based approaches, our method first constructs a pool of concepts and facts represented by phrases from the input documents. Then new sentences are generated by selecting and merging informative phrases to maximize the salience of phrases and meanwhile satisfy the sentence construction constraints. We employ integer linear optimization for conducting phrase selection and merging simultaneously in order to achieve the global optimal solution for a summary. Experimental results on the benchmark data set TAC 2011 show that our framework outperforms the state-of-the-art models under automated pyramid evaluation metric, and achieves reasonably well results on manual linguistic quality evaluation. ... : 11 pages, 1 figure, accepted as a full paper at ACL 2015 ...
Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences
URL: https://dx.doi.org/10.48550/arxiv.1506.01597
https://arxiv.org/abs/1506.01597
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