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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 ...
Abstract: Recently, some E-commerce sites launch a new interaction box called Tips on their mobile apps. Users can express their experience and feelings or provide suggestions using short texts typically several words or one sentence. In essence, writing some tips and giving a numerical rating are two facets of a user's product assessment action, expressing the user experience and feelings. Jointly modeling these two facets is helpful for designing a better recommendation system. While some existing models integrate text information such as item specifications or user reviews into user and item latent factors for improving the rating prediction, no existing works consider tips for improving recommendation quality. We propose a deep learning based framework named NRT which can simultaneously predict precise ratings and generate abstractive tips with good linguistic quality simulating user experience and feelings. For abstractive tips generation, gated recurrent neural networks are employed to "translate" user and item ... : SIGIR 2017 ...
Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences; Information Retrieval cs.IR
URL: https://arxiv.org/abs/1708.00154
https://dx.doi.org/10.48550/arxiv.1708.00154
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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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