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Improving Pre-trained Language Models with Syntactic Dependency Prediction Task for Chinese Semantic Error Recognition ...
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ExpMRC: explainability evaluation for machine reading comprehension
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In: Heliyon (2022)
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Multilingual multi-aspect explainability analyses on machine reading comprehension models
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In: iScience (2022)
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Multilingual Multi-Aspect Explainability Analyses on Machine Reading Comprehension Models ...
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Allocating Large Vocabulary Capacity for Cross-lingual Language Model Pre-training ...
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Chase: A Large-Scale and Pragmatic Chinese Dataset for Cross-Database Context-Dependent Text-to-SQL ...
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GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling ...
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A Closer Look into the Robustness of Neural Dependency Parsers Using Better Adversarial Examples ...
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Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling ...
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Neural Stylistic Response Generation with Disentangled Latent Variables ...
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Language learners' enjoyment and emotion regulation in online collaborative learning
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Canonicalizing Open Knowledge Bases with Multi-Layered Meta-Graph Neural Network ...
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TableGPT: Few-shot Table-to-Text Generation with Table Structure Reconstruction and Content Matching ...
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N-LTP: An Open-source Neural Language Technology Platform for Chinese ...
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Abstract:
We introduce \texttt{N-LTP}, an open-source neural language technology platform supporting six fundamental Chinese NLP tasks: {lexical analysis} (Chinese word segmentation, part-of-speech tagging, and named entity recognition), {syntactic parsing} (dependency parsing), and {semantic parsing} (semantic dependency parsing and semantic role labeling). Unlike the existing state-of-the-art toolkits, such as \texttt{Stanza}, that adopt an independent model for each task, \texttt{N-LTP} adopts the multi-task framework by using a shared pre-trained model, which has the advantage of capturing the shared knowledge across relevant Chinese tasks. In addition, a knowledge distillation method \cite{DBLP:journals/corr/abs-1907-04829} where the single-task model teaches the multi-task model is further introduced to encourage the multi-task model to surpass its single-task teacher. Finally, we provide a collection of easy-to-use APIs and a visualization tool to make users to use and view the processing results more easily ... : Accepted to appear in EMNLP 2021 (Demo) ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.2009.11616 https://arxiv.org/abs/2009.11616
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Towards Better UD Parsing: Deep Contextualized Word Embeddings, Ensemble, and Treebank Concatenation ...
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