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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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Towards Better UD Parsing: Deep Contextualized Word Embeddings, Ensemble, and Treebank Concatenation ...
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Abstract:
This paper describes our system (HIT-SCIR) submitted to the CoNLL 2018 shared task on Multilingual Parsing from Raw Text to Universal Dependencies. We base our submission on Stanford's winning system for the CoNLL 2017 shared task and make two effective extensions: 1) incorporating deep contextualized word embeddings into both the part of speech tagger and parser; 2) ensembling parsers trained with different initialization. We also explore different ways of concatenating treebanks for further improvements. Experimental results on the development data show the effectiveness of our methods. In the final evaluation, our system was ranked first according to LAS (75.84%) and outperformed the other systems by a large margin. ... : System description paper of our system (HIT-SCIR) for the CoNLL 2018 shared task on Universal Dependency parsing, which was ranked first in the LAS evaluation. Fix typos and grammar errors. Add the results of parser without ensemble ...
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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.1807.03121 https://arxiv.org/abs/1807.03121
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