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1
Improving Pre-trained Language Models with Syntactic Dependency Prediction Task for Chinese Semantic Error Recognition ...
Sun, Bo; Wang, Baoxin; Che, Wanxiang. - : arXiv, 2022
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2
ExpMRC: explainability evaluation for machine reading comprehension
In: Heliyon (2022)
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3
Multilingual multi-aspect explainability analyses on machine reading comprehension models
In: iScience (2022)
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4
Multilingual Multi-Aspect Explainability Analyses on Machine Reading Comprehension Models ...
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5
Allocating Large Vocabulary Capacity for Cross-lingual Language Model Pre-training ...
Zheng, Bo; Dong, Li; Huang, Shaohan. - : arXiv, 2021
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6
Chase: A Large-Scale and Pragmatic Chinese Dataset for Cross-Database Context-Dependent Text-to-SQL ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.180 Abstract: The cross-database context-dependent Text-to-SQL (XDTS) problem has attracted considerable attention in recent years due to its wide range of potential applications. However, we identify two biases in existing datasets for XDTS: (1) a high proportion of context-independent questions and (2) a high proportion of easy SQL queries. These biases conceal the major challenges in XDTS to some extent. In this work, we present Chase, a large-scale and pragmatic Chinese dataset for XDTS. It consists of 5,459 coherent question sequences (17,940 questions with their SQL queries annotated) over 280 databases, in which only 35% of questions are context-independent, and 28% of SQL queries are easy. We experiment on Chase with three state-of-the-art XDTS approaches. The best approach only achieves an exact match accuracy of 40% over all questions and 16% over all question sequences, indicating that Chase highlights the challenging problems of XDTS. We ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Pragmaliguistics; Pragmatics; Semantics
URL: https://underline.io/lecture/25503-chase-a-large-scale-and-pragmatic-chinese-dataset-for-cross-database-context-dependent-text-to-sql
https://dx.doi.org/10.48448/yc7a-yd72
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7
GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling ...
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8
A Closer Look into the Robustness of Neural Dependency Parsers Using Better Adversarial Examples ...
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9
Dynamic Connected Networks for Chinese Spelling Check ...
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10
Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling ...
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11
Neural Stylistic Response Generation with Disentangled Latent Variables ...
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12
Guided Generation of Cause and Effect ...
Li, Zhongyang; Ding, Xiao; Liu, Ting. - : arXiv, 2021
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13
Language learners' enjoyment and emotion regulation in online collaborative learning
Zhang, Zhipeng (S34844); Liu, Ting; Lee, Chweebeng (R17032). - : U.K., Elsevier, 2021
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14
Canonicalizing Open Knowledge Bases with Multi-Layered Meta-Graph Neural Network ...
Jiang, Tianwen; Zhao, Tong; Qin, Bing. - : arXiv, 2020
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15
TableGPT: Few-shot Table-to-Text Generation with Table Structure Reconstruction and Content Matching ...
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16
N-LTP: An Open-source Neural Language Technology Platform for Chinese ...
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17
Cross-Lingual Machine Reading Comprehension ...
Cui, Yiming; Che, Wanxiang; Liu, Ting. - : arXiv, 2019
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18
Neural recovery machine for chinese dropped pronoun [<Journal>]
Zhang, Weinan [Verfasser]; Liu, Ting [Verfasser]; Yin, Qingyu [Verfasser].
DNB Subject Category Language
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19
CoNLL 2018 Shared Task System Outputs
Zeman, Daniel; Potthast, Martin; Duthoo, Elie. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2018
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20
Towards Better UD Parsing: Deep Contextualized Word Embeddings, Ensemble, and Treebank Concatenation ...
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