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21
HIT - A Hierarchically Fused Deep Attention Network for Robust Code-mixed Language Representation ...
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22
Minimally-Supervised Morphological Segmentation using Adaptor Grammars with Linguistic Priors ...
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23
Bridging Subword Gaps in Pretrain-Finetune Paradigm for Natural Language Generation ...
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24
LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification ...
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25
Quotation Recommendation and Interpretation Based on Transformation from Queries to Quotations ...
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26
How Did This Get Funded?! Automatically Identifying Quirky Scientific Achievements ...
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27
Minimax and Neyman–Pearson Meta-Learning for Outlier Languages ...
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28
CLINE: Contrastive Learning with Semantic Negative Examples for Natural Language Understanding ...
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29
Towards Protecting Vital Healthcare Programs by Extracting Actionable Knowledge from Policy ...
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30
DYPLOC: Dynamic Planning of Content Using Mixed Language Models for Text Generation ...
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31
Automated Concatenation of Embeddings for Structured Prediction ...
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32
QASR: QCRI Aljazeera Speech Resource A Large Scale Annotated Arabic Speech Corpus ...
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33
Code Generation from Natural Language with Less Prior Knowledge and More Monolingual Data ...
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34
On the Distribution, Sparsity, and Inference-time Quantization of Attention Values in Transformers ...
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35
Learning Disentangled Latent Topics for Twitter Rumour Veracity Classification ...
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36
Sequence Models for Computational Etymology of Borrowings ...
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37
Scaling Within Document Coreference to Long Texts ...
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38
How to Split: the Effect of Word Segmentation on Gender Bias in Speech Translation ...
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39
Prefix-Tuning: Optimizing Continuous Prompts for Generation ...
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40
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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