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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 ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.206 Abstract: Pretrained contextualized embeddings are powerful word representations for structured prediction tasks. Recent work found that better word representations can be obtained by concatenating different types of embeddings. However, the selection of embeddings to form the best concatenated representation usually varies depending on the task and the collection of candidate embeddings, and the ever-increasing number of embedding types makes it a more difficult problem. In this paper, we propose Automated Concatenation of Embeddings (ACE) to automate the process of finding better concatenations of embeddings for structured prediction tasks, based on a formulation inspired by recent progress on neural architecture search. Specifically, a controller alternately samples a concatenation of embeddings, according to its current belief of the effectiveness of individual embedding types in consideration for a task, and updates the belief based on a reward. ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://underline.io/lecture/25534-automated-concatenation-of-embeddings-for-structured-prediction
https://dx.doi.org/10.48448/ysvt-pd64
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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 ...
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