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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 ...
Abstract: Read paper: https://www.aclanthology.org/2021.findings-acl.313 Abstract: Having recognized gender bias as a major issue affecting current translation technologies, researchers have primarily attempted to mitigate it by working on the data front. However, whether algorithmic aspects concur to exacerbate unwanted outputs remains so far under-investigated. In this work, we bring the analysis on gender bias in automatic translation onto a seemingly neutral yet critical component: word segmentation. Can segmenting methods influence the ability to translate gender? Do certain segmentation approaches penalize the representation of feminine linguistic markings? We address these questions by comparing 5 existing segmentation strategies on the target side of speech translation systems. Our results on two language pairs (English-Italian/French) show that state-of-the-art subword splitting (BPE) comes at the cost of higher gender bias. In light of this finding, we propose a combined approach that preserves BPE overall ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Gender Studies; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://underline.io/lecture/26404-how-to-split-the-effect-of-word-segmentation-on-gender-bias-in-speech-translation
https://dx.doi.org/10.48448/ydta-r725
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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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