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HIT - A Hierarchically Fused Deep Attention Network for Robust Code-mixed Language Representation ...
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Minimally-Supervised Morphological Segmentation using Adaptor Grammars with Linguistic Priors ...
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Bridging Subword Gaps in Pretrain-Finetune Paradigm for Natural Language Generation ...
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LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification ...
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Quotation Recommendation and Interpretation Based on Transformation from Queries to Quotations ...
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How Did This Get Funded?! Automatically Identifying Quirky Scientific Achievements ...
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Minimax and Neyman–Pearson Meta-Learning for Outlier Languages ...
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CLINE: Contrastive Learning with Semantic Negative Examples for Natural Language Understanding ...
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Towards Protecting Vital Healthcare Programs by Extracting Actionable Knowledge from Policy ...
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DYPLOC: Dynamic Planning of Content Using Mixed Language Models for Text Generation ...
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Automated Concatenation of Embeddings for Structured Prediction ...
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QASR: QCRI Aljazeera Speech Resource A Large Scale Annotated Arabic Speech Corpus ...
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Code Generation from Natural Language with Less Prior Knowledge and More Monolingual Data ...
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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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Abstract:
Read paper: https://www.aclanthology.org/2021.findings-acl.353 Abstract: We computationally model the processes of word borrowing from a donor word to an incorporated word, and vice versa, by answering two questions: (1) what does a word look like if it is incorporated into another language, and in the opposite direction (2) where did a word come from? We employ neural sequence models, focusing on six specific borrowing relations: calques, partial calques, semantic loans, phono-semantic matches, transliterations, and generic borrowings. We experiment with several model variants, including LSTM encoder-decoders, copy attention, and Transformers, along with several data distribution and preprocessing scenarios. We find that the quality of data strongly influences the performance of models, with low character edit distance between model predictions and the gold words. ...
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Keyword:
Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Neural Network; Semantics
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URL: https://underline.io/lecture/26444-sequence-models-for-computational-etymology-of-borrowings https://dx.doi.org/10.48448/xv8m-yf67
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38 |
How to Split: the Effect of Word Segmentation on Gender Bias in Speech Translation ...
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Prefix-Tuning: Optimizing Continuous Prompts for Generation ...
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Chase: A Large-Scale and Pragmatic Chinese Dataset for Cross-Database Context-Dependent Text-to-SQL ...
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