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1
On Homophony and Rényi Entropy ...
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On the Role of Corpus Ordering in Language Modeling ...
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3
Evaluation of Unsupervised Automatic Readability Assessors Using Rank Correlations ...
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4
Analysis of Language Change in Collaborative Instruction Following ...
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5
Learning Feature Weights using Reward Modeling for Denoising Parallel Corpora ...
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Cross-lingual Aspect-based Sentiment Analysis with Aspect Term Code-Switching ...
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Cross-lingual Transfer for Text Classification with Dictionary-based Heterogeneous Graph ...
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NOAHQA: Numerical Reasoning with Interpretable Graph Question Answering Dataset ...
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9
Automatic Bilingual Markup Transfer ...
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10
An Unsupervised Method for Building Sentence Simplification Corpora in Multiple Languages ...
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11
Challenges in Detoxifying Language Models ...
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12
SD-QA: Spoken Dialectal Question Answering for the Real World ...
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13
Plan-then-Generate: Controlled Data-to-Text Generation via Planning ...
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14
Sparsity and Sentence Structure in Encoder-Decoder Attention of Summarization Systems ...
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15
Identity-Based Patterns in Deep Convolutional Networks: Generative Adversarial Phonology and Reduplication ...
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16
Live Session - 4E: Phonology, Morphology and Word Segmentation ...
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17
Signed Coreference Resolution ...
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18
Backtranslation in Neural Morphological Inflection ...
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19
Rule-based Morphological Inflection Improves Neural Terminology Translation ...
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20
Translating Headers of Tabular Data: A Pilot Study of Schema Translation ...
Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.5/ Abstract: Schema translation is the task of automatically translating headers of tabular data from one language to another. High-quality schema translation plays an important role in cross-lingual table searching, understanding and analysis. Despite its importance, schema translation is not well studied in the community, and state-of-the-art neural machine translation models cannot work well on this task because of two intrinsic differences between plain text and tabular data: morphological difference and context difference. To facilitate the research study, we construct the first parallel dataset for schema translation, which consists of 3,158 tables with 11,979 headers written in 6 different languages, including English, Chinese, French, German, Spanish, and Japanese. Also, we propose the first schema translation model called CAST, which is a header-to-header neural machine translation model augmented with schema context. Specifically, we ...
Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Machine translation; Natural Language Processing
URL: https://underline.io/lecture/37570-translating-headers-of-tabular-data-a-pilot-study-of-schema-translation
https://dx.doi.org/10.48448/e3w6-dn36
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