Remodeling Numerical Representation for Text Generation on Small Corpus

Citation

Tan, Aristotle and Goh, Hui Ngo and Wong, Lai Kuan (2019) Remodeling Numerical Representation for Text Generation on Small Corpus. In: 2019 2nd International Conference on Machine Learning and Natural Language Processing, 18-22 Dec. 2019, Sanya, China.

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Abstract

Data-to-text generation aims to generate natural language descriptions from non-linguistic data. Recent research on data-totext generation often uses a neural encoder-decoder architecture due to its simplicity to work across multiple domain. In this study, we aim to investigate two input encoding strategies: (1) numeral encoding as baseline, and (2) numeral as sequence-of-character tokens as proposed solution in financial data-to-text systems. An empirical study on the financial dataset validates our initial hypothesis that the character-based representation performs comparable results in content selection and diversity towards the generated text descriptions.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Natural Language Generation, Data-to-Text
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Computing and Informatics (FCI)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 15 Oct 2021 02:17
Last Modified: 15 Oct 2021 02:17
URII: http://shdl.mmu.edu.my/id/eprint/9584

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