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        <title>Coding Textual Inputs Boosts the Accuracy of Neural Networks</title>
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        <namePart type="given">Abdul</namePart>
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    <name type="personal">
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            <title>Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)</title>
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            <namePart type="given">Bonnie</namePart>
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    <abstract>Natural Language Processing (NLP) tasks are usually performed word by word on textual inputs. We can use arbitrary symbols to represent the linguistic meaning of a word and use these symbols as inputs. As “alternatives” to a text representation, we introduce Soundex, MetaPhone, NYSIIS, logogram to NLP, and develop fixed-output-length coding and its extension using Huffman coding. Each of those codings combines different character/digital sequences and constructs a new vocabulary based on codewords. We find that the integration of those codewords with text provides more reliable inputs to Neural-Network-based NLP systems through redundancy than text-alone inputs. Experiments demonstrate that our approach outperforms the state-of-the-art models on the application of machine translation, language modeling, and part-of-speech tagging. The source code is available at https://github.com/abdulrafae/coding_nmt.</abstract>
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%0 Conference Proceedings
%T Coding Textual Inputs Boosts the Accuracy of Neural Networks
%A Khan, Abdul Rafae
%A Xu, Jia
%A Sun, Weiwei
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F khan-etal-2020-coding
%X Natural Language Processing (NLP) tasks are usually performed word by word on textual inputs. We can use arbitrary symbols to represent the linguistic meaning of a word and use these symbols as inputs. As “alternatives” to a text representation, we introduce Soundex, MetaPhone, NYSIIS, logogram to NLP, and develop fixed-output-length coding and its extension using Huffman coding. Each of those codings combines different character/digital sequences and constructs a new vocabulary based on codewords. We find that the integration of those codewords with text provides more reliable inputs to Neural-Network-based NLP systems through redundancy than text-alone inputs. Experiments demonstrate that our approach outperforms the state-of-the-art models on the application of machine translation, language modeling, and part-of-speech tagging. The source code is available at https://github.com/abdulrafae/coding_nmt.
%R 10.18653/v1/2020.emnlp-main.104
%U https://aclanthology.org/2020.emnlp-main.104/
%U https://doi.org/10.18653/v1/2020.emnlp-main.104
%P 1350-1360

Markdown (Informal)

[Coding Textual Inputs Boosts the Accuracy of Neural Networks](https://aclanthology.org/2020.emnlp-main.104/) (Khan et al., EMNLP 2020)

• Coding Textual Inputs Boosts the Accuracy of Neural Networks (Khan et al., EMNLP 2020)

ACL

• Abdul Rafae Khan, Jia Xu, and Weiwei Sun. 2020. Coding Textual Inputs Boosts the Accuracy of Neural Networks. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1350–1360, Online. Association for Computational Linguistics.