The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Deep Learning for Joint Source-Channel Coding of Text
Open accessAbstract
We consider the problem of joint source and channel coding of structured data such as natural language over a noisy channel. The typical approach to this problem in both theory and practice involves performing source coding to first compress the text and then channel coding to add robustness for the transmission across the channel. This approach is optimal in terms of minimizing end-to-end distortion with arbitrarily large block lengths of both the source and channel codes when transmission is over discrete memoryless channels. However, the optimality of this approach is no longer ensured for documents of finite length and limitations on the length of the encoding. We will show in this scenario that we can achieve lower word error rates by developing a deep learning based encoder and decoder. While the approach of separate source and channel coding would minimize bit error rates, our approach preserves semantic information of sentences by first embedding sentences in a semantic space where sentences closer in meaning are located closer together, and then performing joint source and channel coding on these embeddings.
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Metadata
- Authors
- Farsad, N., Rao, M., & Goldsmith, A.
- Venue
- 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2018-04
- Pages
- 2326-2330
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W2788661346
- Cited by
- 438
- References
- 20
- FWCI
- 17.34
- Type
- conference paper
- Language
- en
- Open access
- green
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- Farsad, N., Rao, M., & Goldsmith, A. (2018). Deep Learning for Joint Source-Channel Coding of Text. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). https://doi.org/10.1109/icassp.2018.8461983
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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