Using multiple ASR hypotheses to boost i18n NLU performance

Charith Peris, Gokmen Oz, Khadige Abboud, Venkata sai Varada, Prashan Wanigasekara, Haidar Khan. Using multiple ASR hypotheses to boost i18n NLU performance. In Pushpak Bhattacharyya, Dipti Misra Sharma, Rajeev Sangal, editors, Proceedings of the 17th International Conference on Natural Language Processing, ICON 2020, Indian Institute of Technology Patna, Patna, India, December 18-21, 2020. pages 30-39, NLP Association of India (NLPAI), 2020. [doi]

@inproceedings{PerisOAVWK20,
  title = {Using multiple ASR hypotheses to boost i18n NLU performance},
  author = {Charith Peris and Gokmen Oz and Khadige Abboud and Venkata sai Varada and Prashan Wanigasekara and Haidar Khan},
  year = {2020},
  url = {https://aclanthology.org/2020.icon-main.5},
  researchr = {https://researchr.org/publication/PerisOAVWK20},
  cites = {0},
  citedby = {0},
  pages = {30-39},
  booktitle = {Proceedings of the 17th International Conference on Natural Language Processing, ICON 2020, Indian Institute of Technology Patna, Patna, India, December 18-21, 2020},
  editor = {Pushpak Bhattacharyya and Dipti Misra Sharma and Rajeev Sangal},
  publisher = {NLP Association of India (NLPAI)},
}