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        <title>Converting the Point of View of Messages Spoken to Virtual Assistants</title>
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    <abstract>Virtual Assistants can be quite literal at times. If the user says “tell Bob I love him,” most virtual assistants will extract the message “I love him” and send it to the user‘s contact named Bob, rather than properly converting the message to “I love you.” We designed a system to allow virtual assistants to take a voice message from one user, convert the point of view of the message, and then deliver the result to its target user. We developed a rule-based model, which integrates a linear text classification model, part-of-speech tagging, and constituency parsing with rule-based transformation methods. We also investigated Neural Machine Translation (NMT) approaches, including LSTMs, CopyNet, and T5. We explored 5 metrics to gauge both naturalness and faithfulness automatically, and we chose to use BLEU plus METEOR for faithfulness and relative perplexity using a separately trained language model (GPT) for naturalness. Transformer-Copynet and T5 performed similarly on faithfulness metrics, with T5 achieving slight edge, a BLEU score of 63.8 and a METEOR score of 83.0. CopyNet was the most natural, with a relative perplexity of 1.59. CopyNet also has 37 times fewer parameters than T5. We have publicly released our dataset, which is composed of 46,565 crowd-sourced samples.</abstract>
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%0 Conference Proceedings
%T Converting the Point of View of Messages Spoken to Virtual Assistants
%A Lee, Gunhee
%A Zu, Vera
%A Buddi, Sai Srujana
%A Liang, Dennis
%A Kulkarni, Purva
%A FitzGerald, Jack
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F lee-etal-2020-converting
%X Virtual Assistants can be quite literal at times. If the user says “tell Bob I love him,” most virtual assistants will extract the message “I love him” and send it to the user‘s contact named Bob, rather than properly converting the message to “I love you.” We designed a system to allow virtual assistants to take a voice message from one user, convert the point of view of the message, and then deliver the result to its target user. We developed a rule-based model, which integrates a linear text classification model, part-of-speech tagging, and constituency parsing with rule-based transformation methods. We also investigated Neural Machine Translation (NMT) approaches, including LSTMs, CopyNet, and T5. We explored 5 metrics to gauge both naturalness and faithfulness automatically, and we chose to use BLEU plus METEOR for faithfulness and relative perplexity using a separately trained language model (GPT) for naturalness. Transformer-Copynet and T5 performed similarly on faithfulness metrics, with T5 achieving slight edge, a BLEU score of 63.8 and a METEOR score of 83.0. CopyNet was the most natural, with a relative perplexity of 1.59. CopyNet also has 37 times fewer parameters than T5. We have publicly released our dataset, which is composed of 46,565 crowd-sourced samples.
%R 10.18653/v1/2020.findings-emnlp.15
%U https://aclanthology.org/2020.findings-emnlp.15/
%U https://doi.org/10.18653/v1/2020.findings-emnlp.15
%P 154-163

Markdown (Informal)

[Converting the Point of View of Messages Spoken to Virtual Assistants](https://aclanthology.org/2020.findings-emnlp.15/) (Lee et al., Findings 2020)

• Converting the Point of View of Messages Spoken to Virtual Assistants (Lee et al., Findings 2020)

ACL

• Gunhee Lee, Vera Zu, Sai Srujana Buddi, Dennis Liang, Purva Kulkarni, and Jack FitzGerald. 2020. Converting the Point of View of Messages Spoken to Virtual Assistants. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 154–163, Online. Association for Computational Linguistics.