  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.
