
A Breakthrough in Temporal Expression Understanding - caente
https://x.ai/blog/the-unreasonable-effectiveness-of-neural-machine-translation-a-breakthrough-in-temporal-expression-understanding/
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marjimbel
marcos here (one of the authors). i know the word "breakthrough" in the title
is a "little" ambitious, but i really think we've done something interesting
... we'd like to publish so this is a way to collect questions/comments! soot
away.

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wistfully
Do you filter out garbage lines in emails prior to setting up for training or
you include the entire email?

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marjimbel
the memory blows up with the length of encoder sequence. for that reason we
truncate the email at ~300 tokens, which is for the vast majority of cases
enough to capture the relevant info. other than that we don't get rid of any
"garbage" lines. instead, we let the NN (eg. the attention layer) figure out
which lines are irrelevant

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wistfully
Thanks. How do you deal with threads where older emails with headers are in
the body of text. Do you just process the latest/last email.

Also: any comments about the output language and how you taught labelers this
language.

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marjimbel
we use mailgun's email thread parser. which is not perfect but good enough,
and keep only the latest email (we ignore text from older emails in thread).

figuring out the output language is where a lot of the domain knowledge comes
to play. although at the end the output language is just whatever you need it
to be for your task. it may have a very small vocab size depending on what you
are trying to "translate" to. for us it is a sequence of words and numbers
that express the various types of calendric intervals that corresponded to the
input emails. teaching human labelers this sort of task is challenging if you
want to get high-quality labels. we had a team of in-house labelers labeling
data (the human-in-the-loop) and we closely interacted with them and monitored
the quality of their data and gave them feedback through various methods. we
also perpetually improved guidelines for them. it was a pretty intense
iterative process because we wanted their labels to be as accurate as inherent
ambiguity in human language allows. so a lot of work went into that.

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wistfully
Really appreciate the responses. I'm working on a much much simpler use case
for a client that has only thousands, not millions of data points. Perhaps
your paper could simplify/shrink your data and/or output to illustrate the key
points. Cheers.

