Discover and read the best of Twitter Threads about #ACL2021
Most recents (2)
Neat negative result spotted at #ACL2021:
I've seen a number of efforts that try to use MNLI models to do other classification tasks by checking whether the input entails statements like 'this is a negative review'. (1/...)
I've seen a number of efforts that try to use MNLI models to do other classification tasks by checking whether the input entails statements like 'this is a negative review'. (1/...)
This never really made sense. The data collection process behind SNLI/MNLI was meant to capture the relationship between two things that the same speaker could have said in the same situation.
That means strings like 'the text' or 'the premise' or 'the author' are rare in MNLI, and when they appear, they refer to something _that was referred to in the premise_, not to the premise itself or to its author.
I was surprised to see so much discussion of the boundaries of paper submission 'tracks' at this #ACL2021 panel and the business meeting. (1/?)
In my experience with *ACL events, reviewer and AC expectations don't differ in any significant or predictable way across tracks. (Plus, many other AI/ML conferences don't use tracks, and it doesn't seem like the dynamics at these conferences are meaningfully different.)
So, adding/removing/renaming tracks doesn't, on its own, seem likely to make any predictable change in outcomes.
