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You can always do multi-label as a multi-task learning model (or just a set of binary models), which is something we (and many others) have explored before! A
by ajratner 6y ago
You can always do multi-label as a multi-task learning model (or just a set of binary models), which is something we (and many others) have explored before! A lot of the adjustments for mainline Snorkel have to do with (A) the semantics of the labeling functions (need to be able to express that something is not class A and/or have a general per-class prior) and (B) all the infra to support what is just now a bunch of independent per-label binary tasks, at base
- eggie5 6y agoSnorkel has a label mutual exclusion assumption right? My core problem is a multi-label problem, but my snorkel data, from the LabelModel is inherently single-label (mutually exclusive). What is the prevailing recommendation to do multi-label w/ Snorkel? Is the below what you are currently recommending? For a given, k-wise multi-label problem: 1. Generate k binary datasets w/ LabelModel 2. Train k separate binary classifiers for each respective dataset 3. At inference/prediction time pass input though the k classifiers and get scores. Is this what the current recommendation is? Create a set of binary classifiers?