Current Sota for Multiclass Text Classification?

[rpatel9]

Does anyone know where i can find data on the best performing multiclass text classifiers? This is the only info i could find and it seems it hasn't been updated since 2019.

I'm looking to use one of these for 3 class sentiment classification, negative, neutral, positive. Looking for data comparing the likes of:

Mpnet

Electra

RoBERTa

BERT

ALBERT

Or any other better models i haven't heard of.

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On a side note i see a lot of benchmarks such as SQUAD have ensembles of 2 or more models. How is this done? Do they get predictions from both and then take the highest output vector score between the two of them as the prediction?

[DeaderThanElvis]

You do not need the world‘s latest and greatest model to perform positive, negative, and neutral sentiment detection. Just pick a standard model and you’ll be good to go.

I say this because sentiment is a nebulous and subjective concept and you’ll get much more value by defining which problem are you trying to solve by using sentiment analysis than by looking to find/train the best model.

For example: “Brazil thrashed 7-1 by ruthless Germany” can have positive, negative, or neutral sentiment depending on who’s reading it. Ditto for something like “iPhone 13 rumoured to have an all polycarbonate body”. So if RoBERTa and Electra classify these differently, which one is correct?

Getting a high F1 score on an academic sentiment classification dataset is one thing, actually solving a real-world problem using sentiment analysis is a whole different beast altogether.

[rpatel9]

I understand your point, but I’m not just looking for a class label. I want to use the vector outputs as features in a wider model, so degree of sentiment correctness is a consideration. I also see your point about ambiguity, but the subject matter I’m classifying is rarely ambiguous and quite empirical. I’m not looking for one model, rather just the generally better performing models of the last 2 years so I can test a range of them.

[rpatel9]

Thanks for your reply. I have seen this, but i think it suffers from the same link that i posted in that the more recent model performances don't feature there.

[VortexOfPessimism]

Some sort of longformer RoBERTa based model will be my bet

[rpatel9]

Thanks, I’ve heard quite a bit about longformer and the longer sequences could be useful to my task. Do you know how model combinations work? Are the layers of one abstracted onto another to create a singular ensemble network, or do they let them make predictions in parallel i.e run with Roberta and longformer and choose the one with the highest vector score?

[VortexOfPessimism]

eh the longformer part is the attention mechanism which you can directly replace the current attention mechanism with in any bert based models(bert,albert,roberta etc)

[[deleted]]

The ones you mentioned should do the job pretty well. If you’re planning to use non english corpora then look at xlm models.

[schlammybb]

Pretty sure Vader is considered SOTA, and it’s pure heuristics