In natural language, the number of unique words is large but some of them tend to be highly correlated, which means significant implicit redundancy.
Example: For years, they have strived to make a ....... in the community.
Only a few possibilities out of 30,000-100,000 English words in use can fit properly in the blank.
Thus, many tasks, even those designed to test for real understanding, can be “solved” pretty well by putting together a number of relatively shallow cues. BERT and similar models learn from huge datasets (billions of bytes) and they probably capture millions of those correlations in the model. (These models are indeed wonderful accomplishments and are very useful for many things, but not ultimate solutions to true natural language understanding.)
I agree with the author that we need even better datasets evolved under public scrutiny since some of the datasets we currently have are already designed very well by their authors but the problem of designing datasets that can withstand correlation detections by DNNs (and still amenable to standardized evaluation method) could be too challenging for any single team under limited time.
I don't know if I'm just not wired up like most people, but I have real difficulty in fill in the blank tests. These kinds of tests are extremely common for language proficiency tests and I fail at them even for my native English language. For example:
For years, they have strived to make a pizza in the community. (But since they lack flour for the crust, it has not ended well). For me, I can fit nearly any noun into that sentence and imagine a viable scenario where it would be reasonable. I honestly don't know what you were getting at. For others, I am sure that it is obvious and they can tell you the few words you were imagining.
My English language ability is quite good, so I wonder why I can't perform at these kinds of tests. I also wonder if knowing why I can't do this is useful for NLP.
The way to find it is to find candidate words and try them one by one until you hear something that sounds like you've heard it a hundred times before.
> I also wonder if knowing why I can't do this is useful for NLP.
Yes. I wonder if the inability is acquired or learned or innate? Could you learn to do it? Do you have an aversion to catch phrases and well-used (hackneyed) clichés? Do you prefer to weave your own words into sentences?
I notice you use some odd prepositions in odd orders. For example in your second sentence most people would say "I have real difficulty with fill-in-the-blank tests."
You also wrote "perform at these kinds of tests," which is a place where almost all American native English speakers would use "on" rather than "at".
If I had to guess, you're much less sensitive than the average person to slight variations in word pair frequencies, and you could certainly create a test to test this hypothesis. For example you could choose any n-gram likelihood data derived from well-written English texts, and write a program to measure your ability to distinguish high- from low-frequency word pairs. This should be lower than other people in your peer group.
The lack of context does make it impossible to definitely rule out a number of candidates. If most native English speakers were forced to bet $1 million dollars and guess at the missing word written by an unknown author without further clues, I think most would seriously consider only 0.01% of words they know as a possibility.
BERT and similar models do consider a wider context window (up to 1000 lemmas if I recall correctly), which is a reason they do quite well when trying to fill in blanks for a complete passage and usually less accurately for shorter texts.
If the problem is shallow correlations, then we can use hard example mining to clean up the dataset. Train a simple model and skip all examples that are easily being solved. What remains is a harder dataset where simple correlations don't cut it.
Example: For years, they have strived to make a ....... in the community.
Only a few possibilities out of 30,000-100,000 English words in use can fit properly in the blank.
Thus, many tasks, even those designed to test for real understanding, can be “solved” pretty well by putting together a number of relatively shallow cues. BERT and similar models learn from huge datasets (billions of bytes) and they probably capture millions of those correlations in the model. (These models are indeed wonderful accomplishments and are very useful for many things, but not ultimate solutions to true natural language understanding.)
For more info, see: https://super.gluebenchmark.com/
I agree with the author that we need even better datasets evolved under public scrutiny since some of the datasets we currently have are already designed very well by their authors but the problem of designing datasets that can withstand correlation detections by DNNs (and still amenable to standardized evaluation method) could be too challenging for any single team under limited time.