Last week, Hal Daume wrote a nice post entitled “Supplanting vs Augmenting Human Language Capabilities“. Drawing an analogy between natural language processing (NLP) and robotics, he says:
I would say that most NLP research aims to supplant humans. Machine translation puts translators out of work. Summarization puts summarizers out of work (though there aren’t as many of these). Information extraction puts (one form of) informa…
Last week, Hal Daume wrote a nice post entitled “Supplanting vs Augmenting Human Language Capabilities“. Drawing an analogy between natural language processing (NLP) and robotics, he says:
I would say that most NLP research aims to supplant humans. Machine translation puts translators out of work. Summarization puts summarizers out of work (though there aren’t as many of these). Information extraction puts (one form of) information analysts out of work. Parsing puts, well… hrm…
There seems actually to be quite little in the way of trying to augment human capabilities.
He then offers possible ways that NLP might be used to augment, rather than supplant human capabilities:
- Tools for language learning.
- Interactive information retrieval.
- Adaptive tutorials.
The main tenet of HCIR is that information retrieval systems should be working with users, rather than trying to do all of the work on their own. It’s great to see a kindred spirit thinking about machine learning and NLP in the same light.
