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While our ultimate aim in language processing might be making fully unsupervised models that optimally resemble the human way of learning, in many areas of NLP we are still heavily working with high degrees of supervision. Aiming at sparing annotation effort, distant supervision has been explored in the past 10 years as an alternative way to obtain (noisy) training data. This obviously doesn't take us directly to unsupervised models, but in addition to being a cheaper method to labelling instances, it also keeps us closer to the original data and it might give us an indication into the extent to which we can make do with rather spontaneous signals in the data.
In the talk, I will present two experiments in the area of affective computing exploiting distant supervision: one on emotion detection, and one on stance detection. In both cases, we acquire silver labels for training leveraging user generated social media data, and play with different degrees of supervision in building our models. These are eventually tested on standard benchmarks and compared to state-of-the-art approaches. Our (mixed) results are discussed also in the light of whether supervision is truly necessary or not, and the value of silver versus gold data.
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