Trends in ACL 2020

ACL 2020 runs virtual. Thanks to the format, I was able to listen to many talk sessions that would otherwise be held in parallel, and was able to observe some trends from a collection of ~150 papers I took notes about. This blog presents these trends, and the note is attached below.

  1. Deep learning is at the center
  2. Dialog gets exponentially more looks
  3. NLP impacts the society
  4. Linguistics and cognitive psychology needs attentions
  5. We need to understand what happened in NLP

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Tested Positive. Do I Have It?

As the antibody tests of SARS-CoV-2 are being carried out, and people start to explain (e.g., on Twitter) what is means to test positive, I want to elaborate on a concept that most elementary probability textbook have mentioned, since people may be overly anxious (or too careless) about a positive result.
The estimate of how many people are positive in your region (prior) affects a lot the estimated chances of you having it, given you tested positive (posterior).

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Deconfounding Age Impacts

This is a project done while interning at Winterlight Labs. We identify the problem of age being confounded into dementia detection with linguistic features, propose to use fair representation learning to address it, and propose to evaluate with a modified equalized odd score.
On two datasets, DementiaBank and Famous People, our best methods outperform traditional statistical adjustments (residualization and inverse probability weighting), and are comparable to the theoretical upper bound.

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