There are well-meaning efforts to address ethics that will likely make the world a better place, but care needs to be taken to avoid repeating mistakes of the past. In particular, ACL has recently introduced a new process where there are special reviews of some papers for ethics. We would be more comfortable with the new ethics process if there were more checks and balances, due process and transparency. Otherwise, there is a risk that the process could intimidate authors in ways that are not that dissimilar from the ways that academics were intimidated during the Cold War on both sides of the Iron Curtain.

Sentiment analysis is directly affected by compositional phenomena in language that act on the prior polarity of the words and phrases found in the text. Negation is the most prevalent of these phenomena, and in order to correctly predict sentiment, a classifier must be able to identify negation and disentangle the effect that its scope has on the final polarity of a text. This paper proposes a multi-task approach to explicitly incorporate information about negation in sentiment analysis, which we show outperforms learning negation implicitly in an end-to-end manner. We describe our approach, a cascading and hierarchical neural architecture with selective sharing of Long Short-term Memory layers, and show that explicitly training the model with negation as an auxiliary task helps improve the main task of sentiment analysis. The effect is demonstrated across several different standard English-language data sets for both tasks, and we analyze several aspects of our system related to its performance, varying types and amounts of input data and different multi-task setups.