In this paper, we introduce the task of political coalition signal prediction from text, that is, the task of recognizing from the news coverage leading up to an election the (un)willingness of political parties to form a government coalition.We decompose our problem into two related, but distinct tasks: (i) predicting whether a reported statement from a politician or a journalist refers to a potential coalition and (ii) predicting the polarity of the signal -namely, whether the speaker is in favour of or against the coalition.For this, we explore the benefits of multi-task learning and investigate which setup and task formulation is best suited for each sub-task.We evaluate our approach, based on hand-coded newspaper articles, covering elections in three countries (Ireland, Germany, Austria) and two languages (English, German).Our results show that the multi-task learning approach can further improve results over a strong monolingual transfer learning baseline.* election * AND ( * coalition * OR * pact * OR * collaboration * OR * alliance * ) 1The keyword-based sampling was necessary so that the coders did not spend too much time on articles that did not contain any signals.The keywords were optimised for recall by domain experts who,