Recent studies indicate that bidirectional Long Short-Term Memory (BLSTM) recurrent neural networks are well-suited for automatic emotion recognition systems and may lead to better results than systems applying other widely used classifiers such as Support Vector Machines or feedforward Neural Networks. The good performance of BLSTM emotion recognition systems could be attributed to their ability to model and exploit contextual information self-learned via recurrently connected memory blocks which allows them to incorporate information about how emotion evolves over time. However, the actual amount of bidirectional context that a BLSTM classifier takes into account when classifying an observation has not been investigated so far. This paper presents a methodology to systematically investigate the number of past and future utterance-level observations that are considered to generate an emotion prediction for a given utterance, and to examine to what extent this temporal bidirectional context contributes to the overall BLSTM performance.

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Conversational Agents (CAs) can be a proxy for disseminating information and providing support to the public, especially in times of crisis. CAs can scale to reach larger numbers of end-users than human operators, while they can offer information interactively and engagingly. In this work, we present Theano, a Greek-speaking virtual assistant for COVID-19. Theano presents users with COVID-19 statistics and facts and informs users about the best health practices as well as the latest COVID-19 related guidelines. Additionally, Theano provides support to end-users by helping them self-assess their symptoms and redirecting them to first-line health workers. The relevant, localized information that Theano provides, makes it a valuable tool for combating COVID-19 in Greece. Theano has already conversed with different users in more than 170 different conversations through a web interface as a chatbot and over the phone as a voice bot.