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Register a new userERICA: An Empathetic Android Companion for Covid-19 Quarantine
إيريكا: رفيق أندرويد متعاطيك للحجر الصحي Covid-19
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خلال العام الماضي، تم تسريع البحوث في مختلف المجالات، بما في ذلك معالجة اللغة الطبيعية (NLP)، لمكافحة جائحة CovID-19، لكن هذا البحث قد بدأت للتو على أنظمة الحوار.في هذه الورقة، نقدم نظام حوار نهاية إلى نهاية يهدف إلى تخفيف عزل الناس في العزل النفسي.نقوم بإجراء تجربة محاكاة التحكم لتقييم آثار واجهة المستخدم: وكيل ظاهري يستند إلى الويب، نورا مقابل أندرويد إيريكا عبر مكالمة فيديو.تظهر النتائج التجريبية أن Android يمكن أن يقدم تجربة مستخدم أكثر قيمة من خلال إعطاء انطباعا عن كونك أكثر تعاطفا ومشاركة في المحادثة بسبب معلوماته غير اللفظية، مثل تعبيرات الوجه وإيماءات الجسم.
Over the past year, research in various domains, including Natural Language Processing (NLP), has been accelerated to fight against the COVID-19 pandemic, yet such research has just started on dialogue systems. In this paper, we introduce an end-to-end dialogue system which aims to ease the isolation of people under self-quarantine. We conduct a control simulation experiment to assess the effects of the user interface: a web-based virtual agent, Nora vs. the android ERICA via a video call. The experimental results show that the android can offer a more valuable user experience by giving the impression of being more empathetic and engaging in the conversation due to its nonverbal information, such as facial expressions and body gestures.
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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 int
eractively 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.
This paper presents the preliminary results of an ongoing project that analyzes the growing body of scientific research published around the COVID-19 pandemic. In this research, a general-purpose semantic model is used to double annotate a batch of 5
00 sentences that were manually selected from the CORD-19 corpus. Afterwards, a baseline text-mining pipeline is designed and evaluated via a large batch of 100,959 sentences. We present a qualitative analysis of the most interesting facts automatically extracted and highlight possible future lines of development. The preliminary results show that general-purpose semantic models are a useful tool for discovering fine-grained knowledge in large corpora of scientific documents.
In this paper, we present ArCOV-19, an Arabic COVID-19 Twitter dataset that spans one year, covering the period from 27th of January 2020 till 31st of January 2021. ArCOV-19 is the first publicly-available Arabic Twitter dataset covering COVID-19 pan
demic that includes about 2.7M tweets alongside the propagation networks of the most-popular subset of them (i.e., most-retweeted and -liked). The propagation networks include both retweetsand conversational threads (i.e., threads of replies). ArCOV-19 is designed to enable research under several domains including natural language processing, information retrieval, and social computing. Preliminary analysis shows that ArCOV-19 captures rising discussions associated with the first reported cases of the disease as they appeared in the Arab world.In addition to the source tweets and the propagation networks, we also release the search queries and the language-independent crawler used to collect the tweets to encourage the curation of similar datasets.
We present a COVID-19 news dashboard which visualizes sentiment in pandemic news coverage in different languages across Europe. The dashboard shows analyses for positive/neutral/negative sentiment and moral sentiment for news articles across countrie
s and languages. First we extract news articles from news-crawl. Then we use a pre-trained multilingual BERT model for sentiment analysis of news article headlines and a dictionary and word vectors -based method for moral sentiment analysis of news articles. The resulting dashboard gives a unified overview of news events on COVID-19 news overall sentiment, and the region and language of publication from the period starting from the beginning of January 2020 to the end of January 2021.
This paper provides a detailed overview of the system and its outcomes, which were produced as part of the NLP4IF Shared Task on Fighting the COVID-19 Infodemic at NAACL 2021. This task is accomplished using a variety of techniques. We used state-of-
the-art contextualized text representation models that were fine-tuned for the downstream task in hand. ARBERT, MARBERT,AraBERT, Arabic ALBERT and BERT-base-arabic were used. According to the results, BERT-base-arabic had the highest 0.784 F1 score on the test set.
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