ymoslem/acl-abstracts huggingface dataset:
COVID-19: A Semantic-Based Pipeline for Recommending Biomedical Entities (2020)

With the increasing number of publications about COVID-19, it is a challenge to extract personalized knowledge suitable for each researcher. This work aims to build a new semantic-based pipeline for recommending biomedical entities to scientific researchers. To this end, we developed a pipeline that creates an implicit feedback matrix based on Named Entity Recognition (NER) on a corpus of documents, using multidisciplinary ontologies for recognizing and linking the entities. Our hypothesis is that by using ontologies from different fields in the NER phase, we can improve the results for state-of-the-art collaborative-filtering recommender systems applied to the dataset created. The tests performed using the COVID-19 Open Research Dataset (CORD-19) dataset show that when using four ontologies, the results for precision@k, for example, reach the 80{\%}, whereas when using only one ontology, the results for precision@k drops to 20{\%}, for the same users. Furthermore, the use of multi-fields entities may help in the discovery of new items, even if the researchers do not have items from that field in their set of preferences.
Vapur: A Search Engine to Find Related Protein - Compound Pairs in COVID-19 Literature (2020)

Coronavirus Disease of 2019 (COVID-19) created dire consequences globally and triggered an intense scientific effort from different domains. The resulting publications created a huge text collection in which finding the studies related to a biomolecule of interest is challenging for general purpose search engines because the publications are rich in domain specific terminology. Here, we present Vapur: an online COVID-19 search engine specifically designed to find related protein - chemical pairs. Vapur is empowered with a relation-oriented inverted index that is able to retrieve and group studies for a query biomolecule with respect to its related entities. The inverted index of Vapur is automatically created with a BioNLP pipeline and integrated with an online user interface. The online interface is designed for the smooth traversal of the current literature by domain researchers and is publicly available at \url{https://tabilab.cmpe.boun.edu.tr/vapur/}.
Knowledge Discovery in COVID-19 Research Literature (2020)

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 500 sentences that were manually selected by the researchers 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.
Identifying pandemic-related stress factors from social-media posts -- Effects on students and young-adults (2020)

The COVID-19 pandemic has thrown natural life out of gear across the globe. Strict measures are deployed to curb the spread of the virus that is causing it, and the most effective of them have been social isolation. This has led to wide-spread gloom and depression across society but more so among the young and the elderly. There are currently more than 200 million college students in 186 countries worldwide, affected due to the pandemic. The mode of education has changed suddenly, with the rapid adaptation of e-learning, whereby teaching is undertaken remotely and on digital platforms. This study presents insights gathered from social media posts that were posted by students and young adults during the COVID times. Using statistical and NLP techniques, we analyzed the behavioural issues reported by users themselves in their posts in depression related communities on Reddit. We present methodologies to systematically analyze content using linguistic techniques to find out the stress-inducing factors. Online education, losing jobs, isolation from friends and abusive families emerge as key stress factors
Tracking And Understanding Public Reaction During COVID-19: Saudi Arabia As A Use Case (2020)

The coronavirus disease of 2019 (COVID-19) has a huge impact on economies and societies around the world. While governments are taking extreme measures to reduce the spread of the virus, people are getting affected by these new measures. With restrictions like lockdown and social distancing, it became important to understand the emotional response of the public towards the pandemic. In this paper, we study the reaction of Saudi Arabia citizens towards the pandemic. We utilize a collection of Arabic tweets that were sent during 2020, primarily through hashtags that were originated from Saudi Arabia. Our results showed that people had kept a positive reaction towards the pandemic. This positive reaction was at its highest at the beginning of the COVID-19 crisis and started to decline as time passes. Overall, the results showed that people were so supportive of each other through this pandemic. This research can help researchers and policymakers in understanding the emotional effect of a pandemic on societies.
Characterizing drug mentions in COVID-19 Twitter Chatter (2020)

Since the classification of COVID-19 as a global pandemic, there have been many attempts to treat and contain the virus. Although there is no specific antiviral treatment recommended for COVID-19, there are several drugs that can potentially help with symptoms. In this work, we mined a large twitter dataset of 424 million tweets of COVID-19 chatter to identify discourse around drug mentions. While seemingly a straightforward task, due to the informal nature of language use in Twitter, we demonstrate the need of machine learning alongside traditional automated methods to aid in this task. By applying these complementary methods, we are able to recover almost 15{\%} additional data, making misspelling handling a needed task as a pre-processing step when dealing with social media data.
Content analysis of Persian/Farsi Tweets during COVID-19 pandemic in Iran using NLP (2020)

Iran, along with China, South Korea, and Italy was among the countries that were hit hard in the first wave of the COVID-19 spread. Twitter is one of the widely-used online platforms by Iranians inside and abroad for sharing their opinion, thoughts, and feelings about a wide range of issues. In this study, using more than 530,000 original tweets in Persian/Farsi on COVID-19, we analyzed the topics discussed among users, who are mainly Iranians, to gauge and track the response to the pandemic and how it evolved over time. We applied a combination of manual annotation of a random sample of tweets and topic modeling tools to classify the contents and frequency of each category of topics. We identified the top 25 topics among which living experience under home quarantine emerged as a major talking point. We additionally categorized the broader content of tweets that shows satire, followed by news, is the dominant tweet type among Iranian users. While this framework and methodology can be used to track public response to ongoing developments related to COVID-19, a generalization of this framework can become a useful framework to gauge Iranian public reaction to ongoing policy measures or events locally and internationally.
Annotating the Pandemic: Named Entity Recognition and Normalisation in COVID-19 Literature (2020)

The COVID-19 pandemic has been accompanied by such an explosive increase in media coverage and scientific publications that researchers find it difficult to keep up. We are presenting a publicly available pipeline to perform named entity recognition and normalisation in parallel to help find relevant publications and to aid in downstream NLP tasks such as text summarisation. In our approach, we are using a dictionary-based system for its high recall in conjunction with two models based on BioBERT for their accuracy. Their outputs are combined according to different strategies depending on the entity type. In addition, we are using a manually crafted dictionary to increase performance for new concepts related to COVID-19. We have previously evaluated our work on the CRAFT corpus, and make the output of our pipeline available on two visualisation platforms.
AskMe: A LAPPS Grid-based NLP Query and Retrieval System for Covid-19 Literature (2020)

In a recent project, the Language Application Grid was augmented to support the mining of scientific publications. The results of that ef- fort have now been repurposed to focus on Covid-19 literature, including modification of the LAPPS Grid {\textquotedblleft}AskMe{\textquotedblright} query and retrieval engine. We describe the AskMe system and discuss its functionality as compared to other query engines available to search covid-related publications.
Concept Wikification for COVID-19 (2020)

Understanding scientific articles related to COVID-19 requires broad knowledge about concepts such as symptoms, diseases and medicine. Given the very large and ever-growing scientific articles related to COVID-19, it is a daunting task even for experts to recognize the large set of concepts mentioned in these articles. In this paper, we address the problem of concept wikification for COVID-19, which is to automatically recognize mentions of concepts related to COVID-19 in text and resolve them into Wikipedia titles. We develop an approach to curate a COVID-19 concept wikification dataset by mining Wikipedia text and the associated intra-Wikipedia links. We also develop an end-to-end system for concept wikification for COVID-19. Preliminary experiments show very encouraging results. Our dataset, code and pre-trained model are available at github.com/panlybero/Covid19{\_}wikification.
Developing a Curated Topic Model for COVID-19 Medical Research Literature (2020)

Topic models can facilitate search, navigation, and knowledge discovery in large document collections. However, automatic generation of topic models can produce results that fail to meet the needs of users. We advocate for a set of user-focused desiderata in topic modeling for the COVID-19 literature, and describe an effort in progress to develop a curated topic model for COVID-19 articles informed by subject matter expertise and the way medical researchers engage with medical literature.
Collecting Verified COVID-19 Question Answer Pairs (2020)

We release a dataset of over 2,100 COVID19 related Frequently asked Question-Answer pairs scraped from over 40 trusted websites. We include an additional 24, 000 questions pulled from online sources that have been aligned by experts with existing answered questions from our dataset. This paper describes our efforts in collecting the dataset and summarizes the resulting data. Our dataset is automatically updated daily and available at \url{https://github.com/JHU-COVID-QA/} scraping-qas. So far, this data has been used to develop a chatbot providing users information about COVID-19. We encourage others to build analytics and tools upon this dataset as well.
A Comprehensive Dictionary and Term Variation Analysis for COVID-19 and SARS-CoV-2 (2020)

The number of unique terms in the scientific literature used to refer to either SARS-CoV-2 or COVID-19 is remarkably large and has continued to increase rapidly despite well-established standardized terms. This high degree of term variation makes high recall identification of these important entities difficult. In this manuscript we present an extensive dictionary of terms used in the literature to refer to SARS-CoV-2 and COVID-19. We use a rule-based approach to iteratively generate new term variants, then locate these variants in a large text corpus. We compare our dictionary to an extensive collection of terminological resources, demonstrating that our resource provides a substantial number of additional terms. We use our dictionary to analyze the usage of SARS-CoV-2 and COVID-19 terms over time and show that the number of unique terms continues to grow rapidly. Our dictionary is freely available at \url{https://github.com/ncbi-nlp/CovidTermVar}.
Using the Poly-encoder for a COVID-19 Question Answering System (2020)

To combat misinformation regarding COVID- 19 during this unprecedented pandemic, we propose a conversational agent that answers questions related to COVID-19. We adapt the Poly-encoder (Humeau et al., 2020) model for informational retrieval from FAQs. We show that after fine-tuning, the Poly-encoder can achieve a higher F1 score. We make our code publicly available for other researchers to use.
Weibo-COV: A Large-Scale COVID-19 Social Media Dataset from Weibo (2020)

With the rapid development of COVID-19 around the world, people are requested to maintain {\textquotedblleft}social distance{\textquotedblright} and {\textquotedblleft}stay at home{\textquotedblright}. In this scenario, extensive social interactions transfer to cyberspace, especially on social media platforms like Twitter and Sina Weibo. People generate posts to share information, express opinions and seek help during the pandemic outbreak, and these kinds of data on social media are valuable for studies to prevent COVID-19 transmissions, such as early warning and outbreaks detection. Therefore, in this paper, we release a novel and fine-grained large-scale COVID-19 social media dataset collected from Sina Weibo, named Weibo-COV, contains more than 40 million posts ranging from December 1, 2019 to April 30, 2020. Moreover, this dataset includes comprehensive information nuggets like post-level information, interactive information, location information, and repost network. We hope this dataset can promote studies of COVID-19 from multiple perspectives and enable better and rapid researches to suppress the spread of this pandemic.
Detecting Emerging Symptoms of COVID-19 using Context-based Twitter Embeddings (2020)

In this paper, we present an iterative graph-based approach for the detection of symptoms of COVID-19, the pathology of which seems to be evolving. More generally, the method can be applied to finding context-specific words and texts (e.g. symptom mentions) in large imbalanced corpora (e.g. all tweets mentioning {\#}COVID-19). Given the novelty of COVID-19, we also test if the proposed approach generalizes to the problem of detecting Adverse Drug Reaction (ADR). We find that the approach applied to Twitter data can detect symptom mentions substantially before to their being reported by the Centers for Disease Control (CDC).
Hate and Toxic Speech Detection in the Context of Covid-19 Pandemic using XAI: Ongoing Applied Research (2020)

As social distancing, self-quarantines, and travel restrictions have shifted a lot of pandemic conversations to social media so does the spread of hate speech. While recent machine learning solutions for automated hate and offensive speech identification are available on Twitter, there are issues with their interpretability. We propose a novel use of learned feature importance which improves upon the performance of prior state-of-the-art text classification techniques, while producing more easily interpretable decisions. We also discuss both technical and practical challenges that remain for this task.
Real-time Classification, Geolocation and Interactive Visualization of COVID-19 Information Shared on Social Media to Better Understand Global Developments (2020)

As people communicate on social media during COVID-19, it can be an invaluable source of useful and up-to-date information. However, the large volume and noise-to-signal ratio of social media can make this impractical. We present a prototype dashboard for the real-time classification, geolocation and interactive visualization of COVID-19 tweets that addresses these issues. We also describe a novel L2 classification layer that outperforms linear layers on a dataset of respiratory virus tweets.
Modulated Fusion using Transformer for Linguistic-Acoustic Emotion Recognition (2020)

This paper aims to bring a new lightweight yet powerful solution for the task of Emotion Recognition and Sentiment Analysis. Our motivation is to propose two architectures based on Transformers and modulation that combine the linguistic and acoustic inputs from a wide range of datasets to challenge, and sometimes surpass, the state-of-the-art in the field. To demonstrate the efficiency of our models, we carefully evaluate their performances on the IEMOCAP, MOSI, MOSEI and MELD dataset. The experiments can be directly replicated and the code is fully open for future researches.
Multimodal Speech Recognition with Unstructured Audio Masking (2020)

Visual context has been shown to be useful for automatic speech recognition (ASR) systems when the speech signal is noisy or corrupted. Previous work, however, has only demonstrated the utility of visual context in an unrealistic setting, where a fixed set of words are systematically masked in the audio. In this paper, we simulate a more realistic masking scenario during model training, called RandWordMask, where the masking can occur for any word segment. Our experiments on the Flickr 8K Audio Captions Corpus show that multimodal ASR can generalize to recover different types of masked words in this unstructured masking setting. Moreover, our analysis shows that our models are capable of attending to the visual signal when the audio signal is corrupted. These results show that multimodal ASR systems can leverage the visual signal in more generalized noisy scenarios.
Building a Bridge: A Method for Image-Text Sarcasm Detection Without Pretraining on Image-Text Data (2020)

Sarcasm detection in social media with text and image is becoming more challenging. Previous works of image-text sarcasm detection were mainly to fuse the summaries of text and image: different sub-models read the text and image respectively to get the summaries, and fuses the summaries. Recently, some multi-modal models based on the architecture of BERT are proposed such as ViLBERT. However, they can only be pretrained on the image-text data. In this paper, we propose an image-text model for sarcasm detection using the pretrained BERT and ResNet without any further pretraining. BERT and ResNet have been pretrained on much larger text or image data than image-text data. We connect the vector spaces of BERT and ResNet to utilize more data. We use the pretrained Multi-Head Attention of BERT to model the text and image. Besides, we propose a 2D-Intra-Attention to extract the relationships between words and images. In experiments, our model outperforms the state-of-the-art model.
A Benchmark for Structured Procedural Knowledge Extraction from Cooking Videos (2020)

Watching instructional videos are often used to learn about procedures. Video captioning is one way of automatically collecting such knowledge. However, it provides only an indirect, overall evaluation of multimodal models with no finer-grained quantitative measure of what they have learned. We propose instead, a benchmark of structured procedural knowledge extracted from cooking videos. This work is complementary to existing tasks, but requires models to produce interpretable structured knowledge in the form of verb-argument tuples. Our manually annotated open-vocabulary resource includes 356 instructional cooking videos and 15,523 video clip/sentence-level annotations. Our analysis shows that the proposed task is challenging and standard modeling approaches like unsupervised segmentation, semantic role labeling, and visual action detection perform poorly when forced to predict every action of a procedure in a structured form.
IESTAC: English-Italian Parallel Corpus for End-to-End Speech-to-Text Machine Translation (2020)

We discuss a set of methods for the creation of IESTAC: a English-Italian speech and text parallel corpus designed for the training of end-to-end speech-to-text machine translation models and publicly released as part of this work. We first mapped English LibriVox audiobooks and their corresponding English Gutenberg Project e-books to Italian e-books with a set of three complementary methods. Then we aligned the English and the Italian texts using both traditional Gale-Church based alignment methods and a recently proposed tool to perform bilingual sentences alignment computing the cosine similarity of multilingual sentence embeddings. Finally, we forced the alignment between the English audiobooks and the English side of our textual parallel corpus with a text-to-speech and dynamic time warping based forced alignment tool. For each step, we provide the reader with a critical discussion based on detailed evaluation and comparison of the results of the different methods.
Unsupervised Keyword Extraction for Full-Sentence VQA (2020)

In the majority of the existing Visual Question Answering (VQA) research, the answers consist of short, often single words, as per instructions given to the annotators during dataset construction. This study envisions a VQA task for natural situations, where the answers are more likely to be sentences rather than single words. To bridge the gap between this natural VQA and existing VQA approaches, a novel unsupervised keyword extraction method is proposed. The method is based on the principle that the full-sentence answers can be decomposed into two parts: one that contains new information answering the question (i.e. keywords), and one that contains information already included in the question. Discriminative decoders were designed to achieve such decomposition, and the method was experimentally implemented on VQA datasets containing full-sentence answers. The results show that the proposed model can accurately extract the keywords without being given explicit annotations describing them.
MAST: Multimodal Abstractive Summarization with Trimodal Hierarchical Attention (2020)

This paper presents MAST, a new model for Multimodal Abstractive Text Summarization that utilizes information from all three modalities {--} text, audio and video {--} in a multimodal video. Prior work on multimodal abstractive text summarization only utilized information from the text and video modalities. We examine the usefulness and challenges of deriving information from the audio modality and present a sequence-to-sequence trimodal hierarchical attention-based model that overcomes these challenges by letting the model pay more attention to the text modality. MAST outperforms the current state of the art model (video-text) by 2.51 points in terms of Content F1 score and 1.00 points in terms of Rouge-L score on the How2 dataset for multimodal language understanding.
Towards End-to-End In-Image Neural Machine Translation (2020)

In this paper, we offer a preliminary investigation into the task of in-image machine translation: transforming an image containing text in one language into an image containing the same text in another language. We propose an end-to-end neural model for this task inspired by recent approaches to neural machine translation, and demonstrate promising initial results based purely on pixel-level supervision. We then offer a quantitative and qualitative evaluation of our system outputs and discuss some common failure modes. Finally, we conclude with directions for future work.