We consider whether traders are more likely to commit securities violations when trading at home, a new form of working induced by the Covid pandemic. We examine data pre- and post-Covid, during which some traders were unexpectedly forced to work at home. The data indicate the presence of both a treatment and a selection effect, such that those working at home exhibit fewer misconduct cases. Work at home is associated with fewer cases of trading misconduct, although no difference in communications misconduct. The economic significance of working from home on trading misconduct is large for both the treatment and selection effects.

Covid, Work-from-Home, and Securities Misconduct

Mining the causes of political decision-making is an active research area in the field of political science. In the past, most studies have focused on long-term policies that are collected over several decades of time, and have primarily relied on surveys as the main source of predictors. However, the recent COVID-19 pandemic has given rise to a new political phenomenon, where political decision-making consists of frequent short-term decisions, all on the same controlled topic---the pandemic. In this paper, we focus on the question of how public opinion influences policy decisions, while controlling for confounders such as COVID-19 case increases or unemployment rates. Using a dataset consisting of Twitter data from the 50 US states, we classify the sentiments toward governors of each state, and conduct controlled studies and comparisons. Based on the compiled samples of sentiments, policies, and confounders, we conduct causal inference to discover trends in political decision-making across different states.

Mining the Cause of Political Decision-Making from Social Media: A Case Study of COVID-19 Policies across the US States

Finding informative COVID-19 posts in a stream of tweets is very useful to monitor health-related updates. Prior work focused on a balanced data setup and on English, but informative tweets are rare, and English is only one of the many languages spoken in the world. In this work, we introduce a new dataset of 5,000 tweets for finding informative COVID-19 tweets for Danish. In contrast to prior work, which balances the label distribution, we model the problem by keeping its natural distribution.
We examine how well a simple probabilistic model and a convolutional neural network (CNN) perform on this task. We find a weighted CNN to work well but it is sensitive to embedding and hyperparameter choices. We hope the contributed dataset is a starting point for further work in this direction.

Finding the needle in a haystack: Extraction of Informative COVID-19 Danish Tweets

Internet memes have become powerful means to transmit political, psychological, and socio-cultural ideas. Although memes are typically humorous, recent days have witnessed an escalation of harmful memes used for trolling, cyberbullying, and abuse. Detecting such memes is challenging as they can be highly satirical and cryptic. Moreover, while previous work has focused on specific aspects of memes such as hate speech and propaganda, there has been little work on harm in general. Here, we aim to bridge this gap. We focus on two tasks: (i)detecting harmful memes, and (ii)identifying the social entities they target. We further extend a recently released HarMeme dataset, which covered COVID-19, with additional memes and a new topic: US politics. To solve these tasks, we propose MOMENTA (MultimOdal framework for detecting harmful MemEs aNd Their tArgets), a novel multimodal deep neural network that uses global and local perspectives to detect harmful memes. MOMENTA systematically analyzes the local and the global perspective of the input meme (in both modalities) and relates it to the background context. MOMENTA is interpretable and generalizable, and our experiments show that it outperforms several strong rivaling approaches.

MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets

This paper considers the unsupervised domain adaptation problem for neural machine translation (NMT), where we assume the access to only monolingual text in either the source or target language in the new domain. We propose a cross-lingual data selection method to extract in-domain sentences in the missing language side from a large generic monolingual corpus. Our proposed method trains an adaptive layer on top of multilingual BERT by contrastive learning to align the representation between the source and target language. This then enables the transferability of the domain classifier between the languages in a zero-shot manner. Once the in-domain data is detected by the classifier, the NMT model is then adapted to the new domain by jointly learning translation and domain discrimination tasks. We evaluate our cross-lingual data selection method on NMT across five diverse domains in three language pairs, as well as a real-world scenario of translation for COVID-19. The results show that our proposed method outperforms other selection baselines up to +1.5 BLEU score.

Generalised Unsupervised Domain Adaptation of Neural Machine Translation with Cross-Lingual Data Selection

We present a large, challenging dataset, COUGH, for COVID-19 FAQ retrieval. Similar to a standard FAQ dataset, COUGH consists of three parts: FAQ Bank, Query Bank and Relevance Set. The FAQ Bank contains ~16K FAQ items scraped from 55 credible websites (e.g., CDC and WHO). For evaluation, we introduce Query Bank and Relevance Set, where the former contains 1,236 human-paraphrased queries while the latter contains ~32 human-annotated FAQ items for each query. We analyze COUGH by testing different FAQ retrieval models built on top of BM25 and BERT, among which the best model achieves 48.8 under P@5, indicating a great challenge presented by COUGH and encouraging future research for further improvement. Our COUGH dataset is available at https://github.com/sunlab-osu/covid-faq.

COUGH: A Challenge Dataset and Models for COVID-19 FAQ Retrieval

The majority of language domains require prudent use of terminology to ensure clarity and adequacy of information conveyed. While the correct use of terminology for some languages and domains can be achieved by adapting general-purpose MT systems on large volumes of in-domain parallel data, such quantities of domain-specific data are seldom available for less-resourced languages and niche domains. Furthermore, as exemplified by COVID-19 recently, no domain-specific parallel data is readily available for emerging domains. However, the gravity of this recent calamity created a high demand for reliable translation of critical information regarding pandemic and infection prevention. This work is part of WMT2021 Shared Task: Machine Translation using Terminologies, where we describe Tilde MT systems that are capable of dynamic terminology integration at the time of translation. Our systems achieve up to 94% COVID-19 term use accuracy on the test set of the EN-FR language pair without having access to any form of in-domain information during system training.

Dynamic Terminology Integration for COVID-19 and other Emerging Domains

Neural passage retrieval is a new and promising approach in open retrieval question answering. In this work, we stress-test the Dense Passage Retriever (DPR), a state-of-the-art (SOTA) open domain neural retrieval model, on closed and specialized target domains such as COVID-19, and find that it lags behind standard BM25 in this important real-world setting. To make DPR more robust under domain shift, we explore its fine-tuning with synthetic training examples, which we generate from unlabeled target domain text using a text-to-text generator. In our experiments, this noisy but fully automated target domain supervision gives DPR a sizable advantage over BM25 in out-of-domain settings, making it a more viable model in practice. Finally, an ensemble of BM25 and our improved DPR model yields the best results, further pushing the SOTA for open retrieval QA on multiple out-of-domain test sets.

Synthetic Target Domain Supervision for Open Retrieval QA

Queer in AI’s demographic survey reveals that most queer scientists in our community do not feel completely welcome in conferences and their work environments, with the main reasons being a lack of queer community and role models. Over the past years, Queer in AI has worked towards eliminating these issues, yet we have observed that the voices of marginalized queer communities, especially transgender, non-binary folks and queer BIPOC folks have been neglected. Furthermore, the coronavirus pandemic has introduced many novel scenarios including the ubiquity of virtual conferences, with which D&I chairs may not have prior experience. Queer in AI frequently gets inquires about making virtual conferences more inclusive from both conference organizers and queer community organizers. The purpose of this document is to provide a tutorial for D&I organizers on how to make virtual conferences queer friendly.

How to Make Virtual Conferences Queer-Friendly: A Guide

With tightened restrictions in hospital settings, COVID-19 has greatly impacted expecting parents, newborns, and their families. However, no existing work has used natural language processing techniques to analyze impacts of COVID-19 from long-form text written by new parents explicitly describing their birthing experiences. This work investigates whether, and if so how, birthing narratives written by new parents on Reddit changed during COVID-19. To this aim, 4,484 Reddit posts about birthing experiences were analyzed using computational methods including persona analysis and LDA topic modeling. The results of this research indicate that, compared to their frequencies before COVID-19, the presence of partners significantly increased and that of other family members significantly decreased in the narratives during COVID-19. In addition, themes related to induced labor significantly increased in the narratives during COVID-19. This work presents a case study in how patient experience can be analyzed from their own written narratives and perspectives.

Discovering changes in birthing narratives during COVID-19

Irrespective of the success of the deep learning- based mixed-domain transfer learning approach for solving various Natural Language Processing tasks, it does not lend a generalizable solution for detecting misinformation from COVID-19 social media data. Due to the inherent complexity of this type of data, caused by its dynamic (context evolves rapidly), nuanced (misinformation types are often ambiguous), and diverse (skewed, fine-grained, and overlapping categories) nature, it is imperative for an effective model to capture both the local and global context of the target domain. By conducting a systematic investigation, we show that: (i) the deep Transformer- based pre-trained models, utilized via the mixed-domain transfer learning, are only good at capturing the local context, thus exhibits poor generalization, and (ii) a combination of shallow network-based domain-specific models and convolutional neural networks can efficiently extract local as well as global context directly from the target data in a hierarchical fashion, enabling it to offer a more generalizable solution.

Navigating the Kaleidoscope of COVID-19 Misinformation Using Deep Learning

Health and medical researchers often give clinical and policy recommendations to inform health practice and public health policy. How- ever, no current health information system supports the direct retrieval of health advice. This study fills the gap by developing and validating an NLP-based prediction model for identifying health advice in research publications. We annotated a corpus of 6,000 sentences extracted from structured abstracts in PubMed publications as “strong advice”, “weak advice”, or “no advice”, and developed a BERT-based model that can predict, with a macro-averaged F1- score of 0.93, whether a sentence gives strong advice, weak advice, or not. The prediction model generalized well to sentences in both unstructured abstracts and discussion sections, where health advice normally appears. We also conducted a case study that applied this prediction model to retrieve specific health advice on COVID-19 treatments from LitCovid, a large COVID research literature portal, demonstrating the usefulness of retrieving health advice sentences as an advanced research literature navigation function for health researchers and the general public.

Underline digital video library

Covid, Work-from-Home, and Securities Misconduct

Mining the Cause of Political Decision-Making from Social Media: A Case Study of COVID-19 Policies across the US States

Finding the needle in a haystack: Extraction of Informative COVID-19 Danish Tweets

MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets

Generalised Unsupervised Domain Adaptation of Neural Machine Translation with Cross-Lingual Data Selection

COUGH: A Challenge Dataset and Models for COVID-19 FAQ Retrieval

Dynamic Terminology Integration for COVID-19 and other Emerging Domains

Synthetic Target Domain Supervision for Open Retrieval QA

How to Make Virtual Conferences Queer-Friendly: A Guide

Discovering changes in birthing narratives during COVID-19

Navigating the Kaleidoscope of COVID-19 Misinformation Using Deep Learning

Detecting Health Advice in Medical Research Literature

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