Abstract Most existing work on event extraction has focused on sentence-level texts and presumes the identification of a trigger-span—a word or phrase in the input that evokes the occurrence of an event of interest. Event arguments are then extracted with respect to the trigger. Indeed, triggers are treated as integral to, and trigger detection as an essential component of, event extraction. In this paper, we provide the first investigation of the role of triggers for the more difficult and much less studied task of document-level event extraction. We analyze their usefulness in multiple end-to-end and pipelined transformer-based event extraction models for three document-level event extraction datasets, measuring performance using triggers of varying quality (human-annotated, LLM-generated, keyword-based, and random). We find that whether or not systems benefit from explicitly extracting triggers depends both on dataset characteristics (i.e., the typical number of events per document) and task-specific information available during extraction (i.e., natural language event schemas). Perhaps surprisingly, we also observe that the mere existence of triggers in the input, even random ones, is important for prompt-based in-context learning approaches to the task.

Recent years have witnessed the proliferation of offensive content online such as fake news, propaganda, misinformation, and disinformation. While initially this was mostly about textual content, over time images and videos gained popularity, as they are much easier to consume, attract more attention, and spread further than text. As a result, researchers started leveraging different modalities and combinations thereof to tackle online multimodal offensive content. In this study, we offer a survey on the state-of-the-art on multimodal disinformation detection covering various combinations of modalities: text, images, speech, video, social media network structure, and temporal information. Moreover, while some studies focused on factuality, others investigated how harmful the content is. While these two components in the definition of disinformation – (i) factuality, and (ii) harmfulness –, are equally important, they are typically studied in isolation. Thus, we argue for the need to tackle disinformation detection by taking into account multiple modalities as well as both factuality and harmfulness, in the same framework. Finally, we discuss current challenges and future research directions.

Emotion detection can provide us with a window into understanding human behavior. Due to the complex dynamics of human emotions, however, constructing annotated datasets to train automated models can be expensive. Thus, we explore the efficacy of cross-lingual approaches that would use data from a source language to build models for emotion detection in a target language. We compare three approaches, namely: i) using inherently multilingual models; ii) translating training data into the target language; and iii) using an automatically tagged parallel corpus. In our study, we consider English as the source language with Arabic and Spanish as target languages. We study the effectiveness of different classification models such as BERT and SVMs trained with different features. Our BERT-based monolingual models that are trained on target language data surpass state-of-the-art (SOTA) by 4% and 5% absolute Jaccard score for Arabic and Spanish respectively. Next, we show that using cross-lingual approaches with English data alone, we can achieve more than 90% and 80% relative effectiveness of the Arabic and Spanish BERT models respectively. Lastly, we use LIME to analyze the challenges of training cross-lingual models for different language pairs.

Dimitar Dimitrov, Bishr Bin Ali, Shaden Shaar, Firoj Alam, Fabrizio Silvestri, Hamed Firooz, Preslav Nakov, Giovanni Da San Martino. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

The reporting and the analysis of current events around the globe has expanded from professional, editor-lead journalism all the way to citizen journalism. Nowadays, politicians and other key players enjoy direct access to their audiences through social media, bypassing the filters of official cables or traditional media. However, the multiple advantages of free speech and direct communication are dimmed by the misuse of media to spread inaccurate or misleading claims. These phenomena have led to the modern incarnation of the fact-checker --- a professional whose main aim is to examine claims using available evidence and to assess their veracity. Here, we survey the available intelligent technologies that can support the human expert in the different steps of her fact-checking endeavor. These include identifying claims worth fact-checking, detecting relevant previously fact-checked claims, retrieving relevant evidence to fact-check a claim, and actually verifying a claim. In each case, we pay attention to the challenges and the potential impact on real-world fact-checking.

The recent proliferation of "fake news" has triggered a number of responses, most notably the emergence of several manual fact-checking initiatives.As a result and over time, a large number of fact-checked claims have been accumulated, which increases the likelihood that a new claim in social media or a new statement by a politician might have already been factchecked by some trusted fact-checking organization, as viral claims often come back after a while in social media, and politicians like to repeat their favorite statements, true or false, over and over again.As manual fact-checking is very time-consuming (and fully automatic fact-checking has credibility issues), it is important to try to save this effort and to avoid wasting time on claims that have already been fact-checked.Interestingly, despite the importance of the task, it has been largely ignored by the research community so far.Here, we aim to bridge this gap.In particular, we formulate the task and we discuss how it relates to, but also differs from, previous work.We further create a specialized dataset, which we release to the research community.Finally, we present learning-to-rank experiments that demonstrate sizable improvements over state-of-the-art retrieval and textual similarity approaches.

In this paper, we propose strategies to estimate the accuracy of classifiers on a dataset when resource limitations restrict the number of instances for which true labels can be obtained. Our target scenarios include situations where the classifier output labels, but no scores, e.g. when the "classifier" is not an automated classifier but an inexpert human labeller who only outputs labels. Our objective is to optimally select a subset of the data to obtain true labels for, such that they provide the best estimate of classifier accuracy. We use techniques based on stratified sampling to address this problem. However, stratified sampling poses two challenges: i) how best to stratify the data, and ii) how to allocate samples among the strata. We propose a method of stratifying data and then present two novel interactive algorithms to approximate optimal allocation of samples to the strata. Our proposed methods for stratification and allocation are seen to outperform other popular approaches to the problem.

Children and elderly separating from their family members is a common phenomenon, especially in crowded environments. In order to avoid this problem, places like Disney World and pilgrimage officials have developed systems like wearable tags to determine groups or families. These tags require information about families to be entered manually, either by the users or the facility organizers. The information, if correct, can then be used to help identify and locate a lost person's group. Manually entering information is inefficient, and usually leads to either long waiting times during entry, or partial information entry within the tags. In this paper, we propose a system that uses proximity sensing to determine groups and families without any input or interaction with the user. In our system, each user is given a wearable device that keeps track of it's neighbors using bluetooth transmissions. The system then uses this proximity data to predict cliques that represent family members.