ymoslem/acl-abstracts huggingface dataset:
Benchmarking of Transformer-Based Pre-Trained Models on Social Media Text Classification Datasets (2020)

Free text data from social media is now widely used in natural language processing research, and one of the most common machine learning tasks performed on this data is classification. Generally speaking, performances of supervised classification algorithms on social media datasets are lower than those on texts from other sources, but recently-proposed transformer-based models have considerably improved upon legacy state-of-the-art systems. Currently, there is no study that compares the performances of different variants of transformer-based models on a wide range of social media text classification datasets. In this paper, we benchmark the performances of transformer-based pre-trained models on 25 social media text classification datasets, 6 of which are health-related. We compare three pre-trained language models, RoBERTa-base, BERTweet and ClinicalBioBERT in terms of classification accuracy. Our experiments show that RoBERTa-base and BERTweet perform comparably on most datasets, and considerably better than ClinicalBioBERT, even on health-related datasets.
Pandemic Literature Search: Finding Information on COVID-19 (2020)

Finding information related to a pandemic of a novel disease raises new challenges for information seeking and retrieval, as the new information becomes available gradually. We investigate how to better rank information for pandemic information retrieval. We experiment with different ranking algorithms and propose a novel end-to-end method for neural retrieval, and demonstrate its effectiveness on the TREC COVID search. This work could lead to a search system that aids scientists, clinicians, policymakers and others in finding reliable answers from the scientific literature.
Information Extraction from Legal Documents: A Study in the Context of Common Law Court Judgements (2020)

{\textquoteleft}Common Law' judicial systems follow the doctrine of precedent, which means the legal principles articulated in court judgements are binding in subsequent cases in lower courts. For this reason, lawyers must search prior judgements for the legal principles that are relevant to their case. The difficulty for those within the legal profession is that the information that they are looking for may be contained within a few paragraphs or sentences, but those few paragraphs may be buried within a hundred-page document. In this study, we create a schema based on the relevant information that legal professionals seek within judgements and perform text classification based on it, with the aim of not only assisting lawyers in researching cases, but eventually enabling large-scale analysis of legal judgements to find trends in court outcomes over time.
Convolutional and Recurrent Neural Networks for Spoken Emotion Recognition (2020)

We test four models proposed in the speech emotion recognition (SER) literature on 15 public and academic licensed datasets in speaker-independent cross-validation. Results indicate differences in the performance of the models which is partly dependent on the dataset and features used. We also show that a standard utterance-level feature set still performs competitively with neural models on some datasets. This work serves as a starting point for future model comparisons, in addition to open-sourcing the testing code.
Popularity Prediction of Online Petitions using a Multimodal DeepRegression Model (2020)

Online petitions offer a mechanism for peopleto initiate a request for change and gather sup-port from others to demonstrate support for thecause. In this work, we model the task of peti-tion popularity using both text and image rep-resentations across four different languages,and including petition metadata. We evaluateour proposed approach using a dataset of 75kpetitions from Avaaz.org, and find strong com-plementarity between text and images.
Exploring Looping Effects in RNN-based Architectures (2020)

The paper investigates repetitive loops, a common problem in contemporary text generation (such as machine translation, language modelling, morphological inflection) systems. More specifically, we conduct a study on neural models with recurrent units by explicitly altering their decoder internal state. We use a task of morphological reinflection task as a proxy to study the effects of the changes. Our results show that the probability of the occurrence of repetitive loops is significantly reduced by introduction of an extra neural decoder output. The output should be specifically trained to produce gradually increasing value upon generation of each character of a given sequence. We also explored variations of the technique and found that feeding the extra output back to the decoder amplifies the positive effects.
Transformer Semantic Parsing (2020)

In neural semantic parsing, sentences are mapped to meaning representations using encoder-decoder frameworks. In this paper, we propose to apply the Transformer architecture, instead of recurrent neural networks, to this task. Experiments in two data sets from different domains and with different levels of difficulty show that our model achieved better results than strong baselines in certain settings and competitive results across all our experiments.
Overview of the 2020 ALTA Shared Task: Assess Human Behaviour (2020)

The 2020 ALTA shared task is the 11th in stance of a series of shared tasks organised by ALTA since 2010. The task is to classify texts posted in social media according to human judgements expressed in them. The data used for this task is a subset of SemEval 2018 AIT DISC, which has been annotated by domain experts for this task. In this paper we introduce the task, describe the data and present the results of participating systems.
Automatically Predicting Judgement Dimensions of Human Behaviour (2020)

This paper describes our submission to the ALTA-2020 shared task on assessing behaviour from short text, We evaluate the effectiveness of traditional machine learning and recent transformers pre-trained models. Our submission with the Roberta-large model and prediction threshold achieved first place on the private leaderboard.
Classifying Judgements using Transfer Learning (2020)

We describe our method for classifying short texts into the APPRAISAL framework, work we conducted as part of the ALTA 2020 shared task. We tackled this problem using transfer learning. Our team, {\textquotedblleft}orangutanV2{\textquotedblright} placed equal first in the shared task, with a mean F1-score of 0.1026 on the private data set.
Human Behavior Assessment using Ensemble Models (2020)

Behavioral analysis is a pertinent step in today`s automated age. It is important to judge a statement on a variety of parameters before reaching a valid conclusion. In today`s world of technology and automation, Natural language processing tools have benefited from growing access to data in order to analyze the context and scenario. A better understanding of human behaviors would empower a range of automated tools to provide users a customized experience. For precise analysis, behavior understanding is important. We have experimented with various machine learning techniques, and have obtained a maximum private score of 0.1033 with a public score of 0.1733. The methods are described as part of the ALTA 2020 shared task. In this work, we have enlisted our results and the challenges faced to solve the problem of the human behavior assessment.
Enriching Historic Photography with Structured Data using Image Region Segmentation (2020)

Cultural institutions such as galleries, libraries, archives and museums continue to make commitments to large scale digitization of collections. An ongoing challenge is how to increase discovery and access through structured data and the semantic web. In this paper we describe a method for using computer vision algorithms that automatically detect regions of {\textquotedblleft}stuff{\textquotedblright} {---} such as the sky, water, and roads {---} to produce rich and accurate structured data triples for describing the content of historic photography. We apply our method to a collection of 1610 documentary photographs produced in the 1930s and 1940 by the FSA-OWI division of the U.S. federal government. Manual verification of the extracted annotations yields an accuracy rate of 97.5{\%}, compared to 70.7{\%} for relations extracted from object detection and 31.5{\%} for automatically generated captions. Our method also produces a rich set of features, providing more unique labels (1170) than either the captions (1040) or object detection (178) methods. We conclude by describing directions for a linguistically-focused ontology of region categories that can better enrich historical image data. Open source code and the extracted metadata from our corpus are made available as external resources.
Interlinking Iconclass Data with Concepts of Art \& Architecture Thesaurus (2020)

Iconclass, being a a well established classification system, could benefit from interconnections with other ontologies in order to semantically enrich its content. This work presents a disambiguating and interlinking approach which is used to map Iconclass Subjects to concepts of the Art and Architecture Thesaurus. In a preliminary evaluation, the system is able to produce promising predictions, though the task is highly challenging due to conceptual and schema heterogeneity. Several algorithmic improvements for this specific interlinking task, as well as and future research directions are suggestions. The produced mappings, as well as the source code and additional information can be found at \url{https://github.com/annabreit/taxonomy-interlinking}.
Toward the Automatic Retrieval and Annotation of Outsider Art images: A Preliminary Statement (2020)

The aim of this position paper is to establish an initial approach to the automatic classification of digital images about the Outsider Art style of painting. Specifically, we explore whether is it possible to classify non-traditional artistic styles by using the same features that are used for classifying traditional styles? Our research question is motivated by two facts. First, art historians state that non-traditional styles are influenced by factors {\textquotedblleft}outside{\textquotedblright} of the world of art. Second, some studies have shown that several artistic styles confound certain classification techniques. Following current approaches to style prediction, this paper utilises Deep Learning methods to encode image features. Our preliminary experiments have provided motivation to think that, as is the case with traditional styles, Outsider Art can be computationally modelled with objective means by using training datasets and CNN models. Nevertheless, our results are not conclusive due to the lack of a large available dataset on Outsider Art. Therefore, at the end of the paper, we have mapped future lines of action, which include the compilation of a large dataset of Outsider Art images and the creation of an ontology of Outsider Art.
Automatic Matching of Paintings and Descriptions in Art-Historic Archives using Multimodal Analysis (2020)

Cultural heritage data plays a pivotal role in the understanding of human history and culture. A wealth of information is buried in art-historic archives which can be extracted via digitization and analysis. This information can facilitate search and browsing, help art historians to track the provenance of artworks and enable wider semantic text exploration for digital cultural resources. However, this information is contained in images of artworks, as well as textual descriptions or annotations accompanied with the images. During the digitization of such resources, the valuable associations between the images and texts are frequently lost. In this project description, we propose an approach to retrieve the associations between images and texts for artworks from art-historic archives. To this end, we use machine learning to generate text descriptions for the extracted images on the one hand, and to detect descriptive phrases and titles of images from the text on the other hand. Finally, we use embeddings to align both, the descriptions and the images.
Towards a Comprehensive Assessment of the Quality and Richness of the Europeana Metadata of food-related Images (2020)

Semantic enrichment of historical images to build interactive AI systems for the Digital Humanities domain has recently gained significant attention. However, before implementing any semantic enrichment tool for building AI systems, it is also crucial to analyse the quality and richness of the existing datasets and understand the areas where semantic enrichment is most required. Here, we propose an approach to conducting a preliminary analysis of selected historical images from the Europeana platform using existing linked data quality assessment tools. The analysis targets food images by collecting metadata provided from curators such as Galleries, Libraries, Archives and Museums (GLAMs) and cultural aggregators such as Europeana. We identified metrics to evaluate the quality of the metadata associated with food-related images which are harvested from the Europeana platform. In this paper, we present the food-image dataset, the associated metadata and our proposed method for the assessment. The results of our assessment will be used to guide the current effort to semantically enrich the images and build high-quality metadata using Computer Vision.
Automated Extraction of Socio-political Events from News (AESPEN): Workshop and Shared Task Report (2020)

We describe our effort on automated extraction of socio-political events from news in the scope of a workshop and a shared task we organized at Language Resources and Evaluation Conference (LREC 2020). We believe the event extraction studies in computational linguistics and social and political sciences should further support each other in order to enable large scale socio-political event information collection across sources, countries, and languages. The event consists of regular research papers and a shared task, which is about event sentence coreference identification (ESCI), tracks. All submissions were reviewed by five members of the program committee. The workshop attracted research papers related to evaluation of machine learning methodologies, language resources, material conflict forecasting, and a shared task participation report in the scope of socio-political event information collection. It has shown us the volume and variety of both the data sources and event information collection approaches related to socio-political events and the need to fill the gap between automated text processing techniques and requirements of social and political sciences.
Keynote Abstract: Too soon? The limitations of AI for event data (2020)

Not all conflict datasets offer equal levels of coverage, depth, use-ability, and content. A review of the inclusion criteria, methodology, and sourcing of leading publicly available conflict datasets demonstrates that there are significant discrepancies in the output produced by ostensibly similar projects. This keynote will question the presumption of substantial overlap between datasets, and identify a number of important gaps left by deficiencies across core criteria for effective conflict data collection and analysis.
Keynote Abstract: Current Open Questions for Operational Event Data (2020)

In this brief keynote, I will address what I see as five majorissues in terms of development for operational event datasets (that is, event data intended for real time monitoringand forecasting, rather than purely for academic research).
Analyzing ELMo and DistilBERT on Socio-political News Classification (2020)

This study evaluates the robustness of two state-of-the-art deep contextual language representations, ELMo and DistilBERT, on supervised learning of binary protest news classification (PC) and sentiment analysis (SA) of product reviews. A {\textquotedblright}cross-context{\textquotedblright} setting is enabled using test sets that are distinct from the training data. The models are fine-tuned and fed into a Feed-Forward Neural Network (FFNN) and a Bidirectional Long Short Term Memory network (BiLSTM). Multinomial Naive Bayes (MNB) and Linear Support Vector Machine (LSVM) are used as traditional baselines. The results suggest that DistilBERT can transfer generic semantic knowledge to other domains better than ELMo. DistilBERT is also 30{\%} smaller and 83{\%} faster than ELMo, which suggests superiority for smaller computational training budgets. When generalization is not the utmost preference and test domain is similar to the training domain, the traditional machine learning (ML) algorithms can still be considered as more economic alternatives to deep language representations.
Text Categorization for Conflict Event Annotation (2020)

We cast the problem of event annotation as one of text categorization, and compare state of the art text categorization techniques on event data produced within the Uppsala Conflict Data Program (UCDP). Annotating a single text involves assigning the labels pertaining to at least 17 distinct categorization tasks, e.g., who were the attacking organization, who was attacked, and where did the event take place. The text categorization techniques under scrutiny are a classical Bag-of-Words approach; character-based contextualized embeddings produced by ELMo; embeddings produced by the BERT base model, and a version of BERT base fine-tuned on UCDP data; and a pre-trained and fine-tuned classifier based on ULMFiT. The categorization tasks are very diverse in terms of the number of classes to predict as well as the skeweness of the distribution of classes. The categorization results exhibit a large variability across tasks, ranging from 30.3{\%} to 99.8{\%} F-score.
TF-IDF Character N-grams versus Word Embedding-based Models for Fine-grained Event Classification: A Preliminary Study (2020)

Automating the detection of event mentions in online texts and their classification vis-a-vis domain-specific event type taxonomies has been acknowledged by many organisations worldwide to be of paramount importance in order to facilitate the process of intelligence gathering. This paper reports on some preliminary experiments of comparing various linguistically-lightweight approaches for fine-grained event classification based on short text snippets reporting on events. In particular, we compare the performance of a TF-IDF-weighted character n-gram SVM-based model versus SVMs trained on various of-the-shelf pre-trained word embeddings (GloVe, BERT, FastText) as features. We exploit a relatively large event corpus consisting of circa 610K short text event descriptions classified using a 25-event categories that cover political violence and protest events. The best results, i.e., 83.5{\%} macro and 92.4{\%} micro F1 score, were obtained using the TF-IDF-weighted character n-gram model.
Seeing the Forest and the Trees: Detection and Cross-Document Coreference Resolution of Militarized Interstate Disputes (2020)

Previous efforts to automate the detection of social and political events in text have primarily focused on identifying events described within single sentences or documents. Within a corpus of documents, these automated systems are unable to link event references{---}recognize singular events across multiple sentences or documents. A separate literature in computational linguistics on event coreference resolution attempts to link known events to one another within (and across) documents. I provide a data set for evaluating methods to identify certain political events in text and to link related texts to one another based on shared events. The data set, Headlines of War, is built on the Militarized Interstate Disputes data set and offers headlines classified by dispute status and headline pairs labeled with coreference indicators. Additionally, I introduce a model capable of accomplishing both tasks. The multi-task convolutional neural network is shown to be capable of recognizing events and event coreferences given the headlines' texts and publication dates.
Conflict Event Modelling: Research Experiment and Event Data Limitations (2020)

This paper presents the conflict event modelling experiment, conducted at the Joint Research Centre of the European Commission, particularly focusing on the limitations of the input data. This model is under evaluation as to potentially complement the Global Conflict Risk Index (GCRI), a conflict risk model supporting the design of European Union`s conflict prevention strategies. The model aims at estimating the occurrence of material conflict events, under the assumption that an increase in material conflict events goes along with a decrease in material and verbal cooperation. It adopts a Long-Short Term Memory Cell Recurrent Neural Network on country-level actor-based event datasets that indicate potential triggers to violent conflict such as demonstrations, strikes, or elections-related violence. The observed data and the outcome of the model predictions consecutively, consolidate an early warning alarm system that signals abnormal social unrest upheavals, and appears promising as an approach towards a conflict trigger model. However, event-based systems still require overcoming certain obstacles related to the quality of the input data and the event classification method.
Supervised Event Coding from Text Written in Arabic: Introducing Hadath (2020)

This article introduces Hadath, a supervised protocol for coding event data from text written in Arabic. Hadath contributes to recent efforts in advancing multi-language event coding using computer-based solutions. In this application, we focus on extracting event data about the conflict in Afghanistan from 2008 to 2018 using Arabic information sources. The implementation relies first on a Machine Learning algorithm to classify news stories relevant to the Afghan conflict. Then, using Hadath, we implement the Natural Language Processing component for event coding from Arabic script. The output database contains daily geo-referenced information at the district level on who did what to whom, when and where in the Afghan conflict. The data helps to identify trends in the dynamics of violence, the provision of governance, and traditional conflict resolution in Afghanistan for different actors over time and across space.
Protest Event Analysis: A Longitudinal Analysis for Greece (2020)

The advent of Big Data has shifted social science research towards computational methods. The volume of data that is nowadays available has brought a radical change in traditional approaches due to the cost and effort needed for processing. Knowledge extraction from heterogeneous and ample data is not an easy task to tackle. Thus, interdisciplinary approaches are necessary, combining experts of both social and computer science. This paper aims to present a work in the context of protest analysis, which falls into the scope of Computational Social Science. More specifically, the contribution of this work is to describe a Computational Social Science methodology for Event Analysis. The presented methodology is generic in the sense that it can be applied in every event typology and moreover, it is innovative and suitable for interdisciplinary tasks as it incorporates the human-in-the-loop. Additionally, a case study is presented concerning Protest Analysis in Greece over the last two decades. The conceptual foundation lies mainly upon claims analysis, and newspaper data were used in order to map, document and discuss protests in Greece in a longitudinal perspective.