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COLING 2022

Overview of the SV-Ident 2022 Shared Task on Survey Variable Identification in Social Science Publications

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**COLING**, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes but also in industrial research departments including tech-startups. COLING provides opportunities for all these communities to showcase their exciting discovery.

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COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

We are presenting an approach to the SV-Ident Shared Task which requires the detection and disambiguation of survey variables in sentences taken from social science publications. It deals with both subtasks as problems of semantic textual similarity (STS) and relies on the use of sentence transformers. Sentences and
variables are examined for semantic similarity for both detecting sentences containing variables and disambiguating the respective variables. The focus is placed on analyzing the effects of including different parts of the variables and observing the differences between English and German instances.

Variable Detection and Disambiguation Based on Semantic Similarity

Political competitions are complex settings where candidates use campaigns to promote their chances to be elected. One choice focuses on conducting a positive campaign that highlights the candidate's achievements, leadership skills, and future programs. The alternative is to focus on a negative campaign that emphasizes the \textit{negative aspects of the competing person} and is aimed at\textit{ offending opponents or the opponent’s supporters}. In this proposal, we concentrate on negative campaigns in Israeli elections. 
This work introduces an empirical case study on automatic detection of negative campaigns, using machine learning and natural language processing approaches, applied to the Hebrew-language data from Israeli municipal elections. 
Our contribution is multi-fold: (1) We provide TONIC---daTaset fOr Negative polItical Campaign in Hebrew---which consists of annotated posts from Facebook related to Israeli municipal elections; (2) We introduce results of a case study, that explored several research questions. RQ1: Which classifier and representation perform best for this task? We employed several traditional classifiers which are known for their excellent performance in IR tasks and two pre-trained models based on BERT architecture; several standard representations were employed with traditional ML models. RQ2: Does a negative campaign always contain offensive language? Can a model, trained to detect offensive language, also detect negative campaigns? We are trying to answer this question by reporting results for the transfer learning from a dataset annotated with offensive language to our dataset. 
RQ3: Does a negative campaign necessarily express negative sentiment? Can sentiment analysis help to detect negative campaigns? We experiment with sentiment labels to enrich data representation and report our findings. 

Our dataset and pre-trained models will be freely available for researchers.

Detection of Negative Campaign in Israeli Municipal Elections

Standard approaches to hate speech detection rely on sufficient available hate speech annotations. Extending previous work that repurposes natural language inference (NLI) models for zero-shot text classification, we propose a simple approach that combines multiple hypotheses to improve English NLI-based zero-shot hate speech detection. We first conduct an error analysis for vanilla NLI-based zero-shot hate speech detection and then develop four strategies based on this analysis. The strategies use multiple hypotheses to predict various aspects of an input text and combine these predictions into a final verdict. We find that the zero-shot baseline used for the initial error analysis already outperforms commercial systems and fine-tuned BERT-based hate speech detection models on HateCheck. The combination of the proposed strategies further increases the zero-shot accuracy of 79.4% on HateCheck by 7.9 percentage points (pp), and the accuracy of 69.6% on ETHOS by 10.0pp.

Hypothesis Engineering for Zero-Shot and Few-Shot Hate Speech Detection

In this presentation, we discuss an interpretable frame-
work to integrate toxic language annotations.
Most data sets address only one aspect of the
complex relationship in toxic communication
and are inconsistent with each other. Enrich-
ing annotations with more details and infor-
mation is, however, of great importance in or-
der to develop high-performing and compre-
hensive explainable language models. Such
systems should recognize and interpret both
expressions that are toxic as well as expres-
sions that make reference to specific targets to
combat toxic language. We, therefore, create
a crowd-annotation task to mark the spans of
words that refer to target communities as an ex-
tension of the HateXplain data set. We present
a quantitative and qualitative analysis of the
annotations. We also fine-tune RoBERTa-base
on our data and experiment with different USQ (Media Unit Quality Score)
thresholds to measure their effect on the clas-
sification. The F1-score of our best model on
the test set is 79%. The annotations are freely
available and can be combined with the exist-
ing HateXplain annotations to build richer and
more complete models.

Annotating targets of toxic language at the span level

Annotating abusive language is expensive, logistically complex and creates a risk of psychological harm. However, most machine learning research has prioritized maximizing effectiveness (i.e., F1 or accuracy score) rather than data efficiency (i.e., minimizing the amount of data that is annotated). In this paper, we use simulated experiments over two datasets at varying percentages of abuse to demonstrate that transformers-based active learning is a promising approach to substantially raise efficiency whilst still maintaining high effectiveness, especially when abusive content is a smaller percentage of the dataset. This approach requires a fraction of labeled data to reach performance equivalent to training over the full dataset.

Is More Data Better? Re-thinking the Importance of Efficiency in Abusive Language Detection with Transformers-Based Active Learning

Highly imbalanced textual datasets continue to pose a challenge for supervised learning models. However, viewing such imbalanced text data as an anomaly detection (AD) problem has advantages for certain tasks such as detecting hate speech, or inappropriate and/or offensive language in large social media feeds. There the unwanted content tends to be both rare and non-uniform with respect to its thematic character, and better fits the definition of an anomaly than a class. Several recent approaches to textual AD use transformer models, achieving good results but with trade-offs in pre-training and inflexibility with respect to new domains. In this paper we compare two linear models within the NMF family, which also have a recent history in textual AD. We introduce a new approach based on an alternative regularization of the NMF objective. Our results surpass other linear AD models and are on par with deep models, performing comparably well even in very small outlier concentrations.

A Lightweight yet Robust Approach to Textual Anomaly Detection

The proliferation of online hate speech has necessitated the creation of algorithms which can detect toxicity. Most of the past research focuses on this detection as a classification task, but assigning an absolute toxicity label is often tricky. Hence, few of the past works transform the same task into a regression. This paper shows the comparative evaluation of different transformers and traditional machine learning
models on a recently released toxicity severity measurement dataset by Jigsaw. We further demonstrate the issues with the model predictions using explainability analysis

Which one is more toxic? Findings from Jigsaw Rate Severity of Toxic Comments

Automated textual cyberbullying detection is known to be a challenging task. It is sometimes expected that messages associated with bullying will either be a) busive, b) targeted at a specific individual or group, or c) have a negative sentiment. Transfer learning by fine-tuning pre-trained attention-based transformer language models (LMs) has achieved near state-of-the-art (SOA) precision in identifying textual fragments as being bullying-related or not. This study looks closely at two SOA LMs, BERT and HateBERT, fine-tuned on real-life cyberbullying datasets from multiple social networking platforms. We intend to determine whether these finely calibrated pre-trained LMs learn textual cyberbullying attributes or syntactical features in the text. The results of our comprehensive experiments show that despite the fact that attention weights are drawn more strongly to syntactical features of the text at every layer, attention weights cannot completely account for the decision-making of such attention-based transformers.

Can attention-based transformers explain or interpret cyberbullying detection?

Online hate speech detection is an inherently challenging task that has recently received much attention from the natural language processing community. Despite a substantial increase in performance, considerable challenges remain and include encoding contextual information into automated hate speech detection systems. In this paper, we focus on detecting the target of hate speech in Dutch social media: whether a hateful Facebook comment is directed against migrants or not (i.e., against someone else). We manually annotate the relevant conversational context and investigate the effect of different aspects of context on performance when adding it to a Dutch transformer-based pre-trained language model, BERTje. We show that performance of the model can be significantly improved by integrating relevant contextual information.

The Role of Context in Detecting the Target of Hate Speech

Information extraction on documents still remains a challenge, especially when dealing with unstructured documents with complex and variable layouts. Graph Neural Networks seem to be a promising approach to overcome these difficulties due to their flexible and sparse nature, but they have not been exploited yet. In this work, we present a multi-level graph-based model that performs entity building and linking on unstructured documents, purely based on GNNs, and extremely light (0.3 million parameters). We also propose a novel strategy for an optimal propagation of the information between the graph levels based on hypernodes. The conducted experiments on public and private datasets demonstrate that our model is suitable for solving the tasks, and that the proposed propagation strategy is optimal and outperforms other approaches.

Overview of the SV-Ident 2022 Shared Task on Survey Variable Identification in Social Science Publications

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