United Arab Emirates

EMNLP 2022

Gender Bias in Meta-Embeddings

meta-embedding

social bias

fairness

## Welcome to EMNLP 2022!
I am delighted to welcome you to EMNLP 2022! I believe this conference has been complicated beyond any precedent. Over the past year, it’s been thrilling to see the organization team approach each new puzzle with creativity and enthusiasm. We hope that those participating in Abu Dhabi as well as those joining remotely will leave the conference feeling newly inspired by the program and newly connected to our ever-growing community. Following EMNLP 2021 and major NLP conferences since, EMNLP 2022 is “hybrid,” serving both virtual and in-person participants.

Our key innovations for EMNLP 2022 include:

* EMNLP 2022 is “hybrid” in a second sense, as well: we allowed both direct and rolling review paper submissions, building on the pilot experiment of EMNLP 2021, which considered a small number of ARR submissions. 
* Familiar from NAACL but new to EMNLP, we’ve added an industry track.
* During the conference, “portals” will link virtual poster sessions to in-person conference participants during poster sessions each day.
* The first ACL-family conference in the United Arab Emirates.

 *Message from Noah A. Smith, University of Washington and Allen Institute for AI, Seattle, Washington, USA* 
***EMNLP 2022 General Chair***
 
[![](https://assets.underline.io/uploads/markdown_image/1/image/9eec7d4a287ee18c278b08229290aa83.png)](https://drive.google.com/file/d/1OlPv6QBeo62VVTughj2jkiLeyHd1WnUt/view)
 
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Welcome!
EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

workshop paper

Challenges and Opportunities in Information Manipulation Detection: An Examination of Wartime Russian Media

Encoded text representations often capture sensitive attributes about individuals (e.g., race or gender), which raise privacy concerns and can make downstream models unfair to certain groups. In this work, we propose FEDERATE, an approach that combines ideas from differential privacy and adversarial training to learn private text representations which also induces fairer models. We empirically evaluate the trade-off between the privacy of the representations and the fairness and accuracy of the downstream model on four NLP datasets. Our results show that FEDERATE consistently improves upon previous methods, and thus suggest that privacy and fairness can positively reinforce each other.

Fair NLP Models with Differentially Private Text Encoders

An increasingly prevalent problem for intelligent technologies is text safety, as uncontrolled systems may generate recommendations to their users that lead to injury or life-threatening consequences. However, the degree of explicitness of a generated statement that can cause physical harm varies. In this paper, we distinguish types of text that can lead to physical harm and establish one particularly underexplored category: covertly unsafe text. Then, we further break down this category with respect to the system's information and discuss solutions to mitigate the generation of text in each of these subcategories. Ultimately, our work defines the problem of covertly unsafe language that causes physical harm and argues that this subtle yet dangerous issue needs to be prioritized by stakeholders and regulators. We highlight mitigation strategies to inspire future researchers to tackle this challenging problem and help improve safety within smart systems.

Mitigating Covertly Unsafe Text within Natural Language Systems

Reasoning is central to human intelligence. However, fallacious arguments are common, and some exacerbate problems such as spreading misinformation about climate change. In this paper, we propose the task of logical fallacy detection, and provide a new dataset (Logic) of logical fallacies generally found in text, together with an additional challenge set for detecting logical fallacies in climate change claims (LogicClimate). Detecting logical fallacies is a hard problem as the model must understand the underlying logical structure of the argument. We find that existing pretrained large language models perform poorly on this task. In contrast, we show that a simple structure-aware classifier outperforms the best language model by 5.46% F1 scores on Logic and 4.51% on LogicClimate. We encourage future work to explore this task since (a) it can serve as a new reasoning challenge for language models, and (b) it can have potential applications in tackling the spread of misinformation. Our dataset and code are available at https:// github.com/causalNLP/logical-fallacy

Logical Fallacy Detection

Contrastive explanation methods go beyond transparency and address the contrastive aspect of explanations. Such explanations are emerging as an attractive option to provide actionable change to scenarios adversely impacted by classifiers' decisions. However, their extension to textual data is under-explored and there is little investigation on their vulnerabilities and limitations. 

This work motivates textual counterfactuals by highlighting the social limitations of non-contrastive explainability. We also lay the ground for a novel evaluation scheme inspired by the faithfulness of explanations. Accordingly, we extend the computation of three metrics, proximity, connectedness and stability, to textual data and we benchmark two successful contrastive methods, POLYJUICE and MiCE, on our suggested metrics. Experiments on sentiment analysis data show that the connectedness of counterfactuals to their original counterparts is not obvious in both models. More interestingly, the generated contrastive texts are more attainable with POLYJUICE which highlights the significance of latent representations in counterfactual search. Finally, we perform the first semantic adversarial attack on textual recourse methods. The results demonstrate the robustness of POLYJUICE and the role that latent input representations play in robustness and reliability.

Beyond Model Interpretability: On the Faithfulness and Adversarial Robustness of Contrastive Textual Explanations

The successful completion of the hate speech detection task hinges upon the availability of rich and variable labeled data, which is hard to obtain. In this work, we present a new approach for data augmentation that uses as input real unlabelled data, which is carefully selected from online platforms where invited hate speech is abundant. We show that by harvesting and processing this data (in an automatic manner), one can augment existing manually-labeled datasets to improve the classification performance of hate speech classification models. We observed an improvement in F1-score ranging from 2.7\% and up to 9.5\%, depending on the task (in- or cross-domain) and the model used.

HARALD: Augmenting Hate Speech Data Sets with Real Data

Text data can pose a risk of harm. However, the risks are not fully understood, and how to handle, present, and discuss harmful text in a safe way remains an unresolved issue in the NLP community. We provide an analytical framework categorising harms on three axes: (1) the harm type (e.g., misinformation, hate speech or racial stereotypes); (2) whether a harm is sought as a feature of the research design if explicitly studying harmful content (e.g., training a hate speech classifier), versus unsought if harmful content is encountered when working on unrelated problems (e.g., language generation or part-of-speech tagging); and (3) who it affects, from people (mis)represented in the data to those handling the data and those publishing on the data. We provide advice for practitioners, with concrete steps for mitigating harm in research and in publication. To assist implementation we introduce HarmCheck -- a documentation standard for handling and presenting harmful text in research.

Handling and Presenting Harmful Text in NLP Research

In recent years, there has been increased interest in building predictive models that harness natural language processing and machine learning techniques to detect emotions from various text sources, including social media posts, micro-blogs or news articles. Yet, deployment of such models in real-world sentiment and emotion applications faces challenges, in particular poor out-of-domain generalizability. This is likely due to domain-specific differences (e.g., topics, communicative goals, and annotation schemes) that make transfer between different models of emotion recognition difficult. In this work we propose approaches for text-based emotion detection that leverage transformer models (BERT and RoBERTa) in combination with Bidirectional Long Short-Term Memory (BiLSTM) networks trained on a comprehensive set of psycholinguistic features. First, we evaluate the performance of our models within-domain on two benchmark datasets: GoEmotion (Demszky et al., 2020) and ISEAR (Scherer and Wallbott, 1994). Second, we conduct transfer learning experiments on six datasets from the Unified Emotion Dataset (Bostan and Klinger, 2018) to evaluate their out-of-domain robustness. We find that the proposed hybrid models improve the ability to generalize to out-of-distribution data compared to a standard transformer-based approach. Moreover, we observe that these models perform competitively on in-domain data.'

Improving the Generalizability of Text-Based Emotion Detection by Leveraging Transformers with Psycholinguistic Features

Emotion classification in NLP assigns emotions to texts, such as
  sentences or paragraphs. With texts like "I felt guilty when he
  cried", focusing on the sentence level disregards the standpoint of
  each participant in the situation: the writer ("I") and the other
  entity ("he") could in fact have different affective states. The
  emotions of different entities have been considered only partially
  in emotion semantic role labeling, a task that relates semantic
  roles to emotion cue words.  Proposing a related task, we narrow the
  focus on the experiencers of events, and assign an emotion (if any
  holds) to each of them.  To this end, we represent each emotion both
  categorically and with appraisal variables, as a psychological
  access to explaining why a person develops a particular emotion.  On
  an event description corpus, our experiencer-aware models of
  emotions and appraisals outperform the experiencer-agnostic
  baselines, showing that disregarding event participants is an
  oversimplification for the emotion detection task.

Experiencer-Specific Emotion and Appraisal Prediction

Fertility intentions as verbalized in surveys are a poor predictor of actual fertility outcomes, the number of children people have. This can partly be explained by the uncertainty people have in their intentions. Such uncertainties are hard to capture through traditional survey questions, although open-ended questions can be used to get insight into people's subjective narratives of the future that determine their intentions. Analyzing such answers to open-ended questions can be done through Natural Language Processing techniques. Traditional topic models (e.g., LSA and LDA), however, often fail to do since they rely on co-occurrences, which are often rare in short survey responses. The aim of this study was to apply and evaluate topic models on demographic survey data. In this study, we applied neural topic models (e.g. BERTopic, CombinedTM) based on language models to responses from Dutch women on their fertility plans, and compared the topics and their coherence scores from each model to expert judgments. Our results show that neural models produce topics more in line with human interpretation compared to LDA. However, the coherence score could only partly reflect on this, depending on the corpus used for calculation. This research is important because, first, it helps us develop more informed strategies on model selection and evaluation for topic modeling on survey data; and second, it shows that the field of demography has much to gain from adopting NLP methods.

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