ymoslem/acl-abstracts huggingface dataset:
Exploration of Contrastive Learning Strategies toward more Robust Stance Detection (2023)

Stance Detection is the task of identifying the position of an author of a text towards an issue or a target. Previous studies on Stance Detection indicate that the existing systems are non-robust to the variations and errors in input sentences. Our proposed methodology uses Contrastive Learning to learn sentence representations by bringing semantically similar sentences and sentences implying the same stance closer to each other in the embedding space. We compare our approach to a pretrained transformer model directly finetuned with the stance datasets. We use char-level and word-level adversarial perturbation attacks to measure the resilience of the models and we show that our approach achieves better performances and is more robust to the different adversarial perturbations introduced to the test data. The results indicate that our approach performs better on small-sized and class-imbalanced stance datasets.
Adapting Emotion Detection to Analyze Influence Campaigns on Social Media (2023)

Social media is an extremely potent tool for influencing public opinion, particularly during important events such as elections, pandemics, and national conflicts. Emotions are a crucial aspect of this influence, but detecting them accurately in the political domain is a significant challenge due to the lack of suitable emotion labels and training datasets. In this paper, we present a generalized approach to emotion detection that can be adapted to the political domain with minimal performance sacrifice. Our approach is designed to be easily integrated into existing models without the need for additional training or fine-tuning. We demonstrate the zero-shot and few-shot performance of our model on the 2017 French presidential elections and propose efficient emotion groupings that would aid in effectively analyzing influence campaigns and agendas on social media.
Not Just Iconic: Emoji Interpretation is Shaped by Use (2023)

Where do the meaning of emoji come from? Though it is generally assumed that emoji are fully iconic, with meanings derived from their visual forms, we argue that this is only one component of their meaning. We surveyed users and non-users of the Chinese social media platform WeChat for their interpretations of emoji specific to WeChat. We find that some emoji show significant differences in their interpretations between users and non-users, and based on how familiar a person is with the specific emoji. We argue that this reflects a more complex process for building the meaning of emoji on a platform than pure iconicity.
The Paradox of Multilingual Emotion Detection (2023)

The dominance of English is a well-known issue in NLP research. In this position paper, I turn to state-of-the-art psychological insights to explain why this problem is especially persistent in research on automatic emotion detection, and why the seemingly promising approach of using multilingual models to include lower-resourced languages might not be the desired solution. Instead, I campaign for the use of models that acknowledge linguistic and cultural differences in emotion conceptualization and verbalization. Moreover, I see much potential in NLP to better understand emotions and emotional language use across different languages.
Sadness and Anxiety Language in Reddit Messages Before and After Quitting a Job (2023)

People globally quit their jobs at high rates during the COVID-19 pandemic, yet there is scant research about emotional trajectories surrounding voluntary resignations before or during that era. To explore long-term emotional language patterns before and after quitting a job, we amassed a Reddit sample of people who indicated resigning on a specific day (n = 7,436), each of whom was paired with a comparison user matched on posting history. After excluding people on the basis of low posting frequency and word count, we analyzed 150.3 million words (53.1{\%} from 5,134 target users who indicated quitting) using SALLEE, a dictionary-based syntax-aware tool, and Linguistic Inquiry and Word Count (LIWC) dictionaries. Based on posts in the year before and after quitting, people who had quit their jobs used more sadness and anxiety language than matched comparison users. Lower rates of {\textquotedblleft}I{\textquotedblright} pronouns and cognitive processing language were associated with less sadness and anxiety surrounding quitting. Emotional trajectories during and before the pandemic were parallel, though pandemic messages were more negative. The results have relevance for strategic self-distancing as a means of regulating negative emotions around major life changes.
Communicating Climate Change: A Comparison Between Tweets and Speeches by German Members of Parliament (2023)

Twitter and parliamentary speeches are very different communication channels, but many members of parliament (MPs) make use of both. Focusing on the topic of climate change, we undertake a comparative analysis of speeches and tweets uttered by MPs in Germany in a recent six-year period. By keyword/hashtag analyses and topic modeling, we find substantial differences along party lines, with left-leaning parties discussing climate change through a crisis frame, while liberal and conservative parties try to address climate change through the lens of climate-friendly technology and practices. Only the AfD denies the need to adopt climate change mitigating measures, demeaning those concerned about a deteriorating climate as climate cult or fanatics. Our analysis reveals that climate change communication does not differ substantially between Twitter and parliamentary speeches, but across the political spectrum.
Modelling Political Aggression on Social Media Platforms (2023)

Recent years have seen a proliferation of aggressive social media posts, often wreaking even real-world consequences for victims. Aggressive behaviour on social media is especially evident during important sociopolitical events such as elections, communal incidents, and public protests. In this paper, we introduce a dataset in English to model political aggression. The dataset comprises public tweets collated across the time-frames of two of the most recent Indian general elections. We manually annotate this data for the task of aggression detection and analyze this data for aggressive behaviour. To benchmark the efficacy of our dataset, we perform experiments by fine-tuning pre-trained language models and comparing the results with models trained on an existing but general domain dataset. Our models consistently outperform the models trained on existing data. Our best model achieves a macro F1-score of 66.66 on our dataset. We also train models on a combined version of both datasets, achieving best macro F1-score of 92.77, on our dataset. Additionally, we create subsets of code-mixed and non-code-mixed data from the combined dataset to observe variations in results due to the Hindi-English code-mixing phenomenon. We publicly release the anonymized data, code, and models for further research.
Findings of WASSA 2023 Shared Task on Empathy, Emotion and Personality Detection in Conversation and Reactions to News Articles (2023)

This paper presents the results of the WASSA 2023 shared task on predicting empathy, emotion, and personality in conversations and reactions to news articles. Participating teams were given access to a new dataset from Omitaomu et al. (2022) comprising empathic and emotional reactions to news articles. The dataset included formal and informal text, self-report data, and third-party annotations. Specifically, the dataset contained news articles (where harm is done to a person, group, or other) and crowd-sourced essays written in reaction to the article. After reacting via essays, crowd workers engaged in conversations about the news articles. Finally, the crowd workers self-reported their empathic concern and distress, personality (using the Big Five), and multi-dimensional empathy (via the Interpersonal Reactivity Index). A third-party annotated both the conversational turns (for empathy, emotion polarity, and emotion intensity) and essays (for multi-label emotions). Thus, the dataset contained outcomes (self-reported or third-party annotated) at the turn level (within conversations) and the essay level. Participation was encouraged in five tracks: (i) predicting turn-level empathy, emotion polarity, and emotion intensity in conversations, (ii) predicting state empathy and distress scores, (iii) predicting emotion categories, (iv) predicting personality, and (v) predicting multi-dimensional trait empathy. In total, 21 teams participated in the shared task. We summarize the methods and resources used by the participating teams.
YNU-HPCC at WASSA-2023 Shared Task 1: Large-scale Language Model with LoRA Fine-Tuning for Empathy Detection and Emotion Classification (2023)

This paper describes the system for the YNU-HPCC team in WASSA-2023 Shared Task 1: Empathy Detection and Emotion Classification. This task needs to predict the empathy, emotion, and personality of the empathic reactions. This system is mainly based on the Decoding-enhanced BERT with disentangled attention (DeBERTa) model with parameter-efficient fine-tuning (PEFT) and the Robustly Optimized BERT Pretraining Approach (RoBERTa). Low-Rank Adaptation (LoRA) fine-tuning in PEFT is used to reduce the training parameters of large language models. Moreover, back translation is introduced to augment the training dataset. This system achieved relatively good results on the competition`s official leaderboard. The code of this system is available here.
AdityaPatkar at WASSA 2023 Empathy, Emotion, and Personality Shared Task: RoBERTa-Based Emotion Classification of Essays, Improving Performance on Imbalanced Data (2023)

This paper presents a study on using the RoBERTa language model for emotion classification of essays as part of the {\textquoteleft}Shared Task on Empathy Detection, Emotion Classification and Personality Detection in Interactions' organized as part of {\textquoteleft}WASSA 2023' at {\textquoteleft}ACL 2023'. Emotion classification is a challenging task in natural language processing, and imbalanced datasets further exacerbate this challenge. In this study, we explore the use of various data balancing techniques in combination with RoBERTa to improve the classification performance. We evaluate the performance of our approach (denoted by adityapatkar on Codalab) on a benchmark multi-label dataset of essays annotated with eight emotion categories, provided by the Shared Task organizers. Our results show that the proposed approach achieves the best macro F1 score in the competition`s training and evaluation phase. Our study provides insights into the potential of RoBERTa for handling imbalanced data in emotion classification. The results can have implications for the natural language processing tasks related to emotion classification.
Curtin OCAI at WASSA 2023 Empathy, Emotion and Personality Shared Task: Demographic-Aware Prediction Using Multiple Transformers (2023)

The WASSA 2023 shared task on predicting empathy, emotion and other personality traits consists of essays, conversations and articles in textual form and participants' demographic information in numerical form. To address the tasks, our contributions include (1) converting numerical information into meaningful text information using appropriate templates, (2) summarising lengthy articles, and (3) augmenting training data by paraphrasing. To achieve these contributions, we leveraged two separate T5-based pre-trained transformers. We then fine-tuned pre-trained BERT, DistilBERT and ALBERT for predicting empathy and personality traits. We used the Optuna hyperparameter optimisation framework to fine-tune learning rates, batch sizes and weight initialisation. Our proposed system achieved its highest performance {--} a Pearson correlation coefficient of 0.750 {--} on the onversation-level empathy prediction task1 . The system implementation is publicly available at https: //github.com/hasan-rakibul/WASSA23-empathy-emotion.
Team\_Hawk at WASSA 2023 Empathy, Emotion, and Personality Shared Task: Multi-tasking Multi-encoder based transformers for Empathy and Emotion Prediction in Conversations (2023)

In this paper, we present Team Hawk`s participation in Track 1 of the WASSA 2023 shared task. The objective of the task is to understand the empathy that emerges between individuals during their conversations. In our study, we developed a multi-tasking framework that is capable of automatically assessing empathy, intensity of emotion, and polarity of emotion within participants' conversations. Our proposed core model extends the transformer architecture, utilizing two separate RoBERTa-based encoders to encode both the articles and conversations. Subsequently, a sequence of self-attention, position-wise feed-forward, and dense layers are employed to predict the regression scores for the three sub-tasks: empathy, intensity of emotion, and polarity of emotion. Our best model achieved average Pearson`s correlation of 0.7710 (Empathy: 0.7843, Emotion Polarity: 0.7917, Emotion Intensity: 0.7381) on the released development set and 0.7250 (Empathy: 0.8090, Emotion Polarity: 0.7010, Emotion Intensity: 0.6650) on the released test set. These results earned us the 3rd position in the test set evaluation phase of Track 1.
NCUEE-NLP at WASSA 2023 Shared Task 1: Empathy and Emotion Prediction Using Sentiment-Enhanced RoBERTa Transformers (2023)

This paper describes our proposed system design for the WASSA 2023 shared task 1. We propose a unified architecture of ensemble neural networks to integrate the original RoBERTa transformer with two sentiment-enhanced RoBERTa-Twitter and EmoBERTa models. For Track 1 at the speech-turn level, our best submission achieved an average Pearson correlation score of 0.7236, ranking fourth for empathy, emotion polarity and emotion intensity prediction. For Track 2 at the essay-level, our best submission obtained an average Pearson correlation score of 0.4178 for predicting empathy and distress scores, ranked first among all nine submissions.
Domain Transfer for Empathy, Distress, and Personality Prediction (2023)

This research contributes to the task of predicting empathy and personality traits within dialogue, an important aspect of natural language processing, as part of our experimental work for the WASSA 2023 Empathy and Emotion Shared Task. For predicting empathy, emotion polarity, and emotion intensity on turns within a dialogue, we employ adapters trained on social media interactions labeled with empathy ratings in a stacked composition with the target task adapters. Furthermore, we embed demographic information to predict Interpersonal Reactivity Index (IRI) subscales and Big Five Personality Traits utilizing BERT-based models. The results from our study provide valuable insights, contributing to advancements in understanding human behavior and interaction through text. Our team ranked 2nd on the personality and empathy prediction tasks, 4th on the interpersonal reactivity index, and 6th on the conversational task.
Converge at WASSA 2023 Empathy, Emotion and Personality Shared Task: A Transformer-based Approach for Multi-Label Emotion Classification (2023)

In this paper, we highlight our approach for the {\textquotedblleft}WASSA 2023 Shared-Task 1: Empathy Detection and Emotion Classification{\textquotedblright}. By accurately identifying emotions from textual sources of data, deep learning models can be trained to understand and interpret human emotions more effectively. The classification of emotions facilitates the creation of more emotionally intelligent systems that can better understand and respond to human emotions. We compared multiple transformer-based models for multi-label classification. Ensembling and oversampling were used to improve the performance of the system. A threshold-based voting mechanism performed on three models (Longformer, BERT, BigBird) yields the highest overall macro F1-score of 0.6605.
PICT-CLRL at WASSA 2023 Empathy, Emotion and Personality Shared Task: Empathy and Distress Detection using Ensembles of Transformer Models (2023)

This paper presents our approach for the WASSA 2023 Empathy, Emotion and Personality Shared Task. Empathy and distress are human feelings that are implicitly expressed in natural discourses. Empathy and distress detection are crucial challenges in Natural Language Processing that can aid our understanding of conversations. The provided dataset consists of several long-text examples in the English language, with each example associated with a numeric score for empathy and distress. We experiment with several BERT-based models as a part of our approach. We also try various ensemble methods. Our final submission has a Pearson`s r score of 0.346, placing us third in the empathy and distress detection subtask.
Team Bias Busters at WASSA 2023 Empathy, Emotion and Personality Shared Task: Emotion Detection with Generative Pretrained Transformers (2023)

This paper describes the approach that we used to take part in the multi-label multi-class emotion classification as Track 3 of the WASSA 2023 Empathy, Emotion and Personality Shared Task at ACL 2023. The overall goal of this track is to build models that can predict 8 classes (7 emotions + neutral) based on short English essays written in response to news article that talked about events perceived as harmful to people. We used OpenAI generative pretrained transformers with full-scale APIs for the emotion prediction task by fine-tuning a GPT-3 model and doing prompt engineering for zero-shot / few-shot learning with ChatGPT and GPT-4 models based on multiple experiments on the dev set. The most efficient method was fine-tuning a GPT-3 model which allowed us to beat our baseline character-based XGBoost Classifier and rank 2nd among all other participants by achieving a macro F1 score of 0.65 and a micro F1 score of 0.7 on the final blind test set.
HIT-SCIR at WASSA 2023: Empathy and Emotion Analysis at the Utterance-Level and the Essay-Level (2023)

This paper introduces the participation of team HIT-SCIR to the WASSA 2023 Shared Task on Empathy Detection and Emotion Classification and Personality Detection in Interactions. We focus on three tracks: Track 1 (Empathy and Emotion Prediction in Conversations, CONV), Track 2 (Empathy Prediction, EMP) and Track 3 (Emotion Classification, EMO), and designed three different models to address them separately. For Track 1, we designed a direct fine-tuning DeBERTa model for three regression tasks at the utterance-level. For Track 2, we designed a multi-task learning RoBERTa model for two regression tasks at the essay-level. For Track 3, we designed a RoBERTa model with data augmentation for the classification task at the essay-level. Finally, our team ranked 1st in the Track 1 (CONV), 5th in the Track 2 (EMP) and 3rd in the Track 3 (EMO) in the evaluation phase.
VISU at WASSA 2023 Shared Task: Detecting Emotions in Reaction to News Stories Using Transformers and Stacked Embeddings (2023)

Our system, VISU, participated in the WASSA 2023 Shared Task (3) of Emotion Classification from essays written in reaction to news articles. Emotion detection from complex dialogues is challenging and often requires context/domain understanding. Therefore in this research, we have focused on developing deep learning (DL) models using the combination of word embedding representations with tailored prepossessing strategies to capture the nuances of emotions expressed. Our experiments used static and contextual embeddings (individual and stacked) with Bidirectional Long short-term memory (BiLSTM) and Transformer based models. We occupied rank tenth in the emotion detection task by scoring a Macro F1-Score of 0.2717, validating the efficacy of our implemented approaches for small and imbalanced datasets with mixed categories of target emotions.
Findings of WASSA 2023 Shared Task: Multi-Label and Multi-Class Emotion Classification on Code-Mixed Text Messages (2023)

We present the results of the WASSA 2023 Shared-Task 2: Emotion Classification on code-mixed text messages (Roman Urdu + English), which included two tracks for emotion classification: multi-label and multi-class. The participants were provided with a dataset of code-mixed SMS messages in English and Roman Urdu labeled with 12 emotions for both tracks. A total of 5 teams (19 team members) participated in the shared task. We summarized the methods, resources, and tools used by the participating teams. We also made the data freely available for further improvements to the task.
Emotion classification on code-mixed text messages via soft prompt tuning (2023)

Emotion classification on code-mixed text messages is challenging due to the multilingual languages and non-literal cues (i.e., emoticons). To solve these problems, we propose an innovative soft prompt tuning method, which is lightweight and effective to release potential abilities of the pre-trained language models and improve the classification results. Firstly, we transform emoticons into textual information to utilize their rich emotional information. Then, variety of innovative templates and verbalizers are applied to promote emotion classification. Extensive experiments show that transforming emoticons and employing prompt tuning both benefit the performance. Finally, as a part of WASSA 2023, we obtain the accuracy of 0.972 in track MLEC and 0.892 in track MCEC, yielding the second place in both two tracks.
PrecogIIITH@WASSA2023: Emotion Detection for Urdu-English Code-mixed Text (2023)

Code-mixing refers to the phenomenon of using two or more languages interchangeably within a speech or discourse context. This practice is particularly prevalent on social media platforms, and determining the embedded affects in a code-mixed sentence remains as a challenging problem. In this submission we describe our system for WASSA 2023 Shared Task on Emotion Detection in English-Urdu code-mixed text. In our system we implement a multiclass emotion detection model with label space of 11 emotions. Samples are code-mixed English-Urdu text, where Urdu is written in romanised form. Our submission is limited to one of the subtasks - Multi Class classification and we leverage transformer-based Multilingual Large Language Models (MLLMs), XLM-RoBERTa and Indic-BERT. We fine-tune MLLMs on the released data splits, with and without pre-processing steps (translation to english), for classifying texts into the appropriate emotion category. Our methods did not surpass the baseline, and our submission is ranked sixth overall.
BpHigh at WASSA 2023: Using Contrastive Learning to build Sentence Transformer models for Multi-Class Emotion Classification in Code-mixed Urdu (2023)

In this era of digital communication and social media, texting and chatting among individuals occur mainly through code-mixed or Romanized versions of the native language prevalent in the region. The presence of Romanized and code-mixed language develops the need to build NLP systems in these domains to leverage the digital content for various use cases. This paper describes our contribution to the subtask MCEC of the shared task WASSA 2023:Shared Task on Multi-Label and Multi-Class Emotion Classification on Code-Mixed Text Messages. We explore how one can build sentence transformers models for low-resource languages using unsupervised data by leveraging contrastive learning techniques described in the SIMCSE paper and using the sentence transformer developed to build classification models using the SetFit approach. Additionally, we`ll publish our code and models on GitHub and HuggingFace, two open-source hosting services.