pwc-archive/papers-with-abstracts huggingface dataset:
Uncertainty Regularized Multi-Task Learning
Authors: Kourosh Meshgi, Maryam Sadat Mirzaei, Satoshi Sekine
Date: 2022-05-01 00:00:00
Proceeding: WASSA (ACL) 2022 5
Tasks: Multi-Task Learning, text-classification, Text Classification, Transfer Learning

By sharing parameters and providing task-independent shared features, multi-task deep neural networks are considered one of the most interesting ways for parallel learning from different tasks and domains. However, fine-tuning on one task may compromise the performance of other tasks or restrict the generalization of the shared learned features. To address this issue, we propose to use task uncertainty to gauge the effect of the shared feature changes on other tasks and prevent the model from overfitting or over-generalizing. We conducted an experiment on 16 text classification tasks, and findings showed that the proposed method consistently improves the performance of the baseline, facilitates the knowledge transfer of learned features to unseen data, and provides explicit control over the generalization of the shared model.
Understanding BERT’s Mood: The Role of Contextual-Embeddings as User-Representations for Depression Assessment
Authors: Matthew Matero, Albert Hung, H. Schwartz
Date: 2022-05-01 00:00:00
Proceeding: WASSA (ACL) 2022 5
Tasks: Language Modeling, Language Modelling

Many recent works in natural language processing have demonstrated ability to assess aspects of mental health from personal discourse. At the same time, pre-trained contextual word embedding models have grown to dominate much of NLP but little is known empirically on how to best apply them for mental health assessment. Using degree of depression as a case study, we do an empirical analysis on which off-the-shelf language model, individual layers, and combinations of layers seem most promising when applied to human-level NLP tasks. Notably, we find RoBERTa most effective and, despite the standard in past work suggesting the second-to-last or concatenation of the last 4 layers, we find layer 19 (sixth-to last) is at least as good as layer 23 when using 1 layer. Further, when using multiple layers, distributing them across the second half (i.e. Layers 12+), rather than last 4, of the 24 layers yielded the most accurate results.
Emotion Analysis of Writers and Readers of Japanese Tweets on Vaccinations
Authors: Patrick John Ramos, Kiki Ferawati, Kongmeng Liew, Eiji Aramaki, Shoko Wakamiya
Date: 2022-05-01 00:00:00
Proceeding: WASSA (ACL) 2022 5
Tasks: Emotion Recognition

Public opinion in social media is increasingly becoming a critical factor in pandemic control. Understanding the emotions of a population towards vaccinations and COVID-19 may be valuable in convincing members to become vaccinated. We investigated the emotions of Japanese Twitter users towards Tweets related to COVID-19 vaccination. Using the WRIME dataset, which provides emotion ratings for Japanese Tweets sourced from writers (Tweet posters) and readers, we fine-tuned a BERT model to predict levels of emotional intensity. This model achieved a training accuracy of MSE = 0.356. A separate dataset of 20,254 Japanese Tweets containing COVID-19 vaccine-related keywords was also collected, on which the fine-tuned BERT was used to perform emotion analysis. Afterwards, a correlation analysis between the extracted emotions and a set of vaccination measures in Japan was conducted.The results revealed that surprise and fear were the most intense emotions predicted by the model for writers and readers, respectively, on the vaccine-related Tweet dataset. The correlation analysis also showed that vaccinations were weakly positively correlated with predicted levels of writer joy, writer/reader anticipation, and writer/reader trust.
Opinion-based Relational Pivoting for Cross-domain Aspect Term Extraction
Authors: Ayal Klein, Oren Pereg, Daniel Korat, Vasudev Lal, Moshe Wasserblat, Ido Dagan
Date: 2022-05-01 00:00:00
Proceeding: WASSA (ACL) 2022 5
Tasks: Domain Adaptation, Term Extraction

Domain adaptation methods often exploit domain-transferable input features, a.k.a. pivots. The task of Aspect and Opinion Term Extraction presents a special challenge for domain transfer: while opinion terms largely transfer across domains, aspects change drastically from one domain to another (e.g. from restaurants to laptops). In this paper, we investigate and establish empirically a prior conjecture, which suggests that the linguistic relations connecting opinion terms to their aspects transfer well across domains and therefore can be leveraged for cross-domain aspect term extraction. We present several analyses supporting this conjecture, via experiments with four linguistic dependency formalisms to represent relation patterns. Subsequently, we present an aspect term extraction method that drives models to consider opinion–aspect relations via explicit multitask objectives. This method provides significant performance gains, even on top of a prior state-of-the-art linguistically-informed model, which are shown in analysis to stem from the relational pivoting signal.
English-Malay Word Embeddings Alignment for Cross-lingual Emotion Classification with Hierarchical Attention Network
Authors: Ying Hao Lim, Jasy Suet Yan Liew
Date: 2022-05-01 00:00:00
Proceeding: WASSA (ACL) 2022 5
Tasks: Cross-Lingual Word Embeddings, Emotion Classification, Few-Shot Learning, Machine Translation, Translation, Word Embeddings, Zero-Shot Learning

The main challenge in English-Malay cross-lingual emotion classification is that there are no Malay training emotion corpora. Given that machine translation could fall short in contextually complex tweets, we only limited machine translation to the word level. In this paper, we bridge the language gap between English and Malay through cross-lingual word embeddings constructed using singular value decomposition. We pre-trained our hierarchical attention model using English tweets and fine-tuned it using a set of gold standard Malay tweets. Our model uses significantly less computational resources compared to the language models. Experimental results show that the performance of our model is better than mBERT in zero-shot learning by 2.4% and Malay BERT by 0.8% when a limited number of Malay tweets is available. In exchange for 6 – 7 times less in computational time, our model only lags behind mBERT and XLM-RoBERTa by a margin of 0.9 – 4.3 % in few-shot learning. Also, the word-level attention could be transferred to the Malay tweets accurately using the cross-lingual word embeddings.
Distinguishing In-Groups and Onlookers by Language Use
Authors: Joshua Minot, Milo Trujillo, Samuel Rosenblatt, Guillermo De Anda-Jáuregui, Emily Moog, Allison M. Roth, Briane Paul Samson, Laurent Hébert-Dufresne
Date: 2022-05-01 00:00:00
Proceeding: WASSA (ACL) 2022 5

Inferring group membership of social media users is of high interest in many domains. Group membership is typically inferred via network interactions with other members, or by the usage of in-group language. However, network information is incomplete when users or groups move between platforms, and in-group keywords lose significance as public discussion about a group increases. Similarly, using keywords to filter content and users can fail to distinguish between the various groups that discuss a topic—perhaps confounding research on public opinion and narrative trends. We present a classifier intended to distinguish members of groups from users discussing a group based on contextual usage of keywords. We demonstrate the classifier on a sample of community pairs from Reddit and focus on results related to the COVID-19 pandemic.
Irony Detection for Dutch: a Venture into the Implicit
Authors: Aaron Maladry, Els Lefever, Cynthia Van Hee, Veronique Hoste
Date: 2022-05-01 00:00:00
Proceeding: WASSA (ACL) 2022 5
Tasks: Common Sense Reasoning

This paper presents the results of a replication experiment for automatic irony detection in Dutch social media text, investigating both a feature-based SVM classifier, as was done by Van Hee et al. (2017) and and a transformer-based approach. In addition to building a baseline model, an important goal of this research is to explore the implementation of common-sense knowledge in the form of implicit sentiment, as we strongly believe that common-sense and connotative knowledge are essential to the identification of irony and implicit meaning in tweets.We show promising results and the presented approach can provide a solid baseline and serve as a staging ground to build on in future experiments for irony detection in Dutch.
XLM-EMO: Multilingual Emotion Prediction in Social Media Text
Authors: Federico Bianchi, Debora Nozza, Dirk Hovy
Date: 2022-05-01 00:00:00
Proceeding: WASSA (ACL) 2022 5
Tasks: Prediction

Detecting emotion in text allows social and computational scientists to study how people behave and react to online events. However, developing these tools for different languages requires data that is not always available. This paper collects the available emotion detection datasets across 19 languages. We train a multilingual emotion prediction model for social media data, XLM-EMO. The model shows competitive performance in a zero-shot setting, suggesting it is helpful in the context of low-resource languages. We release our model to the community so that interested researchers can directly use it.
Evaluating Content Features and Classification Methods for Helpfulness Prediction of Online Reviews: Establishing a Benchmark for Portuguese
Authors: Rogério Sousa, Thiago Pardo
Date: 2022-05-01 00:00:00
Proceeding: WASSA (ACL) 2022 5
Tasks: Classification, Prediction

Over the years, the review helpfulness prediction task has been the subject of several works, but remains being a challenging issue in Natural Language Processing, as results vary a lot depending on the domain, on the adopted features and on the chosen classification strategy. This paper attempts to evaluate the impact of content features and classification methods for two different domains. In particular, we run our experiments for a low resource language – Portuguese –, trying to establish a benchmark for this language. We show that simple features and classical classification methods are powerful for the task of helpfulness prediction, but are largely outperformed by a convolutional neural network-based solution.
WASSA 2022 Shared Task: Predicting Empathy, Emotion and Personality in Reaction to News Stories
Authors: Valentin Barriere, Shabnam Tafreshi, João Sedoc, Sawsan Alqahtani
Date: 2022-05-01 00:00:00
Proceeding: WASSA (ACL) 2022 5
Tasks: Articles

This paper presents the results that were obtained from WASSA 2022 shared task on predicting empathy, emotion, and personality in reaction to news stories. Participants were given access to a dataset comprising empathic reactions to news stories where harm is done to a person, group, or other. These reactions consist of essays and Batson’s empathic concern and personal distress scores. The dataset was further extended in WASSA 2021 shared task to include news articles, person-level demographic information (e.g. age, gender), personality information, and Ekman’s six basic emotions at essay level Participation was encouraged in four tracks: predicting empathy and distress scores, predicting emotion categories, predicting personality and predicting interpersonal reactivity. In total, 14 teams participated in the shared task. We summarize the methods and resources used by the participating teams.
IUCL at WASSA 2022 Shared Task: A Text-only Approach to Empathy and Emotion Detection
Authors: Yue Chen, Yingnan Ju, Sandra Kübler
Date: 2022-05-01 00:00:00
Proceeding: WASSA (ACL) 2022 5
Tasks: Classification, Emotion Classification

Our system, IUCL, participated in the WASSA 2022 Shared Task on Empathy Detection and Emotion Classification. Our main goal in building this system is to investigate how the use of demographic attributes influences performance. Our (official) results show that our text-only systems perform very competitively, ranking first in the empathy detection task, reaching an average Pearson correlation of 0.54, and second in the emotion classification task, reaching a Macro-F of 0.572. Our systems that use both text and demographic data are less competitive.
Continuing Pre-trained Model with Multiple Training Strategies for Emotional Classification
Authors: Bin Li, Yixuan Weng, Qiya Song, Bin Sun, Shutao Li
Date: 2022-05-01 00:00:00
Proceeding: WASSA (ACL) 2022 5
Tasks: Attribute, Classification, Data Augmentation, Emotional Intelligence, Emotion Classification, Language Modeling, Language Modelling

Emotion is the essential attribute of human beings. Perceiving and understanding emotions in a human-like manner is the most central part of developing emotional intelligence. This paper describes the contribution of the LingJing team’s method to the Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis (WASSA) 2022 shared task on Emotion Classification. The participants are required to predict seven emotions from empathic responses to news or stories that caused harm to individuals, groups, or others. This paper describes the continual pre-training method for the masked language model (MLM) to enhance the DeBERTa pre-trained language model. Several training strategies are designed to further improve the final downstream performance including the data augmentation with the supervised transfer, child-tuning training, and the late fusion method. Extensive experiments on the emotional classification dataset show that the proposed method outperforms other state-of-the-art methods, demonstrating our method’s effectiveness. Moreover, our submission ranked Top-1 with all metrics in the evaluation phase for the Emotion Classification task.
Empathy and Distress Prediction using Transformer Multi-output Regression and Emotion Analysis with an Ensemble of Supervised and Zero-Shot Learning Models
Authors: Flor Miriam Del Arco, Jaime Collado-Montañez, L. Alfonso Ureña, María-Teresa Martín-Valdivia
Date: 2022-05-01 00:00:00
Proceeding: WASSA (ACL) 2022 5
Tasks: Classification, Emotion Classification, Emotion Recognition, Natural Language Inference, regression, Zero-Shot Learning

This paper describes the participation of the SINAI research group at WASSA 2022 (Empathy and Personality Detection and Emotion Classification). Specifically, we participate in Track 1 (Empathy and Distress predictions) and Track 2 (Emotion classification). We conducted extensive experiments developing different machine learning solutions in line with the state of the art in Natural Language Processing. For Track 1, a Transformer multi-output regression model is proposed. For Track 2, we aim to explore recent techniques based on Zero-Shot Learning models including a Natural Language Inference model and GPT-3, using them in an ensemble manner with a fine-tune RoBERTa model. Our team ranked 2nd in the first track and 3rd in the second track.
Team IITP-AINLPML at WASSA 2022: Empathy Detection, Emotion Classification and Personality Detection
Authors: Soumitra Ghosh, Dhirendra Maurya, Asif Ekbal, Pushpak Bhattacharyya
Date: 2022-05-01 00:00:00
Proceeding: WASSA (ACL) 2022 5
Tasks: Emotion Classification, Prediction

Computational comprehension and identifying emotional components in language have been critical in enhancing human-computer connection in recent years. The WASSA 2022 Shared Task introduced four tracks and released a dataset of news stories: Track-1 for Empathy and Distress Prediction, Track-2 for Emotion classification, Track-3 for Personality prediction, and Track-4 for Interpersonal Reactivity Index prediction at the essay level. This paper describes our participation in the WASSA 2022 shared task on the tasks mentioned above. We developed multi-task deep learning methods to address Tracks 1 and 2 and machine learning models for Track 3 and 4. Our developed systems achieved average Pearson scores of 0.483, 0.05, and 0.08 for Track 1, 3, and 4, respectively, and a macro F1 score of 0.524 for Track 2 on the test set. We ranked 8th, 11th, 2nd and 2nd for tracks 1, 2, 3, and 4 respectively.
Transformer-based Architecture for Empathy Prediction and Emotion Classification
Authors: Himil Vasava, Pramegh Uikey, Gaurav Wasnik, Raksha Sharma
Date: 2022-05-01 00:00:00
Proceeding: WASSA (ACL) 2022 5
Tasks: Classification, Emotion Classification, Prediction, Task 2

This paper describes the contribution of team PHG to the WASSA 2022 shared task on Empathy Prediction and Emotion Classification. The broad goal of this task was to model an empathy score, a distress score and the type of emotion associated with the person who had reacted to the essay written in response to a newspaper article. We have used the RoBERTa model for training and top of which few layers are added to finetune the transformer. We also use few machine learning techniques to augment as well as upsample the data. Our system achieves a Pearson Correlation Coefficient of 0.488 on Task 1 (Empathy - 0.470 and Distress - 0.506) and Macro F1-score of 0.531 on Task 2.
SURREY-CTS-NLP at WASSA2022: An Experiment of Discourse and Sentiment Analysis for the Prediction of Empathy, Distress and Emotion
Authors: Shenbin Qian, Constantin Orasan, Diptesh Kanojia, Hadeel Saadany, Félix do Carmo
Date: 2022-05-01 00:00:00
Proceeding: WASSA (ACL) 2022 5
Tasks: Articles, Ensemble Learning, Multi-Task Learning, Prediction, Sentiment Analysis

This paper summarises the submissions our team, SURREY-CTS-NLP has made for the WASSA 2022 Shared Task for the prediction of empathy, distress and emotion. In this work, we tested different learning strategies, like ensemble learning and multi-task learning, as well as several large language models, but our primary focus was on analysing and extracting emotion-intensive features from both the essays in the training data and the news articles, to better predict empathy and distress scores from the perspective of discourse and sentiment analysis. We propose several text feature extraction schemes to compensate the small size of training examples for fine-tuning pretrained language models, including methods based on Rhetorical Structure Theory (RST) parsing, cosine similarity and sentiment score. Our best submissions achieve an average Pearson correlation score of 0.518 for the empathy prediction task and an F1 score of 0.571 for the emotion prediction task, indicating that using these schemes to extract emotion-intensive information can help improve model performance.
An Ensemble Approach to Detect Emotions at an Essay Level
Authors: Himanshu Maheshwari, Vasudeva Varma
Date: 2022-05-01 00:00:00
Proceeding: WASSA (ACL) 2022 5
Tasks: Emotion Classification

This paper describes our system (IREL, reffered as himanshu.1007 on Codalab) for Shared Task on Empathy Detection, Emotion Classification, and Personality Detection at 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis at ACL 2022. We participated in track 2 for predicting emotion at the essay level. We propose an ensemble approach that leverages the linguistic knowledge of the RoBERTa, BART-large, and RoBERTa model finetuned on the GoEmotions dataset. Each brings in its unique advantage, as we discuss in the paper. Our proposed system achieved a Macro F1 score of 0.585 and ranked one out of thirteen teams
CAISA at WASSA 2022: Adapter-Tuning for Empathy Prediction
Authors: Allison Lahnala, Charles Welch, Lucie Flek
Date: 2022-05-01 00:00:00
Proceeding: WASSA (ACL) 2022 5

We build a system that leverages adapters, a light weight and efficient method for leveraging large language models to perform the task Em- pathy and Distress prediction tasks for WASSA 2022. In our experiments, we find that stacking our empathy and distress adapters on a pre-trained emotion lassification adapter performs best compared to full fine-tuning approaches and emotion feature concatenation. We make our experimental code publicly available
NLPOP: a Dataset for Popularity Prediction of Promoted NLP Research on Twitter
Authors: Leo Obadić, Martin Tutek, Jan Šnajder
Date: 2022-05-01 00:00:00
Proceeding: WASSA (ACL) 2022 5
Tasks: Abstractive Text Summarization, Citation Recommendation

Twitter has slowly but surely established itself as a forum for disseminating, analysing and promoting NLP research. The trend of researchers promoting work not yet peer-reviewed (preprints) by posting concise summaries presented itself as an opportunity to collect and combine multiple modalities of data. In scope of this paper, we (1) construct a dataset of Twitter threads in which researchers promote NLP preprints and (2) evaluate whether it is possible to predict the popularity of a thread based on the content of the Twitter thread, paper content and user metadata. We experimentally show that it is possible to predict popularity of threads promoting research based on their content, and that predictive performance depends on modelling textual input, indicating that the dataset could present value for related areas of NLP research such as citation recommendation and abstractive summarization.
Tagging Without Rewriting: A Probabilistic Model for Unpaired Sentiment and Style Transfer
Authors: Yang Shuo
Date: 2022-05-01 00:00:00
Proceeding: WASSA (ACL) 2022 5
Tasks: Language Modeling, Language Modelling, Sentence, Style Transfer, TAG

Style transfer is the task of paraphrasing text into a target-style domain while retaining the content. Unsupervised approaches mainly focus on training a generator to rewrite input sentences. In this work, we assume that text styles are determined by only a small proportion of words; therefore, rewriting sentences via generative models may be unnecessary. As an alternative, we consider style transfer as a sequence tagging task. Specifically, we use edit operations (i.e., deletion, insertion and substitution) to tag words in an input sentence. We train a classifier and a language model to score tagged sequences and build a conditional random field. Finally, the optimal path in the conditional random field is used as the output. The results of experiments comparing models indicate that our proposed model exceeds end-to-end baselines in terms of accuracy on both sentiment and style transfer tasks with comparable or better content preservation.
Polite Task-oriented Dialog Agents: To Generate or to Rewrite?
Authors: Diogo Silva, David Semedo, João Magalhães
Date: 2022-05-01 00:00:00
Proceeding: WASSA (ACL) 2022 5
Tasks: Transfer Learning

For task-oriented dialog agents, the tone of voice mediates user-agent interactions, playing a central role in the flow of a conversation. Distinct from domain-agnostic politeness constructs, in specific domains such as online stores, booking platforms, and others, agents need to be capable of adopting highly specific vocabulary, with significant impact on lexical and grammatical aspects of utterances. Then, the challenge is on improving utterances’ politeness while preserving the actual content, an utterly central requirement to achieve the task goal. In this paper, we conduct a novel assessment of politeness strategies for task-oriented dialog agents under a transfer learning scenario. We extend existing generative and rewriting politeness approaches, towards overcoming domain-shifting issues, and enabling the transfer of politeness patterns to a novel domain. Both automatic and human evaluation is conducted on customer-store interactions, over the fashion domain, from which contribute with insightful and experimentally supported lessons regarding the improvement of politeness in task-specific dialog agents.