pwc-archive/papers-with-abstracts huggingface dataset:
Innovators @ SMM4H’22: An Ensembles Approach for self-reporting of COVID-19 Vaccination Status Tweets
Authors: Mohammad Zohair, Nidhir Bhavsar, Aakash Bhatnagar, Muskaan Singh
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10

With the Surge in COVID-19, the number of social media postings related to the vaccine has grown, specifically tracing the confirmed reports by the users regarding the COVID-19 vaccine dose termed “Vaccine Surveillance.” To mitigate this research problem, we present our novel ensembled approach for self-reporting COVID-19 vaccination status tweets into two labels, namely “Vaccine Chatter” and “Self Report.” We utilize state-of-the-art models, namely BERT, RoBERTa, and XLNet. Our model provides promising results with 0.77, 0.93, and 0.66 as precision, recall, and F1-score (respectively), comparable to the corresponding median scores of 0.77, 0.9, and 0.68 (respec- tively). The model gave an overall accuracy of 93.43. We also present an empirical analysis of the results to present how well the tweet was able to classify and report. We release our code base here https://github.com/Zohair0209/SMM4H-2022-Task6.git
Innovators@SMM4H’22: An Ensembles Approach for Stance and Premise Classification of COVID-19 Health Mandates Tweets
Authors: Vatsal Savaliya, Aakash Bhatnagar, Nidhir Bhavsar, Muskaan Singh
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10
Tasks: Classification, Stance Detection, Task 2

This paper presents our submission for the Shared Task-2 of classification of stance and premise in tweets about health mandates related to COVID-19 at the Social Media Mining for Health 2022. There have been a plethora of tweets about people expressing their opinions on the COVID-19 epidemic since it first emerged. The shared task emphasizes finding the level of cooperation within the mandates for their stance towards the health orders of the pandemic. Overall the shared subjects the participants to propose system’s that can efficiently perform 1) Stance Detection, which focuses on determining the author’s point of view in the text. 2) Premise Classification, which indicates whether or not the text has arguments. Through this paper we propose an orchestration of multiple transformer based encoders to derive the output for stance and premise classification. Our best model achieves a F1 score of 0.771 for Premise Classification and an aggregate macro-F1 score of 0.661 for Stance Detection. We have made our code public here
AILAB-Udine@SMM4H’22: Limits of Transformers and BERT Ensembles
Authors: Beatrice Portelli, Simone Scaboro, Emmanuele Chersoni, Enrico Santus, Giuseppe Serra
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10
Tasks: Ensemble Learning, text-classification, Text Classification

This paper describes the models developed by the AILAB-Udine team for the SMM4H’22 Shared Task. We explored the limits of Transformer based models on text classification, entity extraction and entity normalization, tackling Tasks 1, 2, 5, 6 and 10. The main takeaways we got from participating in different tasks are: the overwhelming positive effects of combining different architectures when using ensemble learning, and the great potential of generative models for term normalization.
AIR-JPMC@SMM4H’22: Classifying Self-Reported Intimate Partner Violence in Tweets with Multiple BERT-based Models
Authors: Alec Louis Candidato, Akshat Gupta, Xiaomo Liu, Sameena Shah
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10

This paper presents our submission for the SMM4H 2022-Shared Task on the classification of self-reported intimate partner violence on Twitter (in English). The goal of this task was to accurately determine if the contents of a given tweet demonstrated someone reporting their own experience with intimate partner violence. The submitted system is an ensemble of five RoBERTa models each weighted by their respective F1-scores on the validation data-set. This system performed 13% better than the baseline and was the best performing system overall for this shared task.
ARGUABLY@SMM4H’22: Classification of Health Related Tweets using Ensemble, Zero-Shot and Fine-Tuned Language Model
Authors: Prabsimran Kaur, Guneet Kohli, Jatin Bedi
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10
Tasks: Language Modeling, Language Modelling

With the increase in the use of social media, people have become more outspoken and are using platforms like Reddit, Facebook, and Twitter to express their views and share the medical challenges they are facing. This data is a valuable source of medical insight and is often used for healthcare research. This paper describes our participation in Task 1a, 2a, 2b, 3, 5, 6, 7, and 9 organized by SMM4H 2022. We have proposed two transformer-based approaches to handle the classification tasks. The first approach is fine-tuning single language models. The second approach is ensembling the results of BERT, RoBERTa, and ERNIE 2.0.
CASIA@SMM4H’22: A Uniform Health Information Mining System for Multilingual Social Media Texts
Authors: Jia Fu, Sirui Li, Hui Ming Yuan, Zhucong Li, Zhen Gan, Yubo Chen, Kang Liu, Jun Zhao, Shengping Liu
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

This paper presents a description of our system in SMM4H-2022, where we participated in task 1a,task 4, and task 6 to task 10. There are three main challenges in SMM4H-2022, namely the domain shift problem, the prediction bias due to category imbalance, and the noise in informal text. In this paper, we propose a unified framework for the classification and named entity recognition tasks to solve the challenges, and it can be applied to both English and Spanish scenarios. The results of our system are higher than the median F1-scores for 7 tasks and significantly exceed the F1-scores for 6 tasks. The experimental results demonstrate the effectiveness of our system.
Edinburgh_UCL_Health@SMM4H’22: From Glove to Flair for handling imbalanced healthcare corpora related to Adverse Drug Events, Change in medication and self-reporting vaccination
Authors: Imane Guellil, Jinge Wu, Honghan Wu, Tony Sun, Beatrice Alex
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10
Tasks: Classification

This paper reports on the performance of Edinburgh_UCL_Health’s models in the Social Media Mining for Health (SMM4H) 2022 shared tasks. Our team participated in the tasks related to the Identification of Adverse Drug Events (ADEs), the classification of change in medication (change-med) and the classification of self-report of vaccination (self-vaccine). Our best performing models are based on DeepADEMiner (with respective F1= 0.64, 0.62 and 0.39 for ADE identification), on a GloVe model trained on Twitter (with F1=0.11 for the change-med) and finally on a stack embedding including a layer of Glove embedding and two layers of Flair embedding (with F1= 0.77 for self-report).
KUL@SMM4H’22: Template Augmented Adaptive Pre-training for Tweet Classification
Authors: Sumam Francis, Marie-Francine Moens
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10
Tasks: Data Augmentation, Language Modeling, Language Modelling, Masked Language Modeling

This paper describes models developed for the Social Media Mining for Health (SMM4H) 2022 shared tasks. Our team participated in the first subtask that classifies tweets with Adverse Drug Effect (ADE) mentions. Our best-performing model comprises of a template augmented task adaptive pre-training and further fine-tuning on target task data. Augmentation with random prompt templates increases the amount of task-specific data to generalize the LM to the target task domain. We explore 2 pre-training strategies: Masked language modeling (MLM) and Simple contrastive pre-training (SimSCE) and the impact of adding template augmentations with these pre-training strategies. Our system achieves an F1 score of 0.433 on the test set without using supplementary resources and medical dictionaries.
Enolp musk@SMM4H’22 : Leveraging Pre-trained Language Models for Stance And Premise Classification
Authors: Millon Das, Archit Mangrulkar, Ishan Manchanda, Manav Kapadnis, Sohan Patnaik
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10
Tasks: Classification, Dependency Parsing, POS, Stance Classification

This paper covers our approaches for the Social Media Mining for Health (SMM4H) Shared Tasks 2a and 2b. Apart from the baseline architectures, we experiment with Parts of Speech (PoS), dependency parsing, and Tf-Idf features. Additionally, we perform contrastive pretraining on our best models using a supervised contrastive loss function. In both the tasks, we outperformed the mean and median scores and ranked first on the validation set. For stance classification, we achieved an F1-score of 0.636 using the CovidTwitterBERT model, while for premise classification, we achieved an F1-score of 0.664 using BART-base model on test dataset.
AIR-JPMC@SMM4H’22: Identifying Self-Reported Spanish COVID-19 Symptom Tweets Through Multiple-Model Ensembling
Authors: Adrian Garcia Hernandez, Leung Wai Liu, Akshat Gupta, Vineeth Ravi, Saheed O. Obitayo, Xiaomo Liu, Sameena Shah
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10

We present our response to Task 5 of the Social Media Mining for Health Applications (SMM4H) 2022 competition. We share our approach into classifying whether a tweet in Spanish about COVID-19 symptoms pertain to themselves, others, or not at all. Using a combination of BERT based models, we were able to achieve results that were higher than the median result of the competition.
AIR-JPMC@SMM4H’22: BERT + Ensembling = Too Cool: Using Multiple BERT Models Together for Various COVID-19 Tweet Identification Tasks
Authors: Leung Wai Liu, Akshat Gupta, Saheed Obitayo, Xiaomo Liu, Sameena Shah
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10

This paper presents my submission for Tasks 1 and 2 for the Social Media Mining of Health (SMM4H) 2022 Shared Tasks competition. I first describe the background behind each of these tasks, followed by the descriptions of the various subtasks of Tasks 1 and 2, then present the methodology. Through model ensembling, this methodology was able to achieve higher results than the mean and median of the competition for the classification tasks.
CAISA@SMM4H’22: Robust Cross-Lingual Detection of Disease Mentions on Social Media with Adversarial Methods
Authors: Akbar Karimi, Lucie Flek
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10
Tasks: Data Augmentation

We propose adversarial methods for increasing the robustness of disease mention detection on social media. Our method applies adversarial data augmentation on the input and the embedding spaces to the English BioBERT model. We evaluate our method in the SocialDisNER challenge at SMM4H’22 on an annotated dataset of disease mentions in Spanish tweets. We find that both methods outperform a heuristic vocabulary-based baseline by a large margin. Additionally, utilizing the English BioBERT model shows a strong performance and outperforms the data augmentation methods even when applied to the Spanish dataset, which has a large amount of data, while augmentation methods show a significant advantage in a low-data setting.
OFU@SMM4H’22: Mining Advent Drug Events Using Pretrained Language Models
Authors: Omar Adjali, Fréjus A. A. Laleye, Umang Aggarwal
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10

We describe in this paper our proposed systems for the Social Media Mining for Health 2022 shared task 1. In particular, we participated in the three sub-tasks, tasks that aim at extracting and processing Adverse Drug Events. We investigate different transformer-based pretrained models we fine-tuned on each task and proposed some improvement on the task of entity normalization.
CompLx@SMM4H’22: In-domain pretrained language models for detection of adverse drug reaction mentions in English tweets
Authors: Orest Xherija, Hojoon Choi
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10
Tasks: Language Modeling, Language Modelling

The paper describes the system that team CompLx developed for sub-task 1a of the Social Media Mining for Health 2022 (#SMM4H) Shared Task. We finetune a RoBERTa model, a pretrained, transformer-based language model, on a provided dataset to classify English tweets for mentions of Adverse Drug Reactions (ADRs), i.e. negative side effects related to medication intake. With only a simple finetuning, our approach achieves competitive results, significantly outperforming the average score across submitted systems. We make the model checkpoints and code publicly available. We also create a web application to provide a user-friendly, readily accessible interface for anyone interested in exploring the model’s capabilities.
The SocialDisNER shared task on detection of disease mentions in health-relevant content from social media: methods, evaluation, guidelines and corpora
Authors: Luis Gasco Sánchez, Darryl Estrada Zavala, Eulàlia Farré-Maduell, Salvador Lima-López, Antonio Miranda-Escalada, Martin Krallinger
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10
Tasks: Knowledge Graphs, Pharmacovigilance

There is a pressing need to exploit health-related content from social media, a global source of data where key health information is posted directly by citizens, patients and other healthcare stakeholders. Use cases of disease related social media mining include disease outbreak/surveillance, mental health and pharmacovigilance. Current efforts address the exploitation of social media beyond English. The SocialDisNER task, organized as part of the SMM4H 2022 initiative, has applied the LINKAGE methodology to select and annotate a Gold Standard corpus of 9,500 tweets in Spanish enriched with disease mentions generated by patients and medical professionals. As a complementary resource for teams participating in the SocialDisNER track, we have also created a large-scale corpus of 85,000 tweets, where in addition to disease mentions, other medical entities of relevance (e.g., medications, symptoms and procedures, among others) have been automatically labelled. Using these large-scale datasets, co-mention networks or knowledge graphs were released for each entity pair type. Out of the 47 teams registered for the task, 17 teams uploaded a total of 32 runs. The top-performing team achieved a very competitive 0.891 f-score, with a system trained following a continue pre-training strategy. We anticipate that the corpus and systems resulting from the SocialDisNER track might further foster health related text mining of social media content in Spanish and inspire disease detection strategies in other languages.
Romanian micro-blogging named entity recognition including health-related entities
Authors: Vasile Pais, Verginica Barbu Mititelu, Elena Irimia, Maria Mitrofan, Carol Luca Gasan, Roxana Micu
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Word Embeddings

This paper introduces a manually annotated dataset for named entity recognition (NER) in micro-blogging text for Romanian language. It contains gold annotations for 9 entity classes and expressions: persons, locations, organizations, time expressions, legal references, disorders, chemicals, medical devices and anatomical parts. Furthermore, word embeddings models computed on a larger micro-blogging corpus are made available. Finally, several NER models are trained and their performance is evaluated against the newly introduced corpus.
The Best of Both Worlds: Combining Engineered Features with Transformers for Improved Mental Health Prediction from Reddit Posts
Authors: Sourabh Zanwar, Daniel Wiechmann, Yu Qiao, Elma Kerz
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10

In recent years, there has been increasing interest in the application of natural language processing and machine learning techniques to the detection of mental health conditions (MHC) based on social media data. In this paper, we aim to improve the state-of-the-art (SoTA) detection of six MHC in Reddit posts in two ways: First, we built models leveraging Bidirectional Long Short-Term Memory (BLSTM) networks trained on in-text distributions of a comprehensive set of psycholinguistic features for more explainable MHC detection as compared to black-box solutions. Second, we combine these BLSTM models with Transformers to improve the prediction accuracy over SoTA models. In addition, we uncover nuanced patterns of linguistic markers characteristic of specific MHC.
Leveraging Social Media as a Source for Clinical Guidelines: A Demarcation of Experiential Knowledge
Authors: Jia-Zhen Michelle Chan, Florian Kunneman, Roser Morante, Lea Lösch, Teun Zuiderent-Jerak
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10
Tasks: Diversity

In this paper we present a procedure to extract posts that contain experiential knowledge from Facebook discussions in Dutch, using automated filtering, manual annotations and machine learning. We define guidelines to annotate experiential knowledge and test them on a subset of the data. After several rounds of (re-)annotations, we come to an inter-annotator agreement of K=0.69, which reflects the difficulty of the task. We subsequently discuss inclusion and exclusion criteria to cope with the diversity of manifestations of experiential knowledge relevant to guideline development.
SMM4H 2022 Task 2: Dataset for stance and premise detection in tweets about health mandates related to COVID-19
Authors: Vera Davydova, Elena Tutubalina
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10
Tasks: Argument Mining, Stance Detection, Task 2

This paper is an organizers’ report of the competition on argument mining systems dealing with English tweets about COVID-19 health mandates. This competition was held within the framework of the SMM4H 2022 shared tasks. During the competition, the participants were offered two subtasks: stance detection and premise classification. We present a manually annotated corpus containing 6,156 short posts from Twitter on three topics related to the COVID-19 pandemic: school closures, stay-at-home orders, and wearing masks. We hope the prepared dataset will support further research on argument mining in the health field.
Overview of the Seventh Social Media Mining for Health Applications (#SMM4H) Shared Tasks at COLING 2022
Authors: Davy Weissenbacher, Juan Banda, Vera Davydova, Darryl Estrada Zavala, Luis Gasco Sánchez, Yao Ge, Yuting Guo, Ari Klein, Martin Krallinger, Mathias Leddin, Arjun Magge, Raul Rodriguez-Esteban, Abeed Sarker, Lucia Schmidt, Elena Tutubalina, Graciela Gonzalez-Hernandez
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10

For the past seven years, the Social Media Mining for Health Applications (#SMM4H) shared tasks have promoted the community-driven development and evaluation of advanced natural language processing systems to detect, extract, and normalize health-related information in public, user-generated content. This seventh iteration consists of ten tasks that include English and Spanish posts on Twitter, Reddit, and WebMD. Interest in the #SMM4H shared tasks continues to grow, with 117 teams that registered and 54 teams that participated in at least one task—a 17.5% and 35% increase in registration and participation, respectively, over the last iteration. This paper provides an overview of the tasks and participants’ systems. The data sets remain available upon request, and new systems can be evaluated through the post-evaluation phase on CodaLab.
Knowledge Transfer with Visual Prompt in multi-modal Dialogue Understanding and Generation
Authors: Minjun Zhu, Yixuan Weng, Bin Li, Shizhu He, Kang Liu, Jun Zhao
Date: 2022-10-01 00:00:00
Proceeding: TU (COLING) 2022 10
Tasks: Dialogue Understanding, Knowledge Distillation, Transfer Learning, Visual Dialog

Visual Dialogue (VD) task has recently received increasing attention in AI research. Visual Dialog aims to generate multi-round, interactive responses based on the dialog history and image content. Existing textual dialogue models cannot fully understand visual information, resulting in a lack of scene features when communicating with humans continuously. Therefore, how to efficiently fuse multimodal data features remains to be a challenge. In this work, we propose a knowledge transfer method with visual prompt (VPTG) fusing multi-modal data, which is a flexible module that can utilize the text-only seq2seq model to handle visual dialogue tasks. The VPTG conducts text-image co-learning and multi-modal information fusion with visual prompts and visual knowledge distillation. Specifically, we construct visual prompts from visual representations and then induce sequence-to-sequence(seq2seq) models to fuse visual information and textual contexts by visual-text patterns. And we also realize visual knowledge transfer through distillation between two different models’ text representations, so that the seq2seq model can actively learn visual semantic representations. Extensive experiments on the multi-modal dialogue understanding and generation (MDUG) datasets show the proposed VPTG outperforms other single-modal methods, which demonstrate the effectiveness of visual prompt and visual knowledge transfer.
Model Transfer for Event tracking as Transcript Understanding for Videos of Small Group Interaction
Authors: Sumit Agarwal, Rosanna Vitiello, Carolyn Rosé
Date: 2022-10-01 00:00:00
Proceeding: TU (COLING) 2022 10

Videos of group interactions contain a wealth of information beyond the information directly communicated in a transcript of the discussion. Tracking who has participated throughout an extended interaction and what each of their trajectories has been in relation to one another is the foundation for joint activity understanding, though it comes with some unique challenges in videos of tightly coupled group work. Motivated by insights into the properties of such scenarios, including group composition and the properties of task-oriented, goal directed tasks, we present a successful proof-of-concept. In particular, we present a transfer experiment to a dyadic robot construction task, an ablation study, and a qualitative analysis.
BehanceMT: A Machine Translation Corpus for Livestreaming Video Transcripts
Authors: Minh Van Nguyen, Franck Dernoncourt, Thien Nguyen
Date: 2022-10-01 00:00:00
Proceeding: TU (COLING) 2022 10
Tasks: Articles, Machine Translation, Sentence, Translation

Machine translation (MT) is an important task in natural language processing, which aims to translate a sentence in a source language to another sentence with the same/similar semantics in a target language. Despite the huge effort on building MT systems for different language pairs, most previous work focuses on formal-language settings, where text to be translated come from written sources such as books and news articles. As a result, such MT systems could fail to translate livestreaming video transcripts, where text is often shorter and might be grammatically incorrect. To overcome this issue, we introduce a novel MT corpus - BehanceMT for livestreaming video transcript translation. Our corpus contains parallel transcripts for 3 language pairs, where English is the source language and Spanish, Chinese, and Arabic are the target languages. Experimental results show that finetuning a pretrained MT model on BehanceMT significantly improves the performance of the model in translating video transcripts across 3 language pairs. In addition, the finetuned MT model outperforms GoogleTranslate in 2 out of 3 language pairs, further demonstrating the usefulness of our proposed dataset for video transcript translation. BehanceMT will be publicly released upon the acceptance of the paper.