Korea, Republic of

This 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 Workshop. 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

COLING 2022

SMM4H Task 2: Stance and premise detection in tweets about COVID-19 health mandates

premise detection

social media mining

stance detection

argument mining

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### THE CONFERENCE WE KNOW AND WE WANT.
**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.

In fall of 2022, COLING will be held in Gyeongju in a hybrid format. All participants can either present at the venue site or join virtually. As more people get vaccinated, we are happy to provide safer environments for our colleagues. We believe that COLING 2022 will be one of the conferences, free from the pandemic. The hybrid format gives presenters and sponsors a valuable opportunity to promote their companies in both an online and in-person venue. For the first time in a long time, customers can interact with their sponsor's products first-hand. The online venue, too, gives sponsors the chance to network with those unable to attend the in-person session.

The hybrid format ultimately lends itself to greater exposure for our sponsors: COLING2022 will let you reach more potential partners and customers than ever before!<br><br><br>

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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.

workshop paper

Named entity recognition (e.g., disease mention extraction) is one of the most relevant tasks for data mining in the medical field. Our contribution to this task consists of a transfer learning approach to extracting disease mentions in a corpus from Twitter written in Spanish. We fine-tuned a model based on mBERT and applied post-processing using regular expressions to propagate the entities identified by the model. Our system achieved a competitive strict F1 of 0.851 on the testing data set.

Disease Mention Extraction in Spanish Tweets Using Transfer Learning and Search by Propagation

Introduction of the methodology proposed for the shared Task 2 of the Social Media
Mining for Health Application (SMM4H) in 2022

A solution to SMMH Task 2

This presentation covers participation of the SINAI team in the Task 10 of the Social Media Mining for Health (\#SSM4H) workshop at COLING-2022. This task focuses on leveraging Twitter posts written in Spanish for healthcare research. The objective to identify disease mentions in Twitter posts. The presented system explores large RoBERTa language model on general-domain data for the disease recognition task. We also present a text pre-processing methodology and describe an initial weakly-supervised fine-tuning phase alongside with a submission post-processing procedure. The system obtained 0.77 F1-score during the official evaluation.

Weakly Supervised fine-tuning for Disease Mention Detection in Spanish Tweets

This paper summarizes the CLaC submission for SMM4H 2022 Task 10 which concerns the recognition of diseases mentioned in Spanish tweets. Before classifying each token, we encode each token with a transformer encoder using features from Multilingual RoBERTa Large, UMLS gazetteer, and DISTEMIST gazetteer, among others. We obtain a strict F1 score of 0.869, with competition mean of 0.675, standard deviation of 0.245, and median of 0.761.

Using Medical Gazetteers for Named-Entity Recognition of Disease Mentions in Spanish Tweets

The Visual Dialogue (VD) task has recently received increasing attention in AI research. VD 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 multi-modal data features remains to be a challenge. In this work, we propose a knowledge transfer method with visual prompt (\textbf{VPTG}) fusing multi-modal data, which is a flexible module that can utilize the text-only seq2seq model to handle VD 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. Moreover, 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.

Knowledge Transfer with Visual Prompt in Multi-modal Dialogue Understanding and Generation

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.

Model Transfer for Event tracking as Transcript Understanding for Videos of Small Group Interaction

We present an empirical study investigating the influence of automatic speech recognition (ASR) errors on the spoken implicit discourse relation recognition (IDRR) task. We construct a spoken dataset for this task based on the Penn Discourse Treebank 2.0 (Prasad et al., 2008). On this dataset, we conduct “Cascaded” experiments employing state-of-the-art ASR and text-based IDRR models and find that the ASR errors significantly decrease the IDRR performance. In addition, the “Cascaded” approach does remarkably better than an “End-to-End”
one that directly predicts a relation label for each input argument speech pair.

Investigating the Impact of ASR Errors on Spoken Implicit Discourse Relation Recognition

One of the modern challenges in AI is the access to high-quality and annotated data, especially in NLP; that's why augmentation is gaining importance. In computer vision, where image data augmentation is standard, text data augmentation in NLP is complex due to the high complexity of language. And we have seen advantages of augmentation where there are fewer data available, and it can play a massive role in improving the model's accuracy and performance. We have implemented Augmentation in Pairwise sentence scoring in the biomedical domain.

By experimenting with our approach to downstream tasks on biomedical data, we have looked into the solution to improve Bi-encoders' sentence transformer performance using augmented data-set generated by cross-encoders fine-tuned on Biosses and MedNLI on pretrained Bio-BERT model. It has significantly improved the results with respect to the only the model only trained on Gold data for the respective tasks.

Augmented Bio-SBERT

This paper presents the usage of the RELATE platform (https://relate.racai.ro) for translation tasks involving the Romanian language. Using this platform, it is possible to perform text and speech data translations, either for single documents or for entire corpora. Furthermore, the platform was successfully used in international projects to create new resources useful for Romanian language translation.

Romanian language translation in the RELATE platform

Tokenization has been shown to impact the quality of downstream tasks, such as Neural Machine Translation (NMT), which is susceptible to out-of-vocabulary words and low frequency training data. Current state-of-the-art algorithms have been helpful in addressing the issues of out-of-vocabulary words, bigger vocabulary sizes and token frequency by implementing subword segmentation. We argue, however, that there is still room for improvement, in particular regarding low-frequency tokens in the training data. In this paper, we present "High Frequency Tokenizer", or HFT, a new language-independent subword segmentation algorithm that addresses this issue. We also propose a new metric to measure the frequency coverage of a tokenizer's vocabulary, based on a frequency rank weighted average of the frequency values of its items. We experiment with a diverse set of language corpora, vocabulary sizes, and writing systems and report improvements on both frequency statistics and on the average length of the output. We also observe a positive impact on downstream NMT.

Next from COLING 2022

Disease Mention Extraction in Spanish Tweets Using Transfer Learning and Search by Propagation

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SMM4H Task 2: Stance and premise detection in tweets about COVID-19 health mandates

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