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

[![](https://assets.underline.io/uploads/markdown_image/1/image/81a3c0317d24f663b49b996875024d45.png)](https://aclanthology.org/volumes/2022.coling-1/)



### 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>

To gain access to this event page you are required to register.<br> Find more information and pay the registration fee on event the organizer’s website **[https://coling2022.org/reg](https://coling2022.org/reg)**

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

workshop paper

The paper presents our system for coreference
resolution in Polish. We compare the system
with previous works for the Polish language
as well as with the multilingual approach in
the CRAC 2022 Shared Task on Multilingual
Coreference Resolution thanks to a universal,
multilingual data format and evaluation tool.
We discuss the accuracy, computational performance, and evaluation approach of the new System which is a faster, end-to-end solution.


Coreference Resolution for Polish: Improvements within the CRAC 2022 Shared Task

We describe the winning submission to the CRAC 2022 Shared Task on Multilingual Coreference Resolution. Our system first solves mention detection and then coreference linking on the retrieved spans with an antecedent-maximization approach, and both tasks are fine-tuned jointly with shared Transformer weights. We report results of fine-tuning a wide range of pretrained models. The center of this contribution are fine-tuned multilingual models. We found one large multilingual model with sufficiently large encoder to increase performance on all datasets across the board, with the benefit not limited only to the underrepresented languages or groups of typologically relative languages.

Effectivity of Multilingual Models for Coreference Resolution

T4 Knowledge Graph Embeddings for NLP: From Theory to Practice Part 2

T4 Knowledge Graph Embeddings for NLP: From Theory to Practice Part 1

Researchers across disciplines use Twitter geolocation tools to filter data for desired locations. These tools have largely been trained and tested on English tweets, often originating in the United States from almost a decade ago. Despite the importance of these tools for data curation, the impact of tweet language, country of origin, and creation date on tool performance remains largely unknown. We explore these issues with Carmen, a popular tool for Twitter geolocation. To support this study we introduce Carmen 2.0, a major update which includes the incorporation of GeoNames, a gazetteer that provides much broader coverage of locations. We evaluate using two new Twitter datasets, one for multilingual, multiyear geolocation evaluation, and another for usage trends over time. We found that language, country origin, and time does impact geolocation tool performance.

Changes in Tweet Geolocation over Time: A Study with Carmen 2.0

Entity Linking (EL) is the gateway into Knowledge Bases. Recent advances in EL utilize dense retrieval approaches for Candidate Generation, which addresses some of the shortcomings of the Lookup based approach of matching NER mentions against pre-computed dictionaries. In this work, we show that in the domain of Tweets, such methods suffer as users often include informal spelling, limited context, and lack of specificity, among other issues. We investigate these challenges on a large and recent Tweets benchmark for EL, empirically evaluate lookup and dense retrieval approaches, and demonstrate a hybrid solution using long contextual representation from Wikipedia is necessary to achieve considerable gains over previous work, achieving 0.93 recall.

Robust Candidate Generation for Entity Linking on Short Social Media Texts

On social media, additional context is often present in the form of annotations and meta-data such as the post’s author, mentions, Hashtags, and hyperlinks. We refer to these annotations as Non-Textual Units (NTUs). We posit that NTUs provide social context beyond their textual semantics and leveraging these units can enrich social media text representations. In this work we construct an NTU-centric social heterogeneous network to co-embed NTUs. We then principally integrate these NTU embeddings into a large pretrained language model by fine-tuning with these additional units. This adds context to noisy short-text social media. Experiments show that utilizing NTU-augmented text representations significantly outperforms existing text-only baselines by 2-5% relative points on many downstream tasks highlighting the importance of context to social media NLP. We also highlight that including NTU context into the initial layers of language model alongside text is better than using it after the text embedding is generated. Our work leads to the generation of holistic general purpose social media content embedding.

NTULM: Enriching Social Media Text Representations with Non-Textual Units

In this paper, we utilize recent advancements in social media natural language processing to obtain state-of-the-art syntactic dependency parsing results for social media English. We observe performance gains of 3.4 UAS and 4.0 LAS against the previous state-of-the-art as well as less disparity between African-American and Mainstream American English dialects. We demonstrate the computational social scientific utility of this parser for the task of socially embedded entity attribute analysis: for a specified entity, derive its semantic relationships from parses’ rich syntax, and accumulate and compare them across social variables. We conduct a case study on politicized views of U.S. official Anthony Fauci during the COVID-19 pandemic.

Cross-Dialect Social Media Dependency Parsing for Social Scientific Entity Attribute Analysis

This talk will discuss symbolic representations of sentences in context, focusing on abstract meaning representations (AMR), examining their capability for capturing certain aspects of meaning. A focus will be how AMR’s can be expanded to encompass figurative language, the recovery of implicit arguments and relations between events. These examples will be in English, and indeed some features of AMR are English-centric. Uniform Meaning Representations, a multi-sentence annotation scheme that is revising AMRs to make them more suitable for other languages, especially low resource languages will be introduced. UMRs include more formal logical scope, number, tense, aspect and modality as well as temporal relations. The talk will conclude with a discussion of ways in which these meaning representations can be enriched even more, by mapping to Wikidata Qnodes or through accessing the rich logical representations present in VerbNet, and thereby provide additional benefit to challenging applications.

Deep Semantics

NLP researches in developing economies are in many aspects unique compared to those in developed countries. For developing economies with middle and low incomes such as the Philippines, most NLP work focuses on relevant and useful applications for the immediate betterment of society, and involves minority languages of underrepresented regions that are usually low-resource. On the other hand, most of the global NLP research work has been focused on the major languages of the world, especially those of developed countries. Our works contribute to the advancement of research in multi-linguality for Philippine languages, adding to the complexity of NLP application development. Domain areas of these applications include health, disaster risk reduction management and climate change, and gender.
Moreover, organizational and collaborative efforts provide a critical infrastructure in building this
network of Philippine NLP researchers.

Next from COLING 2022

Coreference Resolution for Polish: Improvements within the CRAC 2022 Shared Task

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

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