Korea, Republic of

False medical information on social media poses harm to people&#39;s health.
While the need for biomedical fact-checking has been recognized in recent
years, user-generated medical content has received comparably little attention.
At the same time, models for other text genres might not be reusable, because
the claims they have been trained with are substantially different. For
instance, claims in the SciFact dataset are short and focused: &quot;Side effects
associated with antidepressants increases risk of stroke&quot;. In contrast, social
media holds naturally-occurring claims, often embedded in additional context:
&quot;`If you take antidepressants like SSRIs, you could be at risk of a condition
called serotonin syndrome&#39; Serotonin syndrome nearly killed me in 2010. Had
symptoms of stroke and seizure.&quot; This showcases the mismatch between real-world
medical claims and the input that existing fact-checking systems expect. To
make user-generated content checkable by existing models, we propose to
reformulate the social-media input in such a way that the resulting claim
mimics the claim characteristics in established datasets. To accomplish this,
our method condenses the claim with the help of relational entity information
and either compiles the claim out of an entity-relation-entity triple or
extracts the shortest phrase that contains these elements. We show that the
reformulated input improves the performance of various fact-checking models as
opposed to checking the tweet text in its entirety.

COLING 2022

Entity-based Claim Representation Improves Fact-Checking of Medical Content in Tweets

claim extraction

biomedical data

fake news

tweets

claim detection

fact-checking

social media

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, three amazing keynote events and two new initiatives of invited talks: Spotlight Talks for Young Rising Stars (STIRS) and The Next Big Idea Talks. Posters (including Findings of ACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop will have an oral session and a poster session as part of Poster Session 1. The program also features eight Tutorials and 28 Workshops. 

 
We wish you a wonderful conference! 
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[**Conference Handbook**](https://drive.google.com/file/d/1_BUCMfhMVrjG9E2e71aHdHeE28KSje0l/view?usp=sharing) 
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ACL 2022

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

False medical information on social media poses harm to people's health.
While the need for biomedical fact-checking has been recognized in recent
years, user-generated medical content has received comparably little attention.
At the same time, models for other text genres might not be reusable, because
the claims they have been trained with are substantially different. For
instance, claims in the SciFact dataset are short and focused: "Side effects
associated with antidepressants increases risk of stroke". In contrast, social
media holds naturally-occurring claims, often embedded in additional context:
"`If you take antidepressants like SSRIs, you could be at risk of a condition
called serotonin syndrome' Serotonin syndrome nearly killed me in 2010. Had
symptoms of stroke and seizure." This showcases the mismatch between real-world
medical claims and the input that existing fact-checking systems expect. To
make user-generated content checkable by existing models, we propose to
reformulate the social-media input in such a way that the resulting claim
mimics the claim characteristics in established datasets. To accomplish this,
our method condenses the claim with the help of relational entity information
and either compiles the claim out of an entity-relation-entity triple or
extracts the shortest phrase that contains these elements. We show that the
reformulated input improves the performance of various fact-checking models as
opposed to checking the tweet text in its entirety.

workshop paper

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

To mitigate the lack of diverse dialogue summarization datasets in academia, we present methods to utilize non-dialogue summarization data for enhancing dialogue summarization systems. We apply transformations to document summarization data pairs to create training data that better befit dialogue summarization. The suggested transformations also retain desirable properties of non-dialogue datasets, such as improved faithfulness to the source text. We conduct extensive experiments across both English and Korean to verify our approach. Although absolute gains in ROUGE naturally plateau as more dialogue summarization samples are introduced, utilizing non-dialogue data for training significantly improves summarization performance in zero- and few-shot settings and enhances faithfulness across all training regimes.

Leveraging Non-dialogue Summaries for Dialogue Summarization

We propose a study on multimodal argument mining in the domain of political debates. We collate and extend existing corpora and provide an initial empirical study on multimodal architectures, with a special emphasis on input encoding methods. Our results provide interesting indications about future directions in this important domain.

Multimodal Argument Mining: A Case Study in Political Debates

We address the problem of automatically predicting the quality of a conclusion given a set of (textual) premises of an argument, focusing in particular on the task of predicting the validity and novelty of the argumentative conclusion. We propose a multi-task approach that jointly predicts the validity and novelty of the textual conclusion, relying on pre-trained language models fine-tuned on the task. As training data for this task is scarce and costly to obtain, we experimentally investigate the impact of data augmentation approaches for improving the accuracy of prediction compared to a baseline that relies on task-specific data only. We consider the generation of synthetic data as well as the integration of datasets from related argument tasks. We show that especially our synthetic data, combined with class-balancing and instance-specific learning rates, substantially improves classification results (+15.1 points in F1-score). Using only training data retrieved from related datasets by automatically labeling them for validity and novelty, combined with synthetic data, outperforms the baseline by 11.5 points in F1-score.
Demo at: https://huggingface.co/spaces/pheinisch/ConclusionValidityNoveltyClassifier-Augmentation

Data Augmentation for Improving the Prediction of Validity and Novelty of Argumentative Conclusions

Standard practice for evaluating the performance of machine learning models for argument mining is to report different metrics such as accuracy or F1. However, little is usually known about the model's stability and consistency when deployed in real-world settings. In this paper, we propose a robustness evaluation framework to guide the design of rigorous argument mining models. As part of the framework, we introduce several novel robustness tests tailored specifically to argument mining tasks. Additionally, we integrate existing robustness tests designed for other natural language processing tasks and re-purpose them for argument mining. Finally, we illustrate the utility of our framework on two widely used argument mining corpora, UKP topic-sentences and IBM Debater Evidence Sentence. We argue that our framework should be used in conjunction with standard performance evaluation techniques as a measure of model stability.

A Robustness Evaluation Framework for Argument Mining

In scientific papers, arguments are essential for explaining authors' findings. As substrates of the reasoning process, arguments are often decorated with discourse indicators such as "which shows that" or "suggesting that". However, it remains understudied whether discourse indicators by themselves can be used as effective markers of the local argument components (LACs) in the body text that support the main claim in the abstract, i.e., the global argument. In this work, we investigate whether discourse indicators reflect the global premise and conclusion. We construct a set of regular expressions for over 100 word- and phrase-level discourse indicators and measure the alignment of LACs extracted by discourse indicators with the global arguments. We find a positive correlation between the alignment of local premises and local conclusions. However, compared to a simple textual intersection baseline, discourse indicators achieve lower ROUGE scores and have limited capability of extracting LACs relevant to the global argument; thus their role in scientific reasoning is less salient than expected.

Do Discourse Indicators Reflect the Main Arguments in Scientific Papers?

In this paper, we describe our work for the CreativeSumm 2022 Shared Task, Automatic Summarization for Creative Writing. 
The task is to summarize movie scripts, which is challenging due to their long length and complex format. 
To tackle this problem, we present a two-stage summarization approach using both the abstractive and an extractive summarization methods.
In addition, we preprocess the script to enhance summarization performance. The results of our experiment demonstrate that the presented approach outperforms baseline models in terms of standard summarization evaluation metrics.

A Twe-Stage Summarization Model using Scene Attributes

This paper documents our approach for the Creative-Summ 2022 shared task for Automatic Summarization of Creative Writing. For this purpose, we develop an automatic summarization pipeline where we leverage a denoising autoencoder for pretraining sequence-to-sequence models and fine-tune it on a large-scale abstractive screenplay summarization dataset to summarize TV transcripts from primetime shows. Our pipeline divides the input transcript into smaller conversational blocks, removes redundant text, summarises the conversational blocks, obtains the block-wise summaries, cleans, structures, and then integrates the summaries to create the meeting minutes. Our proposed system achieves some of the best scores across multiple metrics(lexical, semantical) in the Creative-Summ shared task.

Automatic Summarization for Creative Writing: BART based Pipeline Method for Generating Summary of Movie Scripts

Survey Variable Identification (SV-Ident) deals with automatically identifying survey varible mentions in scientific texts. This is important because it increases access to research and improves reproducibility. We present a novel dataset for SV-Ident, findings of the shared task, and future directions.

Overview of the SV-Ident 2022 Shared Task

In this paper we report on our submission to the Multidocument Summarisation for Literature Review (MSLR) shared task. Specifically, we adapt PRIMERA to the biomedical domain by placing global attention on important biomedical entities in several ways. We analyse the outputs of the 23 resulting models, and report patterns in the results related to the presence of additional global attention, number of training steps, and the input configuration.

LED down the rabbit hole: exploring the potential of global attention for biomedical multi-document summarisation

In this paper, we present the first empirical study for Vietnamese disfluency detection. To conduct this study, we first create a disfluency detection dataset for Vietnamese, with manual annotations over two disfluency types. We then empirically perform experiments using strong baseline models, and find that: automatic Vietnamese word segmentation improves the disfluency detection performances of the baselines, and the highest performance results are obtained by fine-tuning pre-trained language models in which the monolingual model PhoBERT for Vietnamese does better than the multilingual model XLM-R.

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Entity-based Claim Representation Improves Fact-Checking of Medical Content in Tweets

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Leveraging Non-dialogue Summaries for Dialogue Summarization

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