Korea, Republic of

Identifying claims in text is a crucial first step in argument mining. In this paper, we investigate factors for the composition of training corpora to improve cross-domain claim detection. To this end, we use four recent argumentation corpora annotated with claims and submit them to several experimental scenarios. Our results
indicate that the &quot;ideal&quot; composition of training corpora is characterized by a large corpus size, homogeneous claim proportions, and less formal text domains.

COLING 2022

On Selecting Training Corpora for Cross-Domain Claim Detection

claim detection

cross-domain

classification

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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.

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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.

Identifying claims in text is a crucial first step in argument mining. In this paper, we investigate factors for the composition of training corpora to improve cross-domain claim detection. To this end, we use four recent argumentation corpora annotated with claims and submit them to several experimental scenarios. Our results
indicate that the "ideal" composition of training corpora is characterized by a large corpus size, homogeneous claim proportions, and less formal text domains.

workshop paper

We develop a novel unified representation for the argumentation mining task facilitating the extracting from text and the labelling of the non-argumentative units and argumentation components---premises, claims, and major claims---and the argumentative relations---premise to claim or premise in a support or attack relation, and claim to major-claim in a for or against relation---in an end-to-end machine learning pipeline. This tightly integrated representation combines the component and relation identification sub-problems and enables a unitary solution for detecting argumentation structures. This new representation together with a new deep learning architecture composed of a mixed embedding method, a multi-head attention layer, two biLSTM layers, and a final linear layer obtain state-of-the-art accuracy on the Persuasive Essays dataset. Also, we have introduced a decoupled solution to identify the entities and relations first, and on top of that a second model is used to detect distance between the detected related components. An augmentation of the corpus (paragraph version) by including copies of major claims has further increased the performance.

A Unified Representation and a Decoupled Deep Learning Architecture for Argumentation Mining of Students' Persuasive Essays

The successful application of argument mining in the legal domain can dramatically impact many disciplines related to law. For this purpose, we present Demosthenes, a novel corpus for argument mining in legal documents, composed of 40 decisions of the Court of Justice of the European Union on matters of fiscal state aid. The annotation specifies three hierarchical levels of information: the argumentative elements, their types, and their argument schemes. In our experimental evaluation, we address 4 different classification tasks, combining advanced language models and traditional classifiers.

Detecting Arguments in CJEU Decisions on Fiscal State Aid

Argument Unit Recognition and Classification aims at identifying argument units from text and classifying them as pro or against. One of the design choices that need to be made when developing systems for this task is what the unit of classification should be: segments of tokens or full sentences. Previous research suggests that fine-tuning language models on the token-level yields more robust results for clas- sifying sentences compared to training on sentences directly. We reproduce the study that originally made this claim and further investigate what exactly token-based systems learned better compared to sentence-based ones. We develop systematic tests for analysing the behavioural differences between the token-based and the sentence-based system. Our results show that token-based models are generally more robust than sentence-based models both on manually perturbed examples and on specific subpopulations of the data.

Perturbations and Subpopulations for Testing Robustness in Token-Based Argument Unit Recognition

The growing interest in developing corpora of persuasive texts has promoted applications in automated systems, e.g., debating and essay scoring systems; however, there is little prior work mining image persuasiveness from an argumentative perspective. To expand persuasiveness mining into a multi-modal realm, we present a multi-modal dataset, ImageArg, consisting of annotations of image persuasiveness in tweets. The annotations are based on a persuasion taxonomy we developed to explore image functionalities and the means of persuasion. We benchmark image persuasiveness tasks on ImageArg using widely used multimodal learning methods. The experimental results show that our dataset offers a useful resource for this rich and challenging topic, and there is ample room for modeling improvement.

ImageArg: A Multi-modal Tweet Dataset for Image Persuasiveness Mining

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.

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

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.

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On Selecting Training Corpora for Cross-Domain Claim Detection

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A Unified Representation and a Decoupled Deep Learning Architecture for Argumentation Mining of Students' Persuasive Essays

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