XFACT Team0331 at P erspective A rg2024: Sampling from Bounded Clusters for Diverse Relevant Argument Retrieval (2024)

Abstract: This paper reports on the argument mining system submitted to the ArgMining workshop 2024 for The Perspective Argument Retrieval Shared Task (Falk et al., 2024). We com- bine the strengths of a smaller Sentence BERT model and a Large Language Model: the for- mer is fine-tuned for a contrastive embedding objective and a classification objective whereas the latter is invoked to augment the query and populate the latent space with diverse relevant arguments. We conduct an ablation study on these components to find that each contributes substantially to the diversity and relevance cri- teria for the top-k retrieval of arguments from the given corpus.

## introduction argument retrieval remains a challenging problem in the natural language processing domain, when considered jointly with perspectives and diversity. the problem is defined as the collection of claims carrying a stance towards a query. a query may be of various topical widths ranging from an entire issue or a single statement. arguments are a widely utilized discursive tools, and performant systems of argument recognition will prove useful in further advancing bias analyses, slant measurement @xcite , content recommendation, and text generation tasks @xcite . perspectives can prove a useful clue in the argument retrieval problem but at the same time bring about an additional challenge. especially with shorter texts, such as the ones handled in this shared task, the demographic profile of the argument author may be of use in trying to extract opinions grounded in various populations. in this paper, we present the work conducted by our team, "xfact team0331", for the argmining 2024 shared task of perspective argument retrieval. our findings indicate feasibility of the system design across the three scenarios, helping us better understand the complexities of taking perspectives into consideration when retrieving arguments. this shared task involves a unique challenge in terms of perspectives and socio-cultural variables. not only are the systems required to pursue relevance to a given query, but they are also assessed in terms of the demographic profile of the argument authors. ## related work recent years have seen rapid progress of argument retrieval along several lines of research. @xcite studied argument extraction in the scientific text domain. later works extend the argument retrieval work to arbitrary domains and eventually the entire world wide web, such as in @xcite and similar systems discussed in the seminal text @xcite . @xcite designed an argument search engine involving an indexing process, which takes candidate documents over the web and indexes the assessed arguments therein into a corpus, and a retrieval process, which, upon arrival of a query, ranks and presents relevant indices of arguments. @xcite put together an offline component and an online component, which account for the indexing and retrieval tasks, respectively. in the closely related problem of stance detection, @xcite proposed methods for recognizing stance across texts from multiple domains, with their design of label embeddings in the latent space that adapt to the arbitrary topic at hand. @xcite presented a similar approach leveraging a topic-guided sampler for alleviating inherent imbalance in the data. then, a pre-trained language model is fine-tuned against a contrastive learning objective for recognizing the in-favor and the against statements. in news writing, @xcite , @xcite , and @xcite profiled media sources by measuring their bias in terms of factuality reporting and any conveyed political ideology. @xcite solved a five-scale political stance prediction problem by incorporating texts from various sources beyond news articles, such as reddit posts, and employs a multi-granularity hierarchy on the texts to capture any subtleties carrying stance information. in a related work by @xcite , a triplet loss was imposed on an anchor article with two same-story versions -one leftist and the other rightist -to train a language model to identify and distinguish ideology-informed representations between articles. argumentative language modeling techniques have also been studied. for instance, @xcite proposed counterargument generation methods assisting language models with knowledge graphs such that the natural language inference process can determine the entailment/relevance of a claim more effectively. @xcite studied a similar problem but with an additional pursuit for fairness in argument generation. for every instance deemed biased, a counter-stereotypical statement is synthesized and used in training. ## task the argmining 2024 shared task for perspective argument retrieval consists of the following three retrieval scenarios: the dataset is originally from vamvas and sennrich (2020), where comments in the french, german, and italian language are organized across various political issues (queries) for the 2019 swiss federal elections. ## method we propose a novel approach to retrieve relevant and diverse arguments. major components of our design are as follows: the overview of the proposed system is presented in fig. 1 . an embedding model produces latent vectors of arguments and a given query. these are then spread out in the latent space, to be clustered according to the nearest generated argument. more details follow in section 4.5. ## results our results are presented in tables 1 and 2 . all reportings are on the dev sets, averaged across the three scenarios, due to limited print space. ## conclusion we present an argument retrieval system combining the strengths of a fine-tuned embedding model and open-source large language models. the embedding model is trained to tell apart relevant arguments from the irrelevant arguments through triplet loss signals leveraging hard negatives. at the same time, the embedding model is also exposed to indistribution classification examples, such that it detects out-of-distribution (irrelevant) arguments and relays the anomalous finding to the relevance cut-off criterion. llms provide diverse anchors to which a given corpus argument may be checked against for membership, and then the member densities are utilized for diverse representation of arguments relevant to a given query. turnover costs, as it is generally more expensive to hire and train new employees. 5. diversity and inclusion: protecting older workers from dismissal promotes diversity and inclusion in the workplace, which can lead to better decision-making and innovation. 6. social justice: strengthening protection against dismissal of older workers can be seen as a step towards social justice, ensuring that all workers are treated fairly regardless of age. 7. improved morale: protecting older workers from dismissal can improve morale and job satisfaction, as employees feel valued and secure in their positions. 8. reduced unemployment: by protecting older workers from dismissal, the number of unemployed individuals may decrease, as they are less likely to be laid off. 9. long-term planning: companies that protect older workers from dismissal can benefit from long-term planning, as they can rely on the experience and knowledge of these employees. 10. legal compliance: strengthening protection against dismissal of older workers can help companies comply with existing labor laws and regulations. arguments against strengthening protection against the dismissal of older workers: