@misc{thomas2026semeval2026task6clarity,
      title={SemEval-2026 Task 6: CLARITY -- Unmasking Political Question Evasions}, 
      author={Konstantinos Thomas and Giorgos Filandrianos and Maria Lymperaiou and Chrysoula Zerva and Giorgos Stamou},
      year={2026},
      eprint={2603.14027},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2603.14027 },
} 


@inproceedings{thomas-etal-2024-never,
    title = "``{I} Never Said That'': A dataset, taxonomy and baselines on response clarity classification",
    author = "Thomas, Konstantinos  and
      Filandrianos, Giorgos  and
      Lymperaiou, Maria  and
      Zerva, Chrysoula  and
      Stamou, Giorgos",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.findings-emnlp.300/",
    doi = "10.18653/v1/2024.findings-emnlp.300",
    pages = "5204--5233",
}


@misc{swayamdipta2020datasetcartographymappingdiagnosing,
      title={Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics}, 
      author={Swabha Swayamdipta and Roy Schwartz and Nicholas Lourie and Yizhong Wang and Hannaneh Hajishirzi and Noah A. Smith and Yejin Choi},
      year={2020},
      eprint={2009.10795},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2009.10795}, 
}


@inproceedings{baric-etal-2023-target,
    title = "Target Two Birds With One {ST}o{N}e: Entity-Level Sentiment and Tone Analysis in {C}roatian News Headlines",
    author = "Bari{\'c}, Ana  and
      Majer, Laura  and
      Duki{\'c}, David  and
      Grbe{\v{s}}a-zenzerovi{\'c}, Marijana  and
      Snajder, Jan",
    editor = "Piskorski, Jakub  and
      Marci{\'n}czuk, Micha{\l}  and
      Nakov, Preslav  and
      Ogrodniczuk, Maciej  and
      Pollak, Senja  and
      P{\v{r}}ib{\'a}{\v{n}}, Pavel  and
      Rybak, Piotr  and
      Steinberger, Josef  and
      Yangarber, Roman",
    booktitle = "Proceedings of the 9th Workshop on Slavic Natural Language Processing 2023 (SlavicNLP 2023)",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.bsnlp-1.10/",
    doi = "10.18653/v1/2023.bsnlp-1.10",
    pages = "78--85",
    abstract = "Sentiment analysis is often used to examine how different actors are portrayed in the media, and analysis of news headlines is of particular interest due to their attention-grabbing role. We address the task of entity-level sentiment analysis from Croatian news headlines. We frame the task as targeted sentiment analysis (TSA), explicitly differentiating between sentiment toward a named entity and the overall tone of the headline. We describe SToNe, a new dataset for this task with sentiment and tone labels. We implement several neural benchmark models, utilizing single- and multi-task training, and show that TSA can benefit from tone information. Finally, we gauge the difficulty of this task by leveraging dataset cartography."
}


@inproceedings{zhang-etal-2023-survey,
    title = "A Survey of Multi-task Learning in Natural Language Processing: Regarding Task Relatedness and Training Methods",
    author = "Zhang, Zhihan  and
      Yu, Wenhao  and
      Yu, Mengxia  and
      Guo, Zhichun  and
      Jiang, Meng",
    editor = "Vlachos, Andreas  and
      Augenstein, Isabelle",
    booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.eacl-main.66/",
    doi = "10.18653/v1/2023.eacl-main.66",
    pages = "943--956",
    abstract = "Multi-task learning (MTL) has become increasingly popular in natural language processing (NLP) because it improves the performance of related tasks by exploiting their commonalities and differences. Nevertheless, it is still not understood very well how multi-task learning can be implemented based on the relatedness of training tasks. In this survey, we review recent advances of multi-task learning methods in NLP, with the aim of summarizing them into two general multi-task training methods based on their task relatedness: (i) joint training and (ii) multi-step training. We present examples in various NLP downstream applications, summarize the task relationships and discuss future directions of this promising topic."
}

@misc{hanneke2026datadoesnthelplimits,
      title={When More Data Doesn't Help: Limits of Adaptation in Multitask Learning}, 
      author={Steve Hanneke and Mingyue Xu},
      year={2026},
      eprint={2601.20774},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2601.20774}, 
}

@article{silla2011survey,
  title   = {A survey of hierarchical classification across different application domains},
  author  = {Silla, Carlos N. Jr. and Freitas, Alex A.},
  journal = {Data Mining and Knowledge Discovery},
  volume  = {22},
  number  = {1-2},
  pages   = {31--72},
  year    = {2011},
  publisher = {Springer},
  doi     = {10.1007/s10618-010-0175-9}
}


@inproceedings{devlin-etal-2019-bert,
    title = "{BERT}: Pre-training of Deep Bidirectional Transformers for Language Understanding",
    author = "Devlin, Jacob  and
      Chang, Ming-Wei  and
      Lee, Kenton  and
      Toutanova, Kristina",
    editor = "Burstein, Jill  and
      Doran, Christy  and
      Solorio, Thamar",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/N19-1423/",
    doi = "10.18653/v1/N19-1423",
    pages = "4171--4186",
    abstract = "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7{\%} (4.6{\%} absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
}

@misc{liu2019robertarobustlyoptimizedbert,
      title={RoBERTa: A Robustly Optimized BERT Pretraining Approach}, 
      author={Yinhan Liu and Myle Ott and Naman Goyal and Jingfei Du and Mandar Joshi and Danqi Chen and Omer Levy and Mike Lewis and Luke Zettlemoyer and Veselin Stoyanov},
      year={2019},
      eprint={1907.11692},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/1907.11692}, 
}

@article{bull-2003,
author = {Bull, Peter},
year = {2003},
month = {12},
pages = {1-220},
title = {The Microanalysis of Political Communication: Claptrap and Ambiguity},
isbn = {9780203417843},
journal = {The Microanalysis of Political Communication: Claptrap and Ambiguity},
doi = {10.4324/9780203417843}
}

@article{silla-freitas-2011,
  title = "A survey of hierarchical classification across different application domains",
  author = "Silla, Carlos N and Freitas, Alex A",
  journal = "Data Mining and Knowledge Discovery",
  volume = "22",
  number = "1-2",
  pages = "31--72",
  year = "2011"
}

@inproceedings{sage-greco-2026-kclarity,
  title = "{KCL}arity at {S}em{E}val-2026 Task 6: Encoder and Zero-Shot Approaches to Political Evasion Detection",
  author = "Sage, Archie and Greco, Salvatore",
  booktitle = "Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)",
  year = "2026"
}

@inproceedings{mancini-etal-2024-multimodal,
    title = "Multimodal Fallacy Classification in Political Debates",
    author = "Mancini, Eleonora  and
      Ruggeri, Federico  and
      Torroni, Paolo",
    editor = "Graham, Yvette  and
      Purver, Matthew",
    booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = mar,
    year = "2024",
    address = "St. Julian{'}s, Malta",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.eacl-short.16/",
    doi = "10.18653/v1/2024.eacl-short.16",
    pages = "170--178",
    abstract = "Recent advances in NLP suggest that some tasks, such as argument detection and relation classification, are better framed in a multimodal perspective. We propose multimodal argument mining for argumentative fallacy classification in political debates. To this end, we release the first corpus for multimodal fallacy classification. Our experiments show that the integration of the audio modality leads to superior classification performance. Our findings confirm that framing fallacy classification as a multimodal task is essential to capture paralinguistic aspects of fallacious arguments."
}

@article{hanneke-xu-2026-limits,
  title = "When More Data Doesn't Help: Limits of Adaptation in Multitask Learning",
  author = "Hanneke, Steve and Xu, Mingyue",
  journal = "arXiv preprint arXiv:2601.20774",
  year = "2026"
}


@inproceedings{dampa-2025-investigating,
  title = "Investigating Hierarchical Structure in Multi-Label Document Classification",
  author = "Dampa, Artemis",
  booktitle = "Proceedings of the 9th Student Research Workshop associated with RANLP 2025",
  pages = "10--19",
  year = "2025"
}

@inproceedings{wulamu-etal-2025-html,
    title = "{HTML}: Hierarchical Topology Multi-task Learning for Semantic Parsing in Knowledge Base Question Answering",
    author = "Wulamu, Aziguli  and
      Zhengyu, Lyu  and
      Gong, Kaiyuan  and
      Han, Yu  and
      Wang, Zewen  and
      Zhu, Zhihong  and
      Xing, Bowen",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.findings-acl.485/",
    doi = "10.18653/v1/2025.findings-acl.485",
    pages = "9307--9321",
    ISBN = "979-8-89176-256-5",
    abstract = "Knowledge base question answering (KBQA) aims to answer natural language questions by reasoning over structured knowledge bases. Existing approaches often struggle with the complexity of mapping questions to precise logical forms, particularly when dealing with diverse entities and relations. In this paper, we propose Hierarchical Topology Multi-task Learning (HTML), a novel framework that leverages a hierarchical multi-task learning paradigm to enhance the performance of logical form generation. Our framework consists of a main task: generating logical forms from questions, and three auxiliary tasks: entity prediction from the input question, relation prediction for the given entities, and logical form generation based on the given entities and relations. Through joint instruction-tuning, HTML allows mutual guidance and knowledge transfer among the hierarchical tasks, capturing the subtle dependencies between entities, relations, and logical forms. Extensive experiments on public benchmarks show that HTML markedly outperforms both supervised fine-tuning methods and training-free ones based on powerful large language models (e.g., GPT-4), demonstrating its superiority in question understanding and structural knowledge reasoning."
}