publications

You can also find my articles on my Google Scholar.

• Effects of Collaboration on the Performance of Interactive Theme Discovery Systems

  Chen, A. P., Das, R., Srinivas, D., Barry, A., Seniw, M., Pacheco, M. L.

  ACL·2026

  NLP-assisted solutions to support qualitative data analysis have gained considerable traction. However, no unified evaluation framework exists which can account for the many different settings in which qualitative researchers may employ them.

  BibTeXPDFDOI

  @inproceedings{chen-etal-2026-effects,
      abbr = {ACL},
      preview = {inter_sys.png},
      title = "Effects of Collaboration on the Performance of Interactive Theme Discovery Systems",
      author = "Chen, Alvin Po-Chun  and
        Das, Rohan  and
        Srinivas, Dananjay  and
        Barry, Alexandra  and
        Seniw, Maksim  and
        Pacheco, Maria Leonor",
      editor = "Liakata, Maria  and
        Moreira, Viviane P.  and
        Zhang, Jiajun  and
        Jurgens, David",
      booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
      month = jul,
      year = "2026",
      address = "San Diego, California, United States",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/2026.acl-long.1968/",
      doi = "10.18653/v1/2026.acl-long.1968",
      pages = "42507--42526",
      ISBN = "979-8-89176-390-6",
      abstract = "NLP-assisted solutions to support qualitative data analysis have gained considerable traction. However, no unified evaluation framework exists which can account for the many different settings in which qualitative researchers may employ them. In this paper, we propose a framework to evaluate the way collaboration settings may produce different research outcomes across a variety of interactive systems. Specifically, we study the impact of synchronous vs. asynchronous collaboration using three different NLP-assisted qualitative research tools and present a comprehensive analysis of the differences in the consistency, cohesiveness, and correctness of their outcomes."
  }

  

• Enhancing Structured Meaning Representations with Aspect Classification

  Post, C. B., Bontempo, P., Milliken, A., Chen, A. P., Derby, N., Khatwani, S., Nabieva, S., Sairam, K., Palmer, A.

  arXiv preprint·2026

  To fully capture the meaning of a sentence, semantic representations should encode aspect, which describes the internal temporal structure of events. In graph-based meaning representation frameworks such as Uniform Meaning Representations (UMR), aspect lets one know how events unfold over time, including distinctions such as states, activities, and completed events.

  BibTeXarXiv

  @misc{post2026enhancingstructuredmeaningrepresentations,
        preview = {neurospect.png},
        title={Enhancing Structured Meaning Representations with Aspect Classification}, 
        author={Claire Benét Post and Paul Bontempo and August Milliken and Alvin Po-Chun Chen and Nicholas Derby and Saksham Khatwani and Sumeyye Nabieva and Karthik Sairam and Alexis Palmer},
        year={2026},
        eprint={2603.24797},
        archivePrefix={arXiv},
        primaryClass={cs.CL},
        url={https://arxiv.org/abs/2603.24797}, 
        abstract = "To fully capture the meaning of a sentence, semantic representations should encode aspect, which describes the internal temporal structure of events. In graph-based meaning representation frameworks such as Uniform Meaning Representations (UMR), aspect lets one know how events unfold over time, including distinctions such as states, activities, and completed events. Despite its importance, aspect remains sparsely annotated across semantic meaning representation frameworks. This has, in turn, hindered not only current manual annotation, but also the development of automatic systems capable of predicting aspectual information. In this paper, we introduce a new dataset of English sentences annotated with UMR aspect labels over Abstract Meaning Representation (AMR) graphs that lack the feature. We describe the annotation scheme and guidelines used to label eventive predicates according to the UMR aspect lattice, as well as the annotation pipeline used to ensure consistency and quality across annotators through a multi-step adjudication process. To demonstrate the utility of our dataset for future automation, we present baseline experiments using three modeling approaches. Our results establish initial benchmarks for automatic UMR aspect prediction and provide a foundation for integrating aspect into semantic meaning representations more broadly."
  }

  

• Mothman at SemEval-2024 Task 9: An Iterative System for Chain-of-Thought Prompt Optimization

  Chen, A. P., Groshan, R., Bayern, S.

  SemEval·2024

  Extensive research exists on the performance of large language models on logic-based tasks, whereas relatively little has been done on their ability to generate creative solutions on lateral thinking tasks. The BrainTeaser shared task tests lateral thinking and uses adversarial datasets to prevent memorization, resulting in poor performance for out-of-the-box models.

  BibTeXPDFDOI

  @inproceedings{chen-etal-2024-mothman,
      abbr = {SemEval},
      preview = {semeval.png},
      title = "Mothman at {S}em{E}val-2024 Task 9: An Iterative System for Chain-of-Thought Prompt Optimization",
      author = "Chen, Alvin Po-Chun  and
        Groshan, Ray  and
        von Bayern, Sean",
      editor = {Ojha, Atul Kr.  and
        Do{\u{g}}ru{\"o}z, A. Seza  and
        Tayyar Madabushi, Harish  and
        Da San Martino, Giovanni  and
        Rosenthal, Sara  and
        Ros{\'a}, Aiala},
      booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
      month = jun,
      year = "2024",
      address = "Mexico City, Mexico",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/2024.semeval-1.263/",
      doi = "10.18653/v1/2024.semeval-1.263",
      pages = "1876--1888",
      abstract = "Extensive research exists on the performance of large language models on logic-based tasks, whereas relatively little has been done on their ability to generate creative solutions on lateral thinking tasks. The BrainTeaser shared task tests lateral thinking and uses adversarial datasets to prevent memorization, resulting in poor performance for out-of-the-box models. We propose a system for iterative, chain-of-thought prompt engineering which optimizes prompts using human evaluation. Using this shared task, we demonstrate our system{'}s ability to significantly improve model performance by optimizing prompts and evaluate the input dataset."
  }

  

• Annotate Chinese Aspect with UMR——a Case Study on The Little Prince

  Ge, S., Li, Z., Chen, A. P., Wang, G.

  LREC-COLING·2024

  Aspect is a valuable tool for determining the perspective from which an event is observed, allowing for viewing both at the situation and viewpoint level. Uniform Meaning Representation (UMR) seeks to provide a standard, typologically-informed representation of aspects across languages.

  BibTeXPDF

  @inproceedings{ge-etal-2024-annotate,
      abbr = {LREC-COLING},
      preview = {chinese_umr.svg},
      title = "Annotate {C}hinese Aspect with {UMR}{---}{---}a Case Study on \textit{{T}he {L}ittle {P}rince}",
      author = "Ge, Sijia  and
        Li, Zilong  and
        Chen, Alvin Po-Chun  and
        Wang, Guanchao",
      editor = "Calzolari, Nicoletta  and
        Kan, Min-Yen  and
        Hoste, Veronique  and
        Lenci, Alessandro  and
        Sakti, Sakriani  and
        Xue, Nianwen",
      booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
      month = may,
      year = "2024",
      address = "Torino, Italia",
      publisher = "ELRA and ICCL",
      url = "https://aclanthology.org/2024.lrec-main.104/",
      pages = "1161--1172",
      abstract = "Aspect is a valuable tool for determining the perspective from which an event is observed, allowing for viewing both at the situation and viewpoint level. Uniform Meaning Representation (UMR) seeks to provide a standard, typologically-informed representation of aspects across languages. It employs an aspectual lattice to adapt to different languages and design values that encompass both viewpoint aspect and situation aspects. In the context of annotating the Chinese version of The Little Prince, we paid particular attention to the interactions between aspect values and aspect markers and we also want to know the annotation effectiveness and challenges under the UMR aspectual lattice. During our annotation process, we identified the relationships between aspectual markers and labels. We further categorized and analyzed complex examples that led to low inter-annotator agreement. The factors contributing to disagreement among annotators included the interpretations of lexical semantics, implications, and the influence of aspectual markers, which is related to the inclination of the situation aspect and the exclusivity between the two aspects' perspectives. Overall, our work sheds light on the challenges of aspect annotation in Chinese and highlights the need for more comprehensive guidelines."
  }