@inproceedings{tatariya-etal-2025-interplay,
    title = "On the Interplay between Positional Encodings, Morphological Complexity, and Word Order Flexibility",
    author = "Tatariya, Kushal  and
      Poelman, Wessel  and
      de Lhoneux, Miryam",
    editor = "Inui, Kentaro  and
      Sakti, Sakriani  and
      Wang, Haofen  and
      Wong, Derek F.  and
      Bhattacharyya, Pushpak  and
      Banerjee, Biplab  and
      Ekbal, Asif  and
      Chakraborty, Tanmoy  and
      Singh, Dhirendra Pratap",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.ijcnlp-long.95/",
    pages = "1761--1778",
    ISBN = "979-8-89176-298-5",
    abstract = "Language model architectures are predominately first created for English and afterwards applied to other languages. This can lead to problems for languages that are structurally different from English. We study one specific architectural choice: positional encodings. We do this through the lens of the trade-off hypothesis: the supposed interplay between morphological complexity and word order flexibility. This hypothesis states there exists a trade-off between the two: a more morphologically complex language can have a more flexible word order, and vice-versa. Positional encodings are a direct target to investigate the implications of this hypothesis in relation to language modelling. We pre-train and evaluate three monolingual model variants with absolute, relative and no position encodings for seven typologically diverse languages and evaluate on four downstream tasks. We fail to find a consistent trend with various proxies for morphological complexity and word order flexibility. Our work shows choice of tasks, languages, and metrics are essential for drawing stable conclusions."
}