arXiv AI
A theoretical model of dynamical grammatical gender shifting based on set-valued set function
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Rethinking and formalising the state across languages: a unified computational learning theory account
arXiv:2608. 00523v2 Announce Type: replace-cross Abstract: The linguistic notion of state has traditionally been restricted to the construct (annexation) state of Afroasiatic languages and treated as a language-specific morphosyntactic phenomenon.
arXiv Computation and Language
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The Limits of BPE Tokenization in Polish: Segmentation-Flexional Forms, Grammatical Anchoring, and First-Person Stability in Inflectional Language Models
The article examines how Byte‑Pair Encoding (BPE) tokenization handles Polish, an inflectional language, and finds that BPE tends to stabilize frequent surface fragments of grammatical exponents rather than true grammatical categories. It introduces the concept of grammatical form anchoring, showing that certain Polish verb forms can signal the speaking subject without an explicit pronoun, and highlights that language models may lack a stable grammatical "I" and can shift gender or mirror user forms. The study proposes Roclawski’s segmentation‑flexional forms as a diagnostic framework and suggests that more stable Polish modeling would require sublexical stabilization, anchoring grammatical form in the inflectional system, and maintaining the grammatical "I" in dialogue.
arXiv Machine Learning
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Automatic Annotation of Ancient Greek Vowel Length
arXiv:2608. 01935v1 Announce Type: cross Abstract: Prior work in Ancient Greek NLP relies on corpora that do not disambiguate the phonemic vowel length of alpha, iota, and ypsilon, together known as the dichrona.
Hugging Face Trending Papers
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The syntax of wh-agreement in Yemeni Ibbi Arabic
This article tackles an important phenomenon in the syntax of Yemeni Ibbi Arabic (YIA), viz. , wh-agreement, a phenomenon common to several languages including Greek, Indonesian, Lubukusu, Irish, etc.
arXiv Computation and Language
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Compact Language, Complex Model Shifts: How and Where Ambiguity and Underspecification Affect LLMs
arXiv:2609.39572v1 Announce Type: new Abstract: We analyze how lexical ambiguity and underspecification affect language model training. We create artificial homonyms and artificial hypernyms as pseud...
arXiv Computation and Language
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Generalization through Lexical Abstraction in Transformer Models: The Case of Functional Words
The study investigates whether pretrained transformer models encode functional words—such as pronouns and adverbs—in a way that mirrors human usage. By comparing embeddings of nouns with those of their functional counterparts in both isolated and parallel sentences, the authors find that functional words occupy a central yet distinct position in embedding space and that parallel lexicalized and functional sentences reside in different subspaces. Experiments show that only a mixed training set of functional and lexicalized sentences reveals shared syntactic and semantic structure, whereas training on either type alone fails to capture this parallelism.