Automatically generating grammatically correct sentences in case-marking languages is hard because nominal case inflection depends on context. In template-based generation, placeholders must be inflected to the right case before insertion, otherwise the result is ungrammatical. We formalise this case selection problem for template slots and present a practical, data-driven solution designed for morphologically rich, case-marking languages, and apply it to Finnish. We automatically derive training instances from raw text via morphological analysis, and fine-tune transformer encoders to predict a distribution over 14 grammatical cases, with and without lemma conditioning. The predicted case is then realized by a morphological generator at deployment. On a held-out test set in the lemma-conditioned setting, our model attains 89.1% precision, 81.1% recall, and 84.2% F1, with recall@3 of 93.3% (macro averages). The probability outputs support abstention and top-k- suggestion User Interfaces, enabling robust, lightweight template filling for production use in multiple domains, such as customer messaging. The pipeline assumes only access to raw text plus a morphological analyzer and generator, and can be applied to other languages with productive case systems.

The paper presents a prototype of a web-app designed to automatically generate verb valency lexica based on the Universal Dependencies (UD) treebanks. It offers an overview of the structure of the app, its core functionality, and functional extensions designed to handle treebank-specific features. Besides, the paper highlights the limitations of the prototype and the potential of its further development.

The uniform information density (UID) hypothesis postulates that linguistic units are distributed in a text in such a way that the variance around an average information density is minimized. The relationship between information density and information status (IS) is so far underexplored. In this ongoing work, we project IS annotations on the English section of the CIEP+ corpus (Verkerk Talamo 2024) to parallel sections in other languages. We then use the projected annotations to evaluate the relationship between IS and information content in a typologically diverse sample of languages. Our preliminary findings indicate that there is an effect of information status on information density, with the directionality of the effect depending on language and part of speech.

It is common in cognitive computational linguistics to use language model surprisal as a measure of the information content of units in language production. From here, it is tempting to then apply this to information structure and status, considering surprising mentions to be new and unsurprising ones to be given, providing us with a ready-made continuous metric of information givenness/newness. To see if this conflation is appropriate, we perform regression experiments to see if language model surprisal is actually well predicted by information status as manually annotated, and if so, if this effect is separable from more trivial linguistic information such as parts of speech and word frequency. We find that information status alone is at best a very weak predictor of surprisal, and that surprisal can be much better predicted by the effect of parts of speech, which are highly correlated with both information status and surprisal; and word frequency. We conclude that surprisal should not be used as a continuous representation of information status by itself.

We present MeiteiRoBERTa, the first publicly available monolingual RoBERTa-based language model for Meitei (Manipuri), a low-resource language spoken by over 1.8 million people in Northeast India. Trained from scratch on 76 million words of Meitei text in Bengali script, our model achieves a perplexity of 65.89, representing a 5.2× improvement over multilingual baselines BERT (341.56) and MuRIL (355.65). Through comprehensive evaluation on perplexity, tokenization efficiency, and semantic representation quality, we demonstrate that domain-specific pre training significantly outperforms general-purpose multilingual models for low-resource languages. Our model exhibits superior semantic understanding with 0.769 similarity separation compared to 0.035 for mBERT and near-zero for MuRIL, despite MuRIL’s better tokenization efficiency (fertility: 3.29 vs. 4.65). We publicly release the model, training code, and datasets to accelerate NLP research for Meitei and other underrepresented Northeast Indian languages

Linguistic reference material is a trove of information that can be utilized for the analysis of languages. The material, in the form of grammar books and sketches, has been used for machine translation, but it can also be used for language analysis. Retrieval Augmented Generation (RAG) has been demonstrated to improve large language model (LLM) capabilities by incorporating external reference material into the generation process. In this paper, we investigate the use of grammar books and RAG techniques to identify language features. We use Grambank for feature definition and ground truth values, and we evaluate on five typologically diverse low-resource languages. We demonstrate that this approach can effectively make use of reference material.