Papers by Adam Wiemerslage
Improving Low-Resource Morphological Inflection via Self-Supervised Objectives (2025.acl-long)
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| Challenge: | Rapid progress in natural language processing (NLP) has largely been driven by training transformer models on massive amounts of unlabeled data, but such large datasets are scarce for many of the world's languages. |
| Approach: | They propose to train encoder-decoder transformers for 19 languages and 13 auxiliary objectives on massive amounts of unlabeled data. |
| Outcome: | The proposed tasks outperform standard CMLM in character-level tasks when available data is limited. |
Quantifying the Hyperparameter Sensitivity of Neural Networks for Character-level Sequence-to-Sequence Tasks (2024.eacl-long)
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| Challenge: | Neural networks are notoriously hard to interpret and slightly mysterious to researchers and practitioners alike. |
| Approach: | They formalize hyperparameter sensitivity using two metrics: similarity-based sensitivity and performance-based-sensitivity. |
| Outcome: | The transformer is more sensitive to hyperparameters according to both metrics, but not batch size . large models, multilinguality of NLP models and tasks make hyperparametric tuning more expensive . |
Model-Based Ranking of Source Languages for Zero-Shot Cross-Lingual Transfer (2025.emnlp-main)
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| Challenge: | NN-Rank is an algorithm for ranking source languages for cross-lingual transfer . it leverages hidden representations from multilingual models and unlabeled target-language data . |
| Approach: | They propose an algorithm for ranking source languages for cross-lingual transfer which leverages hidden representations from multilingual models and unlabeled target-language data. |
| Outcome: | The proposed algorithm outperforms state-of-the-art models on in-domain data and shows that it can achieve 92.8% of the NDCG achieved using all available target data. |
1,729 vs. 1729: The Effect of Scripts and Formats on LLM Numeracy (2026.findings-acl)
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| Challenge: | Large language models (LLMs) have impressive proficiency in basic arithmetic, but little attention has been given to how they perform when numerical expressions deviate from the prevailing conventions present in their training corpora. |
| Approach: | They investigate numerical reasoning across a wide range of numeral scripts and formats . they show that LLM accuracy drops substantially when numerical inputs are rendered in underrepresented scripts or formats despite the underlying mathematical reasoning being identical . |
| Outcome: | The proposed methods can narrow the gap between LLMs and human models when they deviate from prevailing numerical conventions. |
An Investigation of Noise in Morphological Inflection (2023.findings-acl)
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| Challenge: | Neural morphological inflection systems can be used for languages with very little supervised data, but are often less likely to have clean, goldstandard data. |
| Approach: | They propose an error taxonomy and annotation pipeline for inflection training data and propose a character-level masked language modeling (CMLM) pretraining objective. |
| Outcome: | The proposed pipeline is based on error taxonomy and annotation pipelines for unsupervised morphological paradigm completion. |
A Comprehensive Comparison of Neural Networks as Cognitive Models of Inflection (2022.emnlp-main)
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| Challenge: | Neural networks are at the center of a debate about human behavior in inflectional morphology. |
| Approach: | They measure correlation between human judgments and neural network probabilities for unknown word inflections. |
| Outcome: | The proposed model for morphological inflections correlates best with human wug ratings, but not with humans. |
Morphological Processing of Low-Resource Languages: Where We Are and What’s Next (2022.findings-acl)
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Adam Wiemerslage, Miikka Silfverberg, Changbing Yang, Arya McCarthy, Garrett Nicolai, Eliana Colunga, Katharina Kann
| Challenge: | Existing models for morphological processing are not suitable for low-resource languages, but they are still lacking in the field of computational morphology. |
| Approach: | They propose to bridge two unsupervised models to understand a language’s morphology from raw text alone and propose to use them to improve their models. |
| Outcome: | The proposed models perform reasonably, but there is room for improvement. |
Getting The Most Out of Your Training Data: Exploring Unsupervised Tasks for Morphological Inflection (2024.emnlp-main)
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| Challenge: | Pre-trained transformers have been shown to be effective in many natural language tasks, but are under-explored for character-level sequence to sequence tasks. |
| Approach: | They propose to use pre-trained transformers for character-level morphological inflection in several languages to train models for unsupervised tasks. |
| Outcome: | The proposed model outperforms the best two shared tasks on morphological inflection and graphemeto-phoneme conversion benchmarks. |