Papers by Paul Lerner
Unlike “Likely”, “Unlike” is Unlikely: BPE-based Segmentation hurts Morphological Derivations in LLMs (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) use subword vocabularies to process and generate text. |
| Approach: | They find that Large Language Models (LLMs) perform poorly at handling some types of affixations because subwords are marked as initial- or intra-word . |
| Outcome: | The largest models trained on enough data can mitigate this tendency because initial- and intra-word embeddings are aligned; in-context learning also helps when all examples are selected in a consistent way; but only morphological segmentation can achieve a near-perfect accuracy. |
Bazinga! A Dataset for Multi-Party Dialogues Structuring (2022.lrec-1)
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Paul Lerner, Juliette Bergoënd, Camille Guinaudeau, Hervé Bredin, Benjamin Maurice, Sharleyne Lefevre, Martin Bouteiller, Aman Berhe, Léo Galmant, Ruiqing Yin, Claude Barras
| Challenge: | a dataset of 16 TV and movie series is filled with challenging multi-party dialogues. |
| Approach: | They propose a dataset built around 16 TV and movie series with challenging multi-party dialogues. |
| Outcome: | The proposed dataset is a step towards better multi-party dialogue structuring and understanding. |
Towards the Machine Translation of Scientific Neologisms (2025.coling-main)
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| Challenge: | Scientific research continually discovers and invents new concepts, which are then referred to by new terms, neologisms, or nenonyms. |
| Approach: | They propose to leverage term definitions to translate neologisms with Large Language Models . they find that LLMs generate terms from co-hyponyms and terms sharing the same derivation paradigm . |
| Outcome: | The proposed model can generate terms from co-hyponyms and terms sharing the same derivation paradigm. |