A two-layer LSTM language model trained on 250M morphemes of Hindi data can capture the relatively complex interaction between case and agreement in Hindi-Urdu, and it is shown that this model encodes case-marking linearly, implementing a geometrically intuitive and inter-pretable syntactic processing mechanism.
A large language model (LLM) is an AI model (typically a neural network) trained on a vast amount of text for natural language processing tasks, especially language generation. LLMs can typically generate, summarize, translate, and analyze text in many contexts. They are the basis for many modern chatbots, such as Chat...
Seyed Alireza Mousavian Anaraki, Randall Spain, Stefan Hegselmann· 0 citations
The results show that models’ verb representations degrade more when the authors perturb word order, which removes syntactic information, than when they perturb co-occurrence among content words, which removes semantic information.
Xiao-Meng Zhu, R. Thomas McCoy, Robert Frank· Open Mind· 0 citations
Semantic representation-based multilingual natural language generation (NLG) is still a challenging problem in computational linguistics. In this paper, we present universal semantic representation (USR), a new semantic representation that provides semantics at multiple levels, including the speaker's intention and dis...
A layer-wise analysis indicates that surface-level features such as temporality and negation are captured more reliably than deeper semantic phenomena like quantification in large language models, highlighting the limited capacity of current LLMs to generate fully formal meaning representations.
Rémi De Vergnette, Maxime Amblard· International Conference on...· 0 citations
It is shown that useful explanations can extend beyond the model a verbalizer was trained to describe and show that pretrained verbalizers recover words that models have learned to conceal through fine-tuning, without additional verbalizer training.
Federico Torrielli, Gianluca Barmina, Andrea Blasi Núñez et al.· 0 citations
We present edition 2.0 of the PARSEME multilingual corpus annotated for multiword expressions (MWEs), resulting from efforts of the PARSEME community towards universality-driven modeling of idiomaticity. With respect to previous editions, we extend the annotation scope to all syntactic MWE categories: verbal, nominal,...
Agata Savary, Manon Scholivet, C. Ramisch et al.· International Conference on...· 2 citations· ⚡1
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