'Layer su Layer': Identifying and Disambiguating the Italian NPN Construction in BERT's family
arXiv:2604.03673v1 Announce Type: new Abstract: Interpretability research has highlighted the importance of evaluating Pretrained Language Models (PLMs) and in particular contextual embeddings against explicit linguistic theories to determine what linguistic information they encode. This study focuses on the Italian NPN (noun-preposition-noun) constructional family, challenging some of the theoretical and methodological assumptions underlying previous experimental designs and extending this type of research to a lesser-investigated language. Contextual vector representations are extracted from BERT and used as input to layer-wise probing classifiers, systematically evaluating information encoded across the model's internal layers. The results shed light on the extent to which constructional form and meaning are reflected in contextual embeddings, contributing empirical evidence to the dialogue between constructionist theory and neural language modelling
arXiv:2604.03673v1 Announce Type: new Abstract: Interpretability research has highlighted the importance of evaluating Pretrained Language Models (PLMs) and in particular contextual embeddings against explicit linguistic theories to determine what linguistic information they encode. This study focuses on the Italian NPN (noun-preposition-noun) constructional family, challenging some of the theoretical and methodological assumptions underlying previous experimental designs and extending this type of research to a lesser-investigated language. Contextual vector representations are extracted from BERT and used as input to layer-wise probing classifiers, systematically evaluating information encoded across the model's internal layers. The results shed light on the extent to which constructional form and meaning are reflected in contextual embeddings, contributing empirical evidence to the dialogue between constructionist theory and neural language modelling
Executive Summary
This study contributes to the ongoing dialogue between constructionist theory and neural language modeling by investigating the Italian NPN constructional family using BERT's contextual vector representations. The authors employ layer-wise probing classifiers to systematically evaluate information encoded across the model's internal layers, providing empirical evidence on the extent to which constructional form and meaning are reflected in contextual embeddings. The research challenges some theoretical and methodological assumptions underlying previous experimental designs and extends this type of research to a lesser-investigated language, highlighting the importance of evaluating PLMs against explicit linguistic theories. The findings have implications for both the development of more linguistically informed neural language models and the refinement of constructionist theory.
Key Points
- ▸ The study focuses on the Italian NPN constructional family, a lesser-investigated language in the context of neural language modeling.
- ▸ The research employs layer-wise probing classifiers to evaluate information encoded across BERT's internal layers.
- ▸ The findings contribute empirical evidence to the dialogue between constructionist theory and neural language modeling.
Merits
Contribution to the dialogue between constructionist theory and neural language modeling
The study provides empirical evidence on the extent to which constructional form and meaning are reflected in contextual embeddings, shedding light on the representation of linguistic information in PLMs.
Methodological innovation
The use of layer-wise probing classifiers to systematically evaluate information encoded across BERT's internal layers represents a novel approach to interpreting PLMs.
Extension of research to a lesser-investigated language
The study's focus on the Italian NPN constructional family highlights the importance of investigating languages beyond English in the context of neural language modeling.
Demerits
Limitation of scope
The study is limited to a single language and constructional family, which may not generalize to other languages or constructional families.
Dependence on BERT
The study's findings are dependent on the specific architecture and training data of BERT, which may not be representative of other PLMs.
Expert Commentary
The study represents a significant contribution to the field of neural language modeling and constructionist theory. The use of layer-wise probing classifiers to systematically evaluate information encoded across BERT's internal layers is a novel and innovative approach that sheds light on the representation of linguistic information in PLMs. The study's findings have implications for both the development of more linguistically informed neural language models and the refinement of constructionist theory. However, the study's limitations of scope and dependence on BERT are notable and should be considered in the interpretation of the results. Overall, the study is a valuable addition to the ongoing dialogue between constructionist theory and neural language modeling.
Recommendations
- ✓ Future studies should investigate the representation of linguistic information in other PLMs and languages to generalize the findings of this study.
- ✓ The development of more linguistically informed neural language models should be prioritized to better capture the complexities of human language.
Sources
Original: arXiv - cs.CL