Examining Government Relationships in Language Models

Language is complex. It consists of various structures and relationships that help us understand how words work together. One of these relationships is called "Government," where certain words, particularly verbs, affect how other words in a sentence behave. This paper looks into how certain language models, specifically transformer models like BERT, represent these government relationships in sentences.

#Understanding Government in Language

Government refers to how a governor, usually a verb, controls its Dependents, which can be nouns or phrases. For example, in the sentence "I listened to many songs on a trip through Europe," the verb "listened" governs the phrase "to many songs," which is necessary for the sentence to make sense. However, the phrase "on a trip" is optional; it provides extra information but is not required.

In essence, government helps us understand which words rely on others to form correct sentences. A verb can have different kinds of dependents, and these dependents can vary in their necessity. Understanding how government works allows us to see how language is structured.

#The Need for Government Research

Researching government relations is crucial because it helps improve linguistic resources. Language learners benefit from understanding these relationships as it aids them in mastering a language. By tracking which constructions a learner knows, teachers can better plan lessons and resources.

However, there's a lack of data and resources available for studying grammatical constructions, especially government. This study aims to fill that gap by looking into how transformer models encode government relations.

#The Role of Transformer Models

Transformer models, particularly BERT, have shown excellent performance in processing natural language. They learn from data and can represent linguistic knowledge in their inner workings. This study seeks to explore how BERT encodes government relations and whether this information can be used to build practical resources for language learning.

#Research Questions

This study focuses on two main questions:

1. Does BERT encode knowledge about government, and where is this information represented?
2. Can this knowledge be extracted from the model to build resources for language learning?

#Methodology

To explore the first question, we used Probing Classifiers, which are tools designed to examine the internal workings of models like BERT. We used data from two languages, Finnish and Russian, to see how well the model could identify government relations.

We conducted our experiments in several steps:

1. Created a dataset called the Government Bank, which contains rules for how verbs govern their dependents in both Finnish and Russian.
2. Trained probing classifiers to check if they could accurately predict government relationships based on this data.

#Building the Government Bank

The Government Bank is a comprehensive dataset detailing the government relationships for various verbs in Finnish and Russian. It includes rules about which noun forms are governed by specific verbs.

For Finnish, we collected information for 765 verbs, while for Russian, we gathered data for 1,976 verbs. The dataset serves as a critical resource for future studies on government relations.

#Training Probing Classifiers

We built probing classifiers using information from BERT’s attention heads, which contain the knowledge about government relations. By feeding these classifiers data, we aimed to see how accurately they could identify governing verbs and their dependents.

The classifiers were tested on both Finnish and Russian, and the accuracy of their predictions was evaluated.

#Results and Discussion

#General Performance of Classifiers

The results showed that the probing classifiers performed quite well, with high accuracy in identifying government relations. They were able to distinguish between positive instances (correct relations) and negative instances (incorrect relations) effectively. This suggests that BERT does encode substantial information about government.

#Probing Selectivity

We also checked whether the classifiers focused on the adjacency of the governor and its dependent. This means we wanted to know if they were merely identifying words that were close to one another in sentences. The results indicated that the classifiers were indeed capable of identifying government relations even when the dependents were far from their governors.

#Importance of Attention Heads

Through experiments, we found that certain attention heads in BERT were more crucial than others. Some heads contained most of the necessary information to make accurate predictions about government relations, while others still contributed but were less essential. This finding indicates that government information is not evenly spread out across all heads.

#Error Analysis

We examined instances where the classifiers made mistakes. Some errors were due to the underlying data, where the parsing of sentences was not accurate. For examples, sometimes, a dependent was incorrectly labeled, causing confusion. Most of these errors were relatively rare but highlighted the need for better data quality in future studies.

#Discovering New Government Patterns

One of our main goals was to determine if the classifiers could discover new government patterns that were not included in the training data. We tested the classifiers on unseen data, and the results showed that they could indeed identify new relationships. This indicates that the probing classifiers can serve as a valuable tool for expanding linguistic resources.

#Conclusion

This study demonstrates how transformer models like BERT can be used to encode knowledge about government relations in language. The findings suggest that these models are capable of revealing important linguistic structures and can help build resources for language learning.

The release of the Government Bank contributes significantly to the field, offering researchers a tool to study government and grammatical relationships in detail.

#Future Work

Future efforts will focus on expanding the Government Bank to include more languages and exploring government relations in other parts of speech beyond verbs. Additionally, further research into different types of transformer models and probing techniques will enhance our understanding of language processing in models like BERT.

#Acknowledgments

The authors would like to thank the linguistic community for their support and collaboration in developing the Government Bank. The work carried out in this study has laid the foundation for future research in understanding government relations in natural language.