ÚFAL LatinPipe: A Winning Submission for Morphosyntactic Analysis of Latin at EvaLatin 2024

Core Concepts

LatinPipe, a graph-based dependency parser, achieved top performance in the EvaLatin 2024 Dependency Parsing shared task by leveraging fine-tuned pre-trained language models, frozen pretraining, BiLSTM layers, and gold UPOS input, along with multi-treebank training and annotation harmonization.

Abstract

This paper describes the ÚFAL LatinPipe system, the winning submission to the EvaLatin 2024 Dependency Parsing shared task. LatinPipe is a graph-based dependency parser that uses a deep neural network architecture based on fine-tuned pre-trained language models (PLMs), including monolingual Latin models and multilingual models.
The key aspects of the LatinPipe system include:

Experimenting with different PLM configurations, including concatenation of multiple models
Performing frozen pretraining on the Transformer weights before fine-tuning
Adding two bidirectional LSTM layers on top of the Transformers
Leveraging the gold UPOS tags from the shared task data as additional input
Extensive multi-treebank training on seven publicly available Latin corpora
Harmonizing the annotation styles across the treebanks, especially for the PROIEL treebank
Ensembling the output probability distributions from multiple randomly initialized networks
The authors provide a detailed evaluation of the LatinPipe system on both the UD 2.13 test sets and the EvaLatin 2024 test data. They show that the various architectural choices, the multi-treebank training, and the annotation harmonization all contribute to the strong performance of the system. LatinPipe sets new state-of-the-art results for dependency parsing, UPOS tagging, and UFeats tagging on the evaluated Latin treebanks.

Stats

The LatinPipe system was trained on a total of 824K tokens from seven publicly available Latin treebanks.
The largest treebank used for training was ITTB with 391K tokens, followed by LLCT with 194K tokens and PROIEL with 178K tokens.
The smaller treebanks included UDante (31K tokens), Perseus (18K tokens), Sab (11K tokens), and Arch (1K tokens).

Quotes

"LatinPipe is a graph-based dependency parser which uses a deep neural network for scoring the graph edges."
"We provide an extensive evaluation of the approaches used in LatinPipe: a comparison of monolingual and multilingual pre-trained language models and their concatenations; initial pretraining on the frozen Transformer weights; adding two BiLSTM layers on top of the Transformers; and using the gold UPOS from the shared task data on the network input."
"The harmonized version of PROIEL resulted from the harmonization carried out by Gamba and Zeman (2023a,b), who observed persisting differences in the annotation scheme of the five Latin treebanks, annotated by different teams and in different stages of the development of UD guidelines."

Key Insights Distilled From

ÚFAL LatinPipe at EvaLatin 2024

by Mila... at arxiv.org 04-10-2024

https://arxiv.org/pdf/2404.05839.pdf

Deeper Inquiries

How can the LatinPipe system be extended to handle other morphologically rich languages beyond Latin

To extend the LatinPipe system to handle other morphologically rich languages beyond Latin, several adaptations can be made:

Data Collection: Gather annotated treebanks in the target languages to train the system effectively. These treebanks should cover a diverse range of linguistic phenomena to ensure robustness.
Pre-trained Language Models: Fine-tune pre-trained language models on the specific language data to capture language-specific features and nuances.
Architecture Adjustments: Modify the neural network architecture to accommodate the linguistic characteristics of the new languages. This may involve adjusting the attention mechanisms, adding or removing layers, or incorporating language-specific modules.
Annotation Harmonization: If working with multiple treebanks, ensure that the annotations are harmonized to maintain consistency across datasets.
Ensemble Learning: Implement ensemble methods to combine predictions from multiple models for improved accuracy and generalization.

What are the potential challenges in applying the multi-treebank training approach to languages with fewer available treebank resources

Applying the multi-treebank training approach to languages with fewer available treebank resources may pose several challenges:

Data Scarcity: Limited availability of annotated treebanks may hinder the effectiveness of multi-treebank training, as the model may not capture the full linguistic diversity of the language.
Annotation Variability: Inconsistencies in annotation styles across different treebanks can lead to difficulties in harmonizing the data, potentially affecting the model's performance.
Domain Specificity: Treebanks may focus on specific domains or genres, leading to biases in the model if not appropriately balanced during training.
Resource Intensiveness: Training on multiple treebanks requires significant computational resources and time, which may be prohibitive for languages with fewer resources.
Generalization: The model's ability to generalize to unseen data may be compromised if the training data does not adequately represent the language's linguistic diversity.

What insights can be drawn from the LatinPipe methodology to inform the development of more general-purpose dependency parsing and morphological analysis systems

Insights from the LatinPipe methodology can inform the development of more general-purpose dependency parsing and morphological analysis systems in the following ways:

Multi-Task Learning: LatinPipe's approach of jointly learning dependency parsing and morphological analysis can be applied to other languages to enhance model performance and efficiency.
Data Harmonization: The emphasis on harmonizing annotations across treebanks can improve model robustness and generalization, especially in multilingual settings.
Ensemble Techniques: Leveraging ensemble methods, as demonstrated in LatinPipe, can boost model accuracy and reliability by combining predictions from multiple models.
Architecture Enhancements: Techniques such as adding bidirectional LSTM layers and incorporating gold UPOS tags can enhance the model's ability to capture contextual information and improve overall performance.
Transfer Learning: LatinPipe's methodology showcases the effectiveness of fine-tuning pre-trained language models for specific tasks, which can be beneficial in developing systems for various languages with limited resources.