# Embedding-based system for the Text part of CALL v3 shared task

## TL;DR

This paper introduces a text embedding-based scoring system for CALL v3 that achieves top results without relying on reference grammar files, demonstrating the effectiveness of embedding models like NNLM and BERT.

## Contribution

The paper presents a novel embedding-based scoring approach that outperforms traditional methods relying on reference grammar files in CALL v3 shared task.

## Key findings

- Achieved top performance on CALL v3 text subset
- Comparable or better results than grammar-based approaches
- Effective data preparation process for training embeddings

## Abstract

This paper presents a scoring system that has shown the top result on the text subset of CALL v3 shared task. The presented system is based on text embeddings, namely NNLM~\cite{nnlm} and BERT~\cite{Bert}. The distinguishing feature of the given approach is that it does not rely on the reference grammar file for scoring. The model is compared against approaches that use the grammar file and proves the possibility to achieve similar and even higher results without a predefined set of correct answers. The paper describes the model itself and the data preparation process that played a crucial role in the model training.