# Understanding Neural Machine Translation by Simplification: The Case of Encoder-free Models

## TL;DR

This paper investigates simplified encoder-free neural machine translation models, revealing that attention mechanisms serve as effective feature extractors and that non-contextualized embeddings impact performance differently across language pairs.

## Contribution

It introduces and analyzes encoder-free NMT models, demonstrating their capabilities and limitations compared to traditional architectures.

## Key findings

- Attention acts as a strong feature extractor in encoder-free models.
- Word embeddings in encoder-free models are competitive with traditional models.
- Non-contextualized source representations cause significant performance drops.

## Abstract

In this paper, we try to understand neural machine translation (NMT) via simplifying NMT architectures and training encoder-free NMT models. In an encoder-free model, the sums of word embeddings and positional embeddings represent the source. The decoder is a standard Transformer or recurrent neural network that directly attends to embeddings via attention mechanisms. Experimental results show (1) that the attention mechanism in encoder-free models acts as a strong feature extractor, (2) that the word embeddings in encoder-free models are competitive to those in conventional models, (3) that non-contextualized source representations lead to a big performance drop, and (4) that encoder-free models have different effects on alignment quality for German-English and Chinese-English.