Shuoyang Ding

How Do Source-side Monolingual Word Embeddings Impact Neural Machine Translation?

2018 · arXiv (Cornell University)

Using pre-trained word embeddings as input layer is a common practice in many natural language processing (NLP) tasks, but it is largely neglected for neural machine translation (NMT). In this paper, we conducted a systematic …

Evaluating Saliency Methods for Neural Language Models

2021

Saliency methods are widely used to interpret neural network predictions, but different variants of saliency methods often disagree even on the interpretations of the same prediction made by the same model. In these cases, how …

EMMeTT: Efficient Multimodal Machine Translation Training

2025

A rising interest in the modality extension of foundation language models warrants discussion on the most effective, and efficient, multimodal training approach. This work focuses on neural machine translation (NMT) and proposes a joint multimodal …

Saliency-driven Word Alignment Interpretation for Neural Machine Translation

2019

Despite their original goal to jointly learn to align and translate, Neural Machine Translation (NMT) models, especially Transformer, are often perceived as not learning interpretable word alignments. In this paper, we show that NMT models …