# Domain Adaptation in Neural Machine Translation using a Qualia-Enriched FrameNet

arXiv: https://arxiv.org/abs/2202.10287 · HTML: https://mlsanity.com/paper/2202.10287/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 49 references that are in the map.
- First posted on arXiv 21 Feb 2022.

## Builds on (most cited first, 8 of 8)

- [Six Challenges for Neural Machine Translation](https://mlsanity.com/paper/1706.03872/) (2017)
- [OpenNMT: Open-source Toolkit for Neural Machine Translation](https://mlsanity.com/paper/1709.03815/) (2017)
- [A Survey of Domain Adaptation for Neural Machine Translation](https://mlsanity.com/paper/1806.00258/) (2018)
- [Incorporating Discrete Translation Lexicons into Neural Machine Translation](https://mlsanity.com/paper/1606.02006/) (2016)
- [Frame-Semantic Parsing with Softmax-Margin Segmental RNNs and a Syntactic Scaffold](https://mlsanity.com/paper/1706.09528/) (2017)
- [Bridging Neural Machine Translation and Bilingual Dictionaries](https://mlsanity.com/paper/1610.07272/) (2016)
- [UDPipe at EvaLatin 2020: Contextualized Embeddings and Treebank Embeddings](https://mlsanity.com/paper/2006.03687/) (2020)
- [Translating Terminological Expressions in Knowledge Bases with Neural Machine Translation](https://mlsanity.com/paper/1709.02184/) (2017)

## Built on by (most cited first, 2 of 2)

- [Argument Mining in Data Scarce Settings: Cross-lingual Transfer and Few-shot Techniques](https://mlsanity.com/paper/2407.03748/) (2024)
- [Evaluating the Impact of LLM-Assisted Annotation in a Perspectivized Setting: the Case of FrameNet Annotation](https://mlsanity.com/paper/2510.25904/) (2025)

## Abstract (arXiv)

In this paper we present Scylla, a methodology for domain adaptation of Neural Machine Translation (NMT) systems that make use of a multilingual FrameNet enriched with qualia relations as an external knowledge base. Domain adaptation techniques used in NMT usually require fine-tuning and in-domain training data, which may pose difficulties for those working with lesser-resourced languages and may also lead to performance decay of the NMT system for out-of-domain sentences. Scylla does not require fine-tuning of the NMT model, avoiding the risk of model over-fitting and consequent decrease in performance for out-of-domain translations. Two versions of Scylla are presented: one using the source sentence as input, and another one using the target sentence. We evaluate Scylla in comparison to a state-of-the-art commercial NMT system in an experiment in which 50 sentences from the Sports domain are translated from Brazilian Portuguese to English. The two versions of Scylla significantly outperform the baseline commercial system in HTER.