# Controlling Formality in Low-Resource NMT with Domain Adaptation and Re-Ranking: SLT-CDT-UoS at IWSLT2022

arXiv: https://arxiv.org/abs/2205.05990 · HTML: https://mlsanity.com/paper/2205.05990/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 12 May 2022.

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

- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)
- [WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia](https://mlsanity.com/paper/1907.05791/) (2019)
- [Getting Gender Right in Neural Machine Translation](https://mlsanity.com/paper/1909.05088/) (2019)
- [Controlling the Output Length of Neural Machine Translation](https://mlsanity.com/paper/1910.10408/) (2019)
- [CoCoA-MT: A Dataset and Benchmark for Contrastive Controlled MT with Application to Formality](https://mlsanity.com/paper/2205.04022/) (2022)
- [Rethinking Text Attribute Transfer: A Lexical Analysis](https://mlsanity.com/paper/1909.12335/) (2019)

## Built on by (most cited first, 4 of 4)

- [Reference-less Analysis of Context Specificity in Translation with Personalised Language Models](https://mlsanity.com/paper/2303.16618/) (2023)
- [We Need to Talk About Classification Evaluation Metrics in NLP](https://mlsanity.com/paper/2401.03831/) (2024)
- [MTCue: Learning Zero-Shot Control of Extra-Textual Attributes by Leveraging Unstructured Context in Neural Machine Translation](https://mlsanity.com/paper/2305.15904/) (2023)
- [Machine Translation to Control Formality Features in the Target Language](https://mlsanity.com/paper/2311.13475/) (2023)

## Abstract (arXiv)

This paper describes the SLT-CDT-UoS group's submission to the first Special Task on Formality Control for Spoken Language Translation, part of the IWSLT 2022 Evaluation Campaign. Our efforts were split between two fronts: data engineering and altering the objective function for best hypothesis selection. We used language-independent methods to extract formal and informal sentence pairs from the provided corpora; using English as a pivot language, we propagated formality annotations to languages treated as zero-shot in the task; we also further improved formality controlling with a hypothesis re-ranking approach. On the test sets for English-to-German and English-to-Spanish, we achieved an average accuracy of .935 within the constrained setting and .995 within unconstrained setting. In a zero-shot setting for English-to-Russian and English-to-Italian, we scored average accuracy of .590 for constrained setting and .659 for unconstrained.