# Multilingual Dialogue Generation and Localization with Dialogue Act Scripting

arXiv: https://arxiv.org/abs/2509.22086 · HTML: https://mlsanity.com/paper/2509.22086/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 26 Sep 2025.

## Builds on (most cited first, 6 of 6)

- [DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset](https://mlsanity.com/paper/1710.03957/) (2017)
- [GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction](https://mlsanity.com/paper/2310.03668/) (2023)
- [Translation Artifacts in Cross-lingual Transfer Learning](https://mlsanity.com/paper/2004.04721/) (2020)
- [CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors](https://mlsanity.com/paper/2305.05711/) (2023)
- [XPersona: Evaluating Multilingual Personalized Chatbot](https://mlsanity.com/paper/2003.07568/) (2020)
- [Cross-Lingual Dialogue Dataset Creation via Outline-Based Generation](https://mlsanity.com/paper/2201.13405/) (2022)

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

- [EuroAlpaca: Task-Preserving Localisation of Instruction Data for European Languages](https://mlsanity.com/paper/2609.05043/) (2026)
- [CultureMINE: Datasets and Methods for Improving the Cultural Capabilities of NLP Systems](https://mlsanity.com/paper/2609.22494/) (2026)
- [PDEU-Bench: Benchmarking the Personalized Planning Lifecycle of Tool-Calling LLM Agents](https://mlsanity.com/paper/2609.34930/) (2026)
- [AMU:Admission and Memory Update for Personalized Conversations---Structured Memory with SLM Guided Control](https://mlsanity.com/paper/2609.36976/) (2026)

## Abstract (arXiv)

Non-English dialogue datasets are scarce, and models are often trained or evaluated on translations of English-language dialogues, an approach which can introduce artifacts that reduce their naturalness and cultural appropriateness. This work proposes Dialogue Act Script (DAS), a structured framework for encoding, localizing, and generating multilingual dialogues from abstract intent representations. Rather than translating dialogue utterances directly, DAS enables the generation of new dialogues in the target language that are culturally and contextually appropriate. By using structured dialogue act representations, DAS supports flexible localization across languages, mitigating translationese and enabling more fluent, naturalistic conversations. Human evaluations across Italian, German, and Chinese show that DAS-generated dialogues consistently outperform those produced by both machine and human translators on measures of cultural relevance, coherence, and situational appropriateness.