Multilingual Dialogue Generation and Localization with Dialogue Act Scripting

• URL: http://arxiv.org/abs/2509.22086v1
• Date: Fri, 26 Sep 2025 09:09:08 GMT
• Title: Multilingual Dialogue Generation and Localization with Dialogue Act Scripting
• Authors: Justin Vasselli, Eunike Andriani Kardinata, Yusuke Sakai, Taro Watanabe,
• Abstract summary: Dialogue Act Script (DAS) is a structured framework for encoding, localizing, and generating multilingual dialogues from abstract intent representations.<n>DAS supports flexible localization across languages, mitigating translationese and enabling more fluent, naturalistic conversations.<n>Human evaluations across Italian, German, and Chinese show that DAS-generated dialogues consistently outperform those produced by both machine and human translators.
• Score: 31.83010264062348
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• RealTalk-CN: A Realistic Chinese Speech-Text Dialogue Benchmark With Cross-Modal Interaction Analysis [15.473595594666751]
  We introduce RealTalk-CN, the first Chinese multi-turn, multi-domain speech-text dual-modal TOD dataset.<n>RealTalk-CN captures diverse dialogue scenarios with annotated spontaneous speech disfluencies.<n>We propose a novel cross-modal chat task that authentically simulates real-world user interactions.
  arXiv  Detail & Related papers  (2025-08-06T13:12:57Z)
• Aligning Spoken Dialogue Models from User Interactions [55.192134724622235]
  We propose a novel preference alignment framework to improve spoken dialogue models on realtime conversations from user interactions.<n>We create a dataset of more than 150,000 preference pairs from raw multi-turn speech conversations annotated with AI feedback.<n>Our findings shed light on the importance of a well-calibrated balance among various dynamics, crucial for natural real-time speech dialogue systems.
  arXiv  Detail & Related papers  (2025-06-26T16:45:20Z)
• J-CHAT: Japanese Large-scale Spoken Dialogue Corpus for Spoken Dialogue Language Modeling [43.87842102048749]
  Spoken dialogue plays a crucial role in human-AI interactions, necessitating dialogue-oriented spoken language models (SLMs) To ensure hiqh-quality speech generation, the data must be spontaneous like in-wild data and must be acoustically clean with noise removed. This study addresses this gap by constructing and releasing a large-scale spoken dialogue corpus, named Japanese Corpus for Human-AI Talks (J-CHAT) This paper presents a language-independent method for corpus construction and describes experiments on dialogue generation using SLMs trained on J-CHAT.
  arXiv  Detail & Related papers  (2024-07-22T17:46:50Z)
• Channel-aware Decoupling Network for Multi-turn Dialogue Comprehension [81.47133615169203]
  We propose compositional learning for holistic interaction across utterances beyond the sequential contextualization from PrLMs. We employ domain-adaptive training strategies to help the model adapt to the dialogue domains. Experimental results show that our method substantially boosts the strong PrLM baselines in four public benchmark datasets.
  arXiv  Detail & Related papers  (2023-01-10T13:18:25Z)
• STRUDEL: Structured Dialogue Summarization for Dialogue Comprehension [42.57581945778631]
  Abstractive dialogue summarization has long been viewed as an important standalone task in natural language processing. We propose a novel type of dialogue summarization task - STRUctured DiaLoguE Summarization. We show that our STRUDEL dialogue comprehension model can significantly improve the dialogue comprehension performance of transformer encoder language models.
  arXiv  Detail & Related papers  (2022-12-24T04:39:54Z)
• A Benchmark for Understanding and Generating Dialogue between Characters in Stories [75.29466820496913]
  We present the first study to explore whether machines can understand and generate dialogue in stories. We propose two new tasks including Masked Dialogue Generation and Dialogue Speaker Recognition. We show the difficulty of the proposed tasks by testing existing models with automatic and manual evaluation on DialStory.
  arXiv  Detail & Related papers  (2022-09-18T10:19:04Z)
• Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning [27.92734269206744]
  InstructDial is an instruction tuning framework for dialogue. It consists of a repository of 48 diverse dialogue tasks in a unified text-to-text format created from 59 openly available dialogue datasets. Our analysis reveals that InstructDial enables good zero-shot performance on unseen datasets and tasks such as dialogue evaluation and intent detection, and even better performance in a few-shot setting.
  arXiv  Detail & Related papers  (2022-05-25T11:37:06Z)
• Cross-Lingual Dialogue Dataset Creation via Outline-Based Generation [70.81596088969378]
  Cross-lingual Outline-based Dialogue dataset (termed COD) enables natural language understanding. COD enables dialogue state tracking, and end-to-end dialogue modelling and evaluation in 4 diverse languages.
  arXiv  Detail & Related papers  (2022-01-31T18:11:21Z)
• Graph Based Network with Contextualized Representations of Turns in Dialogue [0.0]
  Dialogue-based relation extraction (RE) aims to extract relation(s) between two arguments that appear in a dialogue. We propose the TUrn COntext awaRE Graph Convolutional Network (TUCORE-GCN) modeled by paying attention to the way people understand dialogues.
  arXiv  Detail & Related papers  (2021-09-09T03:09:08Z)
• Advances in Multi-turn Dialogue Comprehension: A Survey [51.215629336320305]
  We review the previous methods from the perspective of dialogue modeling. We discuss three typical patterns of dialogue modeling that are widely-used in dialogue comprehension tasks.
  arXiv  Detail & Related papers  (2021-03-04T15:50:17Z)
• TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue [113.45485470103762]
  In this work, we unify nine human-human and multi-turn task-oriented dialogue datasets for language modeling. To better model dialogue behavior during pre-training, we incorporate user and system tokens into the masked language modeling.
  arXiv  Detail & Related papers  (2020-04-15T04:09:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.