Large Language Models (LLMs) have demonstrated promising potential in providing empathetic support during interactions. However, their responses often become verbose or overly formulaic, failing to adequately address the diverse emotional support needs of real-world scenarios. To tackle this challenge, we propose an innovative strategy-enhanced role-playing framework, designed to simulate authentic emotional support conversations. Specifically, our approach unfolds in two steps: (1) Strategy-Enhanced Role-Playing Interactions, which involve three pivotal roles—Seeker, Strategy Counselor, and Supporter—engaging in diverse scenarios to emulate real-world interactions and promote a broader range of dialogues; and (2) Emotional Support Agent Training, achieved through fine-tuning LLMs using our specially constructed dataset. Within this framework, we develop the ServeForEmo dataset, comprising an extensive collection of 3.7K+ multi-turn dialogues and 62.8K+ utterances. We further present SweetieChat, an emotional support agent capable of handling diverse open-domain scenarios. Extensive experiments and human evaluations confirm the framework’s effectiveness in enhancing emotional support, highlighting its unique ability to provide more nuanced and tailored assistance.

Document Image Translation (DIT) aims to translate documents in images from one language to another. It requires visual layouts and textual contents understanding, as well as document coherence capturing. However, current methods often rely on the quality of OCR output, which, particularly in complex-layout scenarios, frequently loses the crucial document coherence, leading to chaotic text. To overcome this problem, we introduce a novel end-to-end network, named Zoom-out DIT (ZoomDIT), inspired by human translation procedures. It jointly accomplishes the multi-level tasks including word positioning, sentence recognition & translation, and document organization, based on a fine-to-coarse zoom-out framework, to progressively realize “chaotic words to coherent document” and improve translation. We further contribute a new large-scale DIT dataset with multi-level fine-grained labels. Extensive experiments on public and our new dataset demonstrate significant improvements in translation quality towards complex-layout document images, offering a robust solution for reorganizing the chaotic OCR outputs to a coherent document translation.

Text image machine translation (TIMT) aims at translating source language texts in images into another target language, which has been proven successful by bridging text image recognition encoder and text translation decoder. However, it is still an open question of how to incorporate fine-grained knowledge supervision to make it consistent between recognition and translation modules. In this paper, we propose a novel TIMT method named as BabyNet, which is optimized with hierarchical parental supervision to improve translation performance. Inspired by genetic recombination and variation in the field of genetics, the proposed BabyNet is inherited from the recognition and translation parent models with a variation module of which parameters can be updated when training on the TIMT task. Meanwhile, hierarchical and multi-granularity supervision from parent models is introduced to bridge the gap between inherited modules in BabyNet. Extensive experiments on both synthetic and real-world TIMT tests show that our proposed method significantly outperforms existing methods. Further analyses of various parent model combinations show the good generalization of our method.

Text image machine translation (TIMT) is a task that translates source texts embedded in the image to target translations. The existing TIMT task mainly focuses on text-line-level images. In this paper, we extend the current TIMT task and propose a novel task, **D**ocument **I**mage **M**achine **T**ranslation to **Markdown** (**DIMT2Markdown**), which aims to translate a source document image with long context and complex layout structure to markdown-formatted target translation.We also introduce a novel framework, **D**ocument **I**mage **M**achine **T**ranslation with **D**ynamic multi-pre-trained models **A**ssembling (**DIMTDA**).A dynamic model assembler is used to integrate multiple pre-trained models to enhance the model’s understanding of layout and translation capabilities.Moreover, we build a novel large-scale **Do**cument image machine **T**ranslation dataset of **A**rXiv articles in markdown format (**DoTA**), containing 126K image-translation pairs.Extensive experiments demonstrate the feasibility of end-to-end translation of rich-text document images and the effectiveness of DIMTDA.

Text image machine translation (TIMT) which translates source language text images into target language texts has attracted intensive attention in recent years. Although the end-to-end TIMT model directly generates target translation from encoded text image features with an efficient architecture, it lacks the recognized source language information resulting in a decrease in translation performance. In this paper, we propose a novel Cross-modal Cross-lingual Interactive Model (CCIM) to incorporate source language information by synchronously generating source language and target language results through an interactive attention mechanism between two language decoders. Extensive experimental results have shown the interactive decoder significantly outperforms end-to-end TIMT models and has faster decoding speed with smaller model size than cascade models.

Document image translation (DIT) aims to translate text embedded in images from one language to another. It is a challenging task that needs to understand visual layout with text semantics simultaneously. However, existing methods struggle to capture the crucial visual layout in real-world complex document images. In this work, we make the first attempt to incorporate layout knowledge into DIT in an end-to-end way. Specifically, we propose a novel Layout-aware end-to-end Document Image Translation (LayoutDIT) with multi-step conductive decoder. A layout-aware encoder is first introduced to model visual layout relations with raw OCR results. Then a novel multi-step conductive decoder is unified with hidden states conduction across three step-decoders to achieve the document translation step by step. Benefiting from the layout-aware end-to-end joint training, our LayoutDIT outperforms state-of-the-art methods with better parameter efficiency. Besides, we create a new multi-domain document image translation dataset to validate the model’s generalization. Extensive experiments show that LayoutDIT has a good generalization in diverse and complex layout scenes.