Recent advancements in Large Vision-Language Models are accelerating the development of Graphical User Interface (GUI) agents that utilize human-like vision perception capabilities to enhance productivity on digital devices. Compared to approaches predicated on GUI metadata, which are platform-dependent and vulnerable to implementation variations, vision-based approaches offer broader applicability.In this vision-based paradigm, the GUI instruction grounding, which maps user instruction to the location of corresponding element on the given screenshot, remains a critical challenge, particularly due to limited public training dataset and resource-intensive manual instruction data annotation.In this paper, we delve into unexplored challenges in this task including element-to-screen ratio, unbalanced element type, and implicit instruction. To address these challenges, we introduce a large-scale data synthesis pipeline UI-E2I-Synth for generating varying complex instruction datasets using GPT-4o instead of human annotators. Furthermore, we propose a new GUI instruction grounding benchmark UI-I2E-Bench, which is designed to address the limitations of existing benchmarks by incorporating diverse annotation aspects.Our model, trained on the synthesized data, achieves superior performance in GUI instruction grounding, demonstrating the advancements of proposed data synthesis pipeline.The proposed benchmark, accompanied by extensive analyses, provides practical insights for future research in this domain. We will release our dataset and benchmark to facilitate further development of GUI instruction grounding community.

This paper tackles the challenges presented by Automatic Speech Recognition (ASR) errors in voice-based dialog systems, specifically, their adverse impact on Entity Resolution (ER) as a downstream task. Navigating the equilibrium between accuracy and online retrieval’s speed requirement proves challenging, particularly when limited data links the failed mentions to resolved entities. In this paper, we propose a entity reference expansion system, injecting pairs of failed mentions and resolved entity names into the knowledge graph, enhancing its awareness of unresolved mentions. To address data scarcity, we introduce a synthetic data generation approach aligned with noise patterns. This, combined with an ASR-Error-Aware Loss function, facilitates the training of a RoBERTa model, which filters failed mentions and extracts entity pairs for knowledge graph expansion. These designs confront obstacles related to ASR noise, data limitations, and online entity retrieval.