Despite the latest improvements on machine translation, professional translators still must review and post-edit the automatic output to ensure high-quality translations. The research on automating this process lacks an interactive post-editing environment implemented for this purpose; therefore, current approaches do not consider the human interactions that occur in real post-editing scenarios. To address this issue, we present OpenTIPE, a flexible and extensible framework that aims at supporting research on interactive post-editing. Specifically, the interactive environment of OpenTIPE allows researchers to explore human-centered approaches for the post-editing task. We release the OpenTIPE source code and showcase its main functionalities with a demonstration video and an online live demo.

In this paper, we present a system for augmenting virtual AI characters with long-term memory, enabling them to remember facts about themselves, their world, and past experiences. We propose a memory-creation pipeline that converts raw text into condensed memories and a memory-retrieval system that utilizes these memories to generate character responses. Using a fact-checking pipeline based on GPT-4, our evaluation demonstrates that the character responses are grounded in the retrieved memories and maintain factual accuracy. We discuss the implications of our system for creating engaging and consistent virtual characters and highlight areas for future research, including large language model (LLM) guardrailing and virtual character personality development.