Customer-obsessed science

Contextual query rewriting (CQR) is a crucial component in Conversational AI agents, leveraging the contextual information from previous user-agent conversations to improve the comprehension of current user intent. However, traditional CQR methods often concentrate on supervised fine-tuning only, neglecting the opportunities to learn from user feedback to align with user preferences. Inspired by recent

Embodied task completion is a challenge where an agent in a simulated environment must predict environment actions to complete tasks based on natural language instructions and egocentric visual observations. We propose a variant of this problem where the agent predicts actions at a higher level of abstraction called a plan which more directly tests language understanding and reasoning. We show that multimodal

E-commerce product catalogs contain billions of items. Most products have lengthy titles, as sellers pack them with product attributes to improve retrieval, and highlight key product aspects. This results in a gap between such unnatural product titles and how customers refer to them. It also limits how e-commerce stores can use these seller-provided titles for recommendation, QA, or review summarization

Generative models have been widely applied to solve extractive tasks, where parts of the input are extracted to form the desired output, and have achieved significant success. For example, in extractive question answering (QA), generative models have constantly yielded state-of-the-art results. In this work, we study the issue of tokenization inconsistency that is commonly neglected in training these models

A Personalized Query Rewriting system aims to reduce defective queries to ensure robust conversational functionality by considering individual user behavior and preferences. It’s usually structured as a search-based system, maintaining a user history index of past successful interactions with the conversational AI. However, this approach encounters challenges when dealing with unseen interactions, which