We investigate the problem of synthesizing relevant visual imagery from generic long-form text, leveraging Large Language Models (LLMs) and Text-to-Image Models (TIMs). Current Text-to-Image models require short prompts that describe the image content and style explicitly. Unlike image prompts, generation of images from general long-form text requires the image synthesis system to derive the visual content and style elements from the text. In this paper, we study zero-shot prompting and supervised fine-tuning approaches that use LLMs and TIMs jointly for synthesizing images. We present an empirical study on generating images for Wikipedia articles covering a broad spectrum of topic and image styles. We compare these systems using a suite of metrics, including a novel metric specifically designed to evaluate the semantic correctness of generated images. Our study offers a preliminary understanding of existing models’ strengths and limitation for the task of image generation from long-form text, and sets up an evaluation framework and establishes baselines for future research.

Query rewriting (QR) is an important technique for user friction (i.e. recovering ASR error or system error) reduction and contextual carryover (i.e. ellipsis and co-reference) in conversational AI systems. Recently, generation-based QR models have achieved promising results on these two tasks separately. Although these two tasks have many similarities such as they both use the previous dialogue along with the current request as model input, there is no unified model to solve them jointly. To this end, we propose a unified contextual query rewriting model that unifies QR for both reducing friction and contextual carryover purpose. Moreover, we involve multiple auxiliary tasks such as trigger prediction and NLU interpretation tasks to boost the performance of the rewrite. We leverage the text-to-text unified framework which uses independent tasks with weighted loss to account for task importance. Then we propose new unified multitask learning strategies including a sequential model which outputs one sentence for multi-tasks, and a hybrid model where some tasks are independent and some tasks are sequentially generated. Our experimental results demonstrate the effectiveness of the proposed unified learning methods.

Open domain conversational agents can answer a broad range of targeted queries. However, the sequential nature of interaction with these systems makes knowledge exploration a lengthy task which burdens the user with asking a chain of well phrased questions. In this paper, we present a retrieval based system and associated dataset for predicting the next questions that the user might have. Such a system can proactively assist users in knowledge exploration leading to a more engaging dialog. The retrieval system is trained on a dataset called the Follow-up Query Bank (FQ-Bank). FQ-Bank contains ~14K multi-turn information-seeking conversations with a valid follow-up question and a set of invalid candidates. The invalid candidates are generated to simulate various syntactic and semantic confounders such as paraphrases, partial entity match, irrelevant entity, and ASR errors. We use confounder specific techniques to simulate these negative examples on the OR-QuAC dataset. Then, we train ranking models on FQ-Bank and present results comparing supervised and unsupervised approaches. The results suggest that we can retrieve the valid follow-ups by ranking them in higher positions compared to confounders, but further knowledge grounding can improve ranking performance.FQ-Bank is publicly available at https://github.com/amazon-science/fq-bank.

Multi-modality support has become an integral part of creating a seamless user experience with modern voice assistants with smart displays. Users refer to images, video thumbnails, or the accompanying text descriptions on the screen through voice communication with AI powered devices. This raises the need to either augment existing commercial voice only dialogue systems with state-of-the-art multimodal components, or to introduce entirely new architectures; where the latter can lead to costly system revamps. To support the emerging visual navigation and visual product selection use cases, we propose to augment commercially deployed voice-only dialogue systems with additional multi-modal components. In this work, we present a novel yet pragmatic approach to expand an existing dialogue-based context carryover system (Chen et al., 2019a) in a voice assistant with state-of-the-art multimodal components to facilitate quick delivery of visual modality support with minimum changes. We demonstrate a 35% accuracy improvement over the existing system on an in-house multi-modal visual navigation data set.