BERT with History Answer Embedding for Conversational Question Answering

Conversational search is an emerging topic in the information retrieval community. One of the major challenges to multi-turn conversational search is to model the conversation history to answer the current question. Existing methods either prepend history turns to the current question or use complicated attention mechanisms to model the history. We propose a conceptually simple yet highly effective approach referred to as history answer embedding. It enables seamless integration of conversation history into a conversational question answering (ConvQA) model built on BERT (Bidirectional Encoder Representations from Transformers). We first explain our view that ConvQA is a simplified but concrete setting of conversational search, and then we provide a general framework to solve ConvQA. We further demonstrate the effectiveness of our approach under this framework. Finally, we analyze the impact of different numbers of history turns under different settings to provide new insights into conversation history modeling in ConvQA.Comment: Accepted to SIGIR 2019 as a short pape

A latent variable model for viewpoint discovery from threaded forum posts

Threaded discussion forums provide an important social media platform. Its rich user generated content has served as an important source of public feedback. To automatically discover the viewpoints or stances on hot issues from forum threads is an important and useful task. In this paper, we propose a novel latent variable model for viewpoint discovery from threaded forum posts. Our model is a principled generative latent variable model which captures three important factors: viewpoint specific topic preference, user identity and user interactions. Evaluation results show that our model clearly outperforms a number of baseline models in terms of both clustering posts based on viewpoints and clustering users with different viewpoints.

FashionLOGO: Prompting Multimodal Large Language Models for Fashion Logo Embeddings

Logo embedding plays a crucial role in various e-commerce applications by facilitating image retrieval or recognition, such as intellectual property protection and product search. However, current methods treat logo embedding as a purely visual problem, which may limit their performance in real-world scenarios. A notable issue is that the textual knowledge embedded in logo images has not been adequately explored. Therefore, we propose a novel approach that leverages textual knowledge as an auxiliary to improve the robustness of logo embedding. The emerging Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in both visual and textual understanding and could become valuable visual assistants in understanding logo images. Inspired by this observation, our proposed method, FashionLOGO, aims to utilize MLLMs to enhance fashion logo embedding. We explore how MLLMs can improve logo embedding by prompting them to generate explicit textual knowledge through three types of prompts, including image OCR, brief captions, and detailed descriptions prompts, in a zero-shot setting. We adopt a cross-attention transformer to enable image embedding queries to learn supplementary knowledge from textual embeddings automatically. To reduce computational costs, we only use the image embedding model in the inference stage, similar to traditional inference pipelines. Our extensive experiments on three real-world datasets demonstrate that FashionLOGO learns generalized and robust logo embeddings, achieving state-of-the-art performance in all benchmark datasets. Furthermore, we conduct comprehensive ablation studies to demonstrate the performance improvements resulting from the introduction of MLLMs

Design and fabrication of whisker hybrid ceramic membranes with narrow pore size distribution and high permeability via co-sintering process

Ceramic microfiltration membranes (MF) with narrow pore size distribution and high permeability are widely used for the preparation of ceramic ultrafiltration membranes (UF) and in wastewater treatment. In this work, a whisker hybrid ceramic membrane (WHCM) consisting of a whisker layer and an alumina layer was designed to achieve high permeability and narrow pore size distribution based on the relative resistance obtained using the Hagen-Poiseuille and Darcy equations. The whisker layer was designed to prevent the penetration of alumina particles into the support and ensure a high porosity of the membrane, while the alumina layer provided a smooth surface and narrow pore size distribution. Mass transfer resistance is critical to reduce the effect of the membrane layers. It was found that the resistance of the WHCM depended largely on the alumina layer. The effect of the support and whisker layer on the resistance of the WHCM was negligible. This was consistent with theoretical calculations. The WHCM was co-sintered at 1000 °C, which resulted in a high permeability of ~ 645 L m−1 h−1 ;bar−1 and a narrow pore size distribution of ~ 100 nm. Co-sintering was carried out on a macroporous ceramic support (just needed one sintering process), which greatly reduced the preparation cost and time. The WHCM (as the sub-layer) also showed a great potential to be used for the fabrication of ceramic UF membranes with high repeatability. Hence, this study provides an efficient approach for the fabrication of advanced ceramic MF membranes on macroporous supports, allowing for rapid prototyping with scale-up capability