Titlebook: Database Systems for Advanced Applications; 27th International C Arnab Bhattacharya,Janice Lee Mong Li,Rage Uday Ki Conference proceedings

interlude

osteocytes

HRG: A Hybrid Retrieval and Generation Model in Multi-turn Dialogues difficult to maintain the context consistency by using only a few previous turns of the dialogue indiscriminately. Except for the context information, we can retrieve additional candidates from historical contexts, according to semantic similarity. Therefore, in this paper, we integrate the histor

crucial

: A Faithful Contrastive Framework for?Response Generation in?TableQA Systems. Due to the complex syntax of SQL and matching issues with table content, this task is prone to produce factual errors. In this paper, we propose ., a .ithfu. .trastive generation framework to improve the factual correctness of generated responses. . forces the generation model to identify examples

EXPEL

Aspiration

SimEmotion: A Simple Knowledgeable Prompt Tuning Method for Image Emotion Classificationsification are primarily based on proposing new architectures and fine-tuning them on pre-trained Convolutional Neural Networks. Recently, learning transferable visual models from natural language supervision has shown great success in zero-shot settings due to the easily accessible web-scale traini

虛弱的神經(jīng)

Predicting Rumor Veracity on Social Media with Graph Structured Multi-task Learning but the shared layers in multi-task learning tend to yield a compromise between the general and the task-specific representation of structural information. To address this issue, we propose a novel .ulti-.ask .earning framework with .hared .ulti-channel .nteractions (MTL-SMI), which is composed of

陶器

Knowing What I Don’t Know: A Generation Assisted Rejection Framework in Knowledge Base Question Answty, some recent research works introduce external text for KBQA. However, such external information is not always readily available. We argue that it is critical for a KBQA system to know whether it lacks the knowledge to answer a given question. In this paper, we present a novel .neration .ssisted

Choreography

patella

GONG