Intent contrastive learning

Author: clix

August undefined, 2024

NettetFew-Shot-Intent-Detection includes popular challenging intent detection datasets with ... DNNC and CPFT, and the 10-shot learning results of all the models are reported by the paper authors. Citation. ... {zhang2024few, title = {Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuning}, author = {Zhang, Jianguo and Bui ... NettetYongjun Chen, Zhiwei Liu, Jia Li, Julian McAuley, and Caiming Xiong. 2024. Intent Contrastive Learning for Sequential Recommendation. In WWW. 2172--2182. Google Scholar; Guanyi Chu, Xiao Wang, Chuan Shi, and Xunqiang Jiang. 2024. CuCo: Graph representation with curriculum contrastive learning. In IJCAI. 2300--2306. Google …

Intent Contrastive Learning for Sequential Recommendation

NettetarXiv:2109.06349v1 [cs.CL] 13 Sep 2024 Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuning Jian-Guo Zhang1∗, Trung Bui 2, Seunghyun Yoon2, Xiang Chen2, Zhiwei Liu1 Congying Xia1, Quan Hung Tran2, Walter Chang2, Philip Yu1 1 Universityof Illinois at Chicago, Chicago, USA 2Adobe Research, San Jose, USA … NettetarXiv.org e-Print archive bothwell regional health

Co-Modality Graph Contrastive Learning for Imbalanced Node …

NettetIntent Discovery. 9 papers with code • 3 benchmarks • 3 datasets. Given a set of labelled and unlabelled utterances, the idea is to identify existing (known) intents and potential … Nettet9. mar. 2024 · Intent recognition is critical for task-oriented dialogue systems. However, for emerging domains and new services, it is difficult to accurately identify the … NettetThen the acoustic and linguistic embeddings are simul- taneously aligned through cross-modal contrastive learning and fed into an intent classier to predict the intent labels. The model is optimized with two losses: contrastive learn- ing loss from multi-modal embeddings and intent classication loss from the predictions and ground truths. hayabusa feather

Intent Contrastive Learning for Sequential Recommendation

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Nettet1. jun. 2024 · In this work, we propose to model the cooperative association between the two different views through cross-view contrastive learning. By encouraging the alignment of the two separately learned views, each view can distill complementary information from the other view, achieving mutual enhancement. Nettet1. mai 2024 · To deal with such issues, we propose a C ontrastive learning-based T ask Adaptation model (CTA) for few-shot intent recognition. In detail, we leverage contrastive learning to help achieve task adaptation and make full … hayabusa fastest bike in the worldNettetContrastive Learning builds on the notion of a contrastive loss (Chopra et al.,2005), which seeks to create clusters in the embedding space such that examples with a shared class are far from other classes but close to each other. Much like learn-ing that queries with noise have a shared intent, Schroff et al. 15’ leverage contrastive learning to bothwell regional health center ceo

"Nettet5. feb. 2024 · We propose to leverage the learned intents into SR models via contrastive SSL, which maximizes the agreement between a view of sequence and its … " - Intent contrastive learning

Intent contrastive learning

Improving Spoken Language Understanding with Cross-Modal Contrastive …

Nettet14. apr. 2024 · In this work, we propose a novel Multi-behavior Multi-view Contrastive Learning Recommendation (MMCLR) framework, including three new CL tasks to … NettetContrastive learning has the assumption that two views (positive pairs) obtained from the same user behavior sequence must be similar. However, noises typically disturb …

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Nettet10. nov. 2024 · Contrastive learning (CL) benefits the training of sequential recommendation models with informative self-supervision signals. Existing solutions apply general sequential data augmentation strategies to generate positive pairs and encourage their representations to be invariant. NettetOverview We propose a contrastive learning paradigm, named Neighborhood-enriched Contrastive Learning ( NCL ), to explicitly capture potential node relatedness into contrastive learning for graph collaborative filtering. Requirements recbole==1.0.0 python==3.7.7 pytorch==1.7.1 faiss-gpu==1.7.1 cudatoolkit==10.1 Quick Start

Nettet10. apr. 2024 · 摘要：Recently, contrastive learning approaches (e.g., CLIP (Radford et al., 2024)) have received huge success in multimodal learning, where the model tries to minimize the distance between the representations of different views (e.g., image and its caption) of the same data point while keeping the representations of different data … Nettet1. jun. 2024 · CrossCBR: Cross-view Contrastive Learning for Bundle Recommendation. Yunshan Ma, Yingzhi He, An Zhang, Xiang Wang, Tat-Seng Chua. Bundle …

NettetUser intent discovery is a key step in developing a Natural Language Understanding (NLU) module at the core of any modern Conversational AI system. Typically, human experts review a representative sample of user input data to discover new intents, which is subjective, costly, and error-prone. Nettet14. apr. 2024 · The key challenge is how to learn discriminative intent representations that are beneficial for distinguishing in-domain ... Then, we present an inter-class constraint contrastive learning ...

NettetBecause mental contrasting supplies motivation for implementation intentions, the two techniques are frequently taught together as a combined* technique: mental contrasting …

NettetContrastive Curriculum Learning for Sequential User Behavior Modeling via Data Augmentation. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 3737--3746. Google ScholarDigital Library Renqin Cai, Jibang Wu, Aidan San, Chong Wang, and Hongning Wang. 2024. bothwell regional health center addressNettet本文提出了意图对比学习（ICL），通过聚类将潜在意图变量引入SR。其核心思想是从未标记的用户行为序列中学习用户的意图分布函数，并通过考虑学习的意图来优化SR模 … hayabusa fight gearNettet7. apr. 2024 · User intent discovery is a key step in developing a Natural Language Understanding (NLU) module at the core of any modern Conversational AI … bothwell regional health center foundationNettet5. feb. 2024 · We propose to leverage the learned intents into SR models via contrastive SSL, which maximizes the agreement between a view of sequence and its … bothwell regional health center employmentNettetIntent Contrastive Learning for Sequential Recommendation (ICLRec) Source code for paper: Intent Contrastive Learning for Sequential Recommendation Introduction … hayabusa fight stickNettetcontrastive self-supervised pre-training on intent datasets without using any intent labels. We then jointly perform few-shot intent detection and su-pervised contrastive … bothwell regional health center erNettet构建基于节点语义关系的对比学习任务：将每个用户（物品）与它具有相似语义关系的节点进行对比。这里具有语义关系指的是，图上不可到达，但具有相似物品特征、用户偏好等的节点。怎么识别具有相同语义的节点呢？我们认为相似的节点倾向于落在临近的embedding空间中，而我们的目标就是寻找代表一组语义邻居的中心（原型）。因 … hayabusa fight promo code