Search Results for author: Mingming Li

Found 7 papers, 2 papers with code

A Multi-Granularity Matching Attention Network for Query Intent Classification in E-commerce Retrieval

no code implementations28 Mar 2023 Chunyuan Yuan, Yiming Qiu, Mingming Li, Haiqing Hu, Songlin Wang, Sulong Xu

However, these models cannot capture multi-granularity matching features from queries and categories, which makes them hard to mitigate the gap in the expression between informal queries and categories.

intent-classification Intent Classification +2

Learning Multi-Stage Multi-Grained Semantic Embeddings for E-Commerce Search

no code implementations20 Mar 2023 Binbin Wang, Mingming Li, Zhixiong Zeng, Jingwei Zhuo, Songlin Wang, Sulong Xu, Bo Long, Weipeng Yan

Retrieving relevant items that match users' queries from billion-scale corpus forms the core of industrial e-commerce search systems, in which embedding-based retrieval (EBR) methods are prevailing.

Retrieval

Learning to Generate Time Series Conditioned Graphs with Generative Adversarial Nets

no code implementations3 Mar 2020 Shanchao Yang, Jing Liu, Kai Wu, Mingming Li

Differently, in this paper, we are interested in a novel problem named Time Series Conditioned Graph Generation: given an input multivariate time series, we aim to infer a target relation graph modeling the underlying interrelationships between time series with each node corresponding to each time series.

Graph Generation Time Series +1

Multi-hop Selector Network for Multi-turn Response Selection in Retrieval-based Chatbots

1 code implementation IJCNLP 2019 Chunyuan Yuan, Wei Zhou, Mingming Li, Shangwen Lv, Fuqing Zhu, Jizhong Han, Songlin Hu

Existing works mainly focus on matching candidate responses with every context utterance on multiple levels of granularity, which ignore the side effect of using excessive context information.

Conversational Response Selection Retrieval

Role Playing Learning for Socially Concomitant Mobile Robot Navigation

no code implementations29 May 2017 Mingming Li, Rui Jiang, Shuzhi Sam Ge, Tong Heng Lee

In this paper, we present the Role Playing Learning (RPL) scheme for a mobile robot to navigate socially with its human companion in populated environments.

Navigate Reinforcement Learning (RL) +1

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