Search Results for author: Jingang Wang

Found 27 papers, 10 papers with code

Decoupling Pseudo Label Disambiguation and Representation Learning for Generalized Intent Discovery

1 code implementation28 May 2023 Yutao Mou, Xiaoshuai Song, Keqing He, Chen Zeng, Pei Wang, Jingang Wang, Yunsen Xian, Weiran Xu

Previous methods suffer from a coupling of pseudo label disambiguation and representation learning, that is, the reliability of pseudo labels relies on representation learning, and representation learning is restricted by pseudo labels in turn.

Pseudo Label

RankCSE: Unsupervised Sentence Representations Learning via Learning to Rank

no code implementations26 May 2023 Jiduan Liu, Jiahao Liu, Qifan Wang, Jingang Wang, Wei Wu, Yunsen Xian, Dongyan Zhao, Kai Chen, Rui Yan

In this paper, we propose a novel approach, RankCSE, for unsupervised sentence representation learning, which incorporates ranking consistency and ranking distillation with contrastive learning into a unified framework.

Contrastive Learning Learning-To-Rank +3

Task-agnostic Distillation of Encoder-Decoder Language Models

no code implementations21 May 2023 Chen Zhang, Yang Yang, Jingang Wang, Dawei Song

Finetuning pretrained language models (LMs) have enabled appealing performance on a diverse array of tasks.

Abstractive Text Summarization

Lifting the Curse of Capacity Gap in Distilling Language Models

1 code implementation20 May 2023 Chen Zhang, Yang Yang, Jiahao Liu, Jingang Wang, Yunsen Xian, Benyou Wang, Dawei Song

However, when the capacity gap between the teacher and the student is large, a curse of capacity gap appears, invoking a deficiency in distilling LMs.

Knowledge Distillation

Solve the Puzzle of Instance Segmentation in Videos: A Weakly Supervised Framework with Spatio-Temporal Collaboration

no code implementations15 Dec 2022 Liqi Yan, Qifan Wang, Siqi Ma, Jingang Wang, Changbin Yu

Instance segmentation in videos, which aims to segment and track multiple objects in video frames, has garnered a flurry of research attention in recent years.

Depth Estimation Instance Segmentation +2

UniNL: Aligning Representation Learning with Scoring Function for OOD Detection via Unified Neighborhood Learning

1 code implementation19 Oct 2022 Yutao Mou, Pei Wang, Keqing He, Yanan Wu, Jingang Wang, Wei Wu, Weiran Xu

Specifically, we design a K-nearest neighbor contrastive learning (KNCL) objective for representation learning and introduce a KNN-based scoring function for OOD detection.

Contrastive Learning Out of Distribution (OOD) Detection +2

Semi-Supervised Knowledge-Grounded Pre-training for Task-Oriented Dialog Systems

1 code implementation17 Oct 2022 Weihao Zeng, Keqing He, Zechen Wang, Dayuan Fu, Guanting Dong, Ruotong Geng, Pei Wang, Jingang Wang, Chaobo Sun, Wei Wu, Weiran Xu

Recent advances in neural approaches greatly improve task-oriented dialogue (TOD) systems which assist users to accomplish their goals.

Watch the Neighbors: A Unified K-Nearest Neighbor Contrastive Learning Framework for OOD Intent Discovery

1 code implementation17 Oct 2022 Yutao Mou, Keqing He, Pei Wang, Yanan Wu, Jingang Wang, Wei Wu, Weiran Xu

For OOD clustering stage, we propose a KCC method to form compact clusters by mining true hard negative samples, which bridges the gap between clustering and representation learning.

Contrastive Learning Intent Discovery +2

XPrompt: Exploring the Extreme of Prompt Tuning

no code implementations10 Oct 2022 Fang Ma, Chen Zhang, Lei Ren, Jingang Wang, Qifan Wang, Wei Wu, Xiaojun Quan, Dawei Song

Prompt tuning learns soft prompts to condition frozen Pre-trained Language Models (PLMs) for performing downstream tasks in a parameter-efficient manner.

Unified Knowledge Prompt Pre-training for Customer Service Dialogues

no code implementations31 Aug 2022 Keqing He, Jingang Wang, Chaobo Sun, Wei Wu

In this paper, we propose a novel unified knowledge prompt pre-training framework, UFA (\textbf{U}nified Model \textbf{F}or \textbf{A}ll Tasks), for customer service dialogues.

Natural Language Understanding Text Generation

MiniDisc: Minimal Distillation Schedule for Language Model Compression

no code implementations29 May 2022 Chen Zhang, Yang Yang, Qifan Wang, Jiahao Liu, Jingang Wang, Yunsen Xian, Wei Wu, Dawei Song

In particular, motivated by the finding that the performance of the student is positively correlated to the scale-performance tradeoff of the teacher assistant, MiniDisc is designed with a $\lambda$-tradeoff to measure the optimality of the teacher assistant without trial distillation to the student.

Knowledge Distillation Language Modelling +2

Making Pretrained Language Models Good Long-tailed Learners

1 code implementation11 May 2022 Chen Zhang, Lei Ren, Jingang Wang, Wei Wu, Dawei Song

Prompt-tuning has shown appealing performance in few-shot classification by virtue of its capability in effectively exploiting pre-trained knowledge.


GNN-encoder: Learning a Dual-encoder Architecture via Graph Neural Networks for Dense Passage Retrieval

no code implementations18 Apr 2022 Jiduan Liu, Jiahao Liu, Yang Yang, Jingang Wang, Wei Wu, Dongyan Zhao, Rui Yan

To enhance the performance of dense retrieval models without loss of efficiency, we propose a GNN-encoder model in which query (passage) information is fused into passage (query) representations via graph neural networks that are constructed by queries and their top retrieved passages.

Natural Questions Passage Retrieval +2

Deep Partial Multiplex Network Embedding

no code implementations5 Mar 2022 Qifan Wang, Yi Fang, Anirudh Ravula, Ruining He, Bin Shen, Jingang Wang, Xiaojun Quan, Dongfang Liu

Network embedding is an effective technique to learn the low-dimensional representations of nodes in networks.

Link Prediction Network Embedding +1

VIRT: Improving Representation-based Models for Text Matching through Virtual Interaction

no code implementations8 Dec 2021 Dan Li, Yang Yang, Hongyin Tang, Jingang Wang, Tong Xu, Wei Wu, Enhong Chen

With the booming of pre-trained transformers, representation-based models based on Siamese transformer encoders have become mainstream techniques for efficient text matching.

Text Matching

Improving Document Representations by Generating Pseudo Query Embeddings for Dense Retrieval

no code implementations ACL 2021 Hongyin Tang, Xingwu Sun, Beihong Jin, Jingang Wang, Fuzheng Zhang, Wei Wu

Recently, the retrieval models based on dense representations have been gradually applied in the first stage of the document retrieval tasks, showing better performance than traditional sparse vector space models.


ASAP: A Chinese Review Dataset Towards Aspect Category Sentiment Analysis and Rating Prediction

1 code implementation NAACL 2021 Jiahao Bu, Lei Ren, Shuang Zheng, Yang Yang, Jingang Wang, Fuzheng Zhang, Wei Wu

Aspect category sentiment analysis (ACSA) and review rating prediction (RP) are two essential tasks to detect the fine-to-coarse sentiment polarities.

Sentiment Analysis

Query-aware Tip Generation for Vertical Search

no code implementations19 Oct 2020 Yang Yang, Junmei Hao, Canjia Li, Zili Wang, Jingang Wang, Fuzheng Zhang, Rao Fu, Peixu Hou, Gong Zhang, Zhongyuan Wang

Existing work on tip generation does not take query into consideration, which limits the impact of tips in search scenarios.

Decision Making

Earlier Attention? Aspect-Aware LSTM for Aspect-Based Sentiment Analysis

no code implementations19 May 2019 Bowen Xing, Lejian Liao, Dandan song, Jingang Wang, Fuzheng Zhang, Zhongyuan Wang, He-Yan Huang

This paper proposes a novel variant of LSTM, termed as aspect-aware LSTM (AA-LSTM), which incorporates aspect information into LSTM cells in the context modeling stage before the attention mechanism.

Aspect-Based Sentiment Analysis (ABSA)

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