117 citations · 692 across the 53 of their papers we have counts for
17 papers · 1 filter
MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain Conversation
Jiazhan Feng, Qingfeng Sun, Can Xu +5
Responding with multi-modal content has been recognized as an essential capability for an intelligent conversational agent. In this paper, we introduce the MMDialog dataset to bett…
Fine-Grained Distillation for Long Document Retrieval
Yucheng Zhou, Tao Shen, Xiubo Geng +4
Long document retrieval aims to fetch query-relevant documents from a large-scale collection, where knowledge distillation has become de facto to improve a retriever by mimicking a…
Adam: Dense Retrieval Distillation with Adaptive Dark Examples
Chongyang Tao, Chang Liu, Tao Shen +4
To improve the performance of the dual-encoder retriever, one effective approach is knowledge distillation from the cross-encoder ranker. Existing works construct the candidate pas…
CCPrefix: Counterfactual Contrastive Prefix-Tuning for Many-Class Classification
Yang Li, Canran Xu, Guodong Long +3
Recently, prefix-tuning was proposed to efficiently adapt pre-trained language models to a broad spectrum of natural language classification tasks. It leverages soft prefix as task…
Collaborative Reasoning on Multi-Modal Semantic Graphs for Video-Grounded Dialogue Generation
Xueliang Zhao, Yuxuan Wang, Chongyang Tao +2
We study video-grounded dialogue generation, where a response is generated based on the dialogue context and the associated video. The primary challenges of this task lie in (1) th…
There Is No Standard Answer: Knowledge-Grounded Dialogue Generation with Adversarial Activated Multi-Reference Learning
Xueliang Zhao, Tingchen Fu, Chongyang Tao +1
Knowledge-grounded conversation (KGC) shows excellent potential to deliver an engaging and informative response. However, existing approaches emphasize selecting one golden knowled…
LexMAE: Lexicon-Bottlenecked Pretraining for Large-Scale Retrieval
Tao Shen, Xiubo Geng, Chongyang Tao +5
In large-scale retrieval, the lexicon-weighting paradigm, learning weighted sparse representations in vocabulary space, has shown promising results with high quality and low latenc…
LED: Lexicon-Enlightened Dense Retriever for Large-Scale Retrieval
Kai Zhang, Chongyang Tao, Tao Shen +4
Retrieval models based on dense representations in semantic space have become an indispensable branch for first-stage retrieval. These retrievers benefit from surging advances in r…
Towards Robust Ranker for Text Retrieval
Yucheng Zhou, Tao Shen, Xiubo Geng +5
A ranker plays an indispensable role in the de facto 'retrieval & rerank' pipeline, but its training still lags behind -- learning from moderate negatives or/and serving as an auxi…
KnowDA: All-in-One Knowledge Mixture Model for Data Augmentation in Low-Resource NLP
Yufei Wang, Jiayi Zheng, Can Xu +4
This paper focuses on the data augmentation for low-resource NLP tasks where the training set is limited. The existing solutions either leverage task-independent heuristic rules (e…
UnifieR: A Unified Retriever for Large-Scale Retrieval
Tao Shen, Xiubo Geng, Chongyang Tao +4
Large-scale retrieval is to recall relevant documents from a huge collection given a query. It relies on representation learning to embed documents and queries into a common semant…
Learning to Express in Knowledge-Grounded Conversation
Xueliang Zhao, Tingchen Fu, Chongyang Tao +3
Grounding dialogue generation by extra knowledge has shown great potentials towards building a system capable of replying with knowledgeable and engaging responses. Existing studie…
There Are a Thousand Hamlets in a Thousand People's Eyes: Enhancing Knowledge-grounded Dialogue with Personal Memory
Tingchen Fu, Xueliang Zhao, Chongyang Tao +2
Knowledge-grounded conversation (KGC) shows great potential in building an engaging and knowledgeable chatbot, and knowledge selection is a key ingredient in it. However, previous…
PromDA: Prompt-based Data Augmentation for Low-Resource NLU Tasks
Yufei Wang, Can Xu, Qingfeng Sun +4
This paper focuses on the Data Augmentation for low-resource Natural Language Understanding (NLU) tasks. We propose Prompt-based D}ata Augmentation model (PromDA) which only trains…
TegTok: Augmenting Text Generation via Task-specific and Open-world Knowledge
Chao-Hong Tan, Jia-Chen Gu, Chongyang Tao +5
Generating natural and informative texts has been a long-standing problem in NLP. Much effort has been dedicated into incorporating pre-trained language models (PLMs) with various…
HeterMPC: A Heterogeneous Graph Neural Network for Response Generation in Multi-Party Conversations
Jia-Chen Gu, Chao-Hong Tan, Chongyang Tao +4
Recently, various response generation models for two-party conversations have achieved impressive improvements, but less effort has been paid to multi-party conversations (MPCs) wh…
PCL: Peer-Contrastive Learning with Diverse Augmentations for Unsupervised Sentence Embeddings
Qiyu Wu, Chongyang Tao, Tao Shen +3
Learning sentence embeddings in an unsupervised manner is fundamental in natural language processing. Recent common practice is to couple pre-trained language models with unsupervi…