84 citations · 318 across the 18 of their papers we have counts for
18 papers
SSP: Self-Supervised Post-training for Conversational Search
Quan Tu, Shen Gao, Xiaolong Wu +3
Conversational search has been regarded as the next-generation search paradigm. Constrained by data scarcity, most existing methods distill the well-trained ad-hoc retriever to the…
SpokenWOZ: A Large-Scale Speech-Text Benchmark for Spoken Task-Oriented Dialogue Agents
Shuzheng Si, Wentao Ma, Haoyu Gao +7
Task-oriented dialogue (TOD) models have made significant progress in recent years. However, previous studies primarily focus on datasets written by annotators, which has resulted…
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…
All in One: Exploring Unified Video-Language Pre-training
Alex Jinpeng Wang, Yixiao Ge, Rui Yan +7
Mainstream Video-Language Pre-training models \cite{actbert,clipbert,violet} consist of three parts, a video encoder, a text encoder, and a video-text fusion Transformer. They purs…
Revitalize Region Feature for Democratizing Video-Language Pre-training of Retrieval
Guanyu Cai, Yixiao Ge, Binjie Zhang +8
Recent dominant methods for video-language pre-training (VLP) learn transferable representations from the raw pixels in an end-to-end manner to achieve advanced performance on down…
CUGE: A Chinese Language Understanding and Generation Evaluation Benchmark
Yuan Yao, Qingxiu Dong, Jian Guan +32
Realizing general-purpose language intelligence has been a longstanding goal for natural language processing, where standard evaluation benchmarks play a fundamental and guiding ro…
Object-aware Video-language Pre-training for Retrieval
Alex Jinpeng Wang, Yixiao Ge, Guanyu Cai +5
Recently, by introducing large-scale dataset and strong transformer network, video-language pre-training has shown great success especially for retrieval. Yet, existing video-langu…
TaskDrop: A Competitive Baseline for Continual Learning of Sentiment Classification
Jianping Mei, Yilun Zheng, Qianwei Zhou +1
In this paper, we study the multi-task sentiment classification problem in the continual learning setting, i.e., a model is sequentially trained to classifier the sentiment of revi…
Modeling Text-visual Mutual Dependency for Multi-modal Dialog Generation
Shuhe Wang, Yuxian Meng, Xiaofei Sun +5
Multi-modal dialog modeling is of growing interest. In this work, we propose frameworks to resolve a specific case of multi-modal dialog generation that better mimics multi-modal d…
OpenViDial: A Large-Scale, Open-Domain Dialogue Dataset with Visual Contexts
Yuxian Meng, Shuhe Wang, Qinghong Han +4
When humans converse, what a speaker will say next significantly depends on what he sees. Unfortunately, existing dialogue models generate dialogue utterances only based on precedi…
Learning to Customize Model Structures for Few-shot Dialogue Generation Tasks
Yiping Song, Zequn Liu, Wei Bi +2
Training the generative models with minimal corpus is one of the critical challenges for building open-domain dialogue systems. Existing methods tend to use the meta-learning frame…
Neural Emoji Recommendation in Dialogue Systems
Ruobing Xie, Zhiyuan Liu, Rui Yan +1
Emoji is an essential component in dialogues which has been broadly utilized on almost all social platforms. It could express more delicate feelings beyond plain texts and thus smo…
Dialogue Session Segmentation by Embedding-Enhanced TextTiling
Yiping Song, Lili Mou, Rui Yan +4
In human-computer conversation systems, the context of a user-issued utterance is particularly important because it provides useful background information of the conversation. Howe…
Sequence to Backward and Forward Sequences: A Content-Introducing Approach to Generative Short-Text Conversation
Lili Mou, Yiping Song, Rui Yan +3
Using neural networks to generate replies in human-computer dialogue systems is attracting increasing attention over the past few years. However, the performance is not satisfactor…
StalemateBreaker: A Proactive Content-Introducing Approach to Automatic Human-Computer Conversation
Xiang Li, Lili Mou, Rui Yan +1
Existing open-domain human-computer conversation systems are typically passive: they either synthesize or retrieve a reply provided a human-issued utterance. It is generally presum…
How Transferable are Neural Networks in NLP Applications?
Lili Mou, Zhao Meng, Rui Yan +4
Transfer learning is aimed to make use of valuable knowledge in a source domain to help model performance in a target domain. It is particularly important to neural networks, which…
Backward and Forward Language Modeling for Constrained Sentence Generation
Lili Mou, Rui Yan, Ge Li +2
Recent language models, especially those based on recurrent neural networks (RNNs), make it possible to generate natural language from a learned probability. Language generation ha…
Natural Language Inference by Tree-Based Convolution and Heuristic Matching
Lili Mou, Rui Men, Ge Li +4
In this paper, we propose the TBCNN-pair model to recognize entailment and contradiction between two sentences. In our model, a tree-based convolutional neural network (TBCNN) capt…