23 citations · 65 across the 17 of their papers we have counts for
17 papers
A New Role for Relevance: Guiding Corpus Interaction in Agentic Search
Jiangnan Li, Yuqing Li, Mo Yu +2
Relevance is a query-dependent estimate of whether a document or excerpt contains useful evidence. Existing retrieval agents use relevance to select top- content, but document r…
Mental Health Assessment for the Chatbots
Yong Shan, Jinchao Zhang, Zekang Li +2
Previous researches on dialogue system assessment usually focus on the quality evaluation (e.g. fluency, relevance, etc) of responses generated by the chatbots, which are local and…
Constructing Emotion Consensus and Utilizing Unpaired Data for Empathetic Dialogue Generation
Lei Shen, Jinchao Zhang, Jiao Ou +2
Researches on dialogue empathy aim to endow an agent with the capacity of accurate understanding and proper responding for emotions. Existing models for empathetic dialogue generat…
Challenging Instances are Worth Learning: Generating Valuable Negative Samples for Response Selection Training
Yao Qiu, Jinchao Zhang, Huiying Ren +1
Retrieval-based chatbot selects the appropriate response from candidates according to the context, which heavily depends on a response selection module. A response selection module…
Improving Gradient-based Adversarial Training for Text Classification by Contrastive Learning and Auto-Encoder
Yao Qiu, Jinchao Zhang, Jie Zhou
Recent work has proposed several efficient approaches for generating gradient-based adversarial perturbations on embeddings and proved that the model's performance and robustness c…
Different Strokes for Different Folks: Investigating Appropriate Further Pre-training Approaches for Diverse Dialogue Tasks
Yao Qiu, Jinchao Zhang, Jie Zhou
Loading models pre-trained on the large-scale corpus in the general domain and fine-tuning them on specific downstream tasks is gradually becoming a paradigm in Natural Language Pr…
Guiding Topic Flows in the Generative Chatbot by Enhancing the ConceptNet with the Conversation Corpora
Pengda Si, Yao Qiu, Jinchao Zhang +1
Human conversations consist of reasonable and natural topic flows, which are observed as the shifts of the mentioned concepts across utterances. Previous chatbots that incorporate…
Towards Expressive Communication with Internet Memes: A New Multimodal Conversation Dataset and Benchmark
Zhengcong Fei, Zekang Li, Jinchao Zhang +2
As a kind of new expression elements, Internet memes are popular and extensively used in online chatting scenarios since they manage to make dialogues vivid, moving, and interestin…
Structure-Enhanced Pop Music Generation via Harmony-Aware Learning
Xueyao Zhang, Jinchao Zhang, Yao Qiu +2
Pop music generation has always been an attractive topic for both musicians and scientists for a long time. However, automatically composing pop music with a satisfactory structure…
Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification
Shuhuai Ren, Jinchao Zhang, Lei Li +2
Data augmentation aims to enrich training samples for alleviating the overfitting issue in low-resource or class-imbalanced situations. Traditional methods first devise task-specif…
GTM: A Generative Triple-Wise Model for Conversational Question Generation
Lei Shen, Fandong Meng, Jinchao Zhang +2
Generating some appealing questions in open-domain conversations is an effective way to improve human-machine interactions and lead the topic to a broader or deeper direction. To a…
Addressing Inquiries about History: An Efficient and Practical Framework for Evaluating Open-domain Chatbot Consistency
Zekang Li, Jinchao Zhang, Zhengcong Fei +2
A good open-domain chatbot should avoid presenting contradictory responses about facts or opinions in a conversational session, known as its consistency capacity. However, evaluati…
Conversations Are Not Flat: Modeling the Dynamic Information Flow across Dialogue Utterances
Zekang Li, Jinchao Zhang, Zhengcong Fei +2
Nowadays, open-domain dialogue models can generate acceptable responses according to the historical context based on the large-scale pre-trained language models. However, they gene…
Sequence-Level Training for Non-Autoregressive Neural Machine Translation
Chenze Shao, Yang Feng, Jinchao Zhang +2
In recent years, Neural Machine Translation (NMT) has achieved notable results in various translation tasks. However, the word-by-word generation manner determined by the autoregre…
An Iterative Multi-Knowledge Transfer Network for Aspect-Based Sentiment Analysis
Yunlong Liang, Fandong Meng, Jinchao Zhang +3
Aspect-based sentiment analysis (ABSA) mainly involves three subtasks: aspect term extraction, opinion term extraction, and aspect-level sentiment classification, which are typical…
Retrieving Sequential Information for Non-Autoregressive Neural Machine Translation
Chenze Shao, Yang Feng, Jinchao Zhang +3
Non-Autoregressive Transformer (NAT) aims to accelerate the Transformer model through discarding the autoregressive mechanism and generating target words independently, which fails…
BigOP: Generating Comprehensive Big Data Workloads as a Benchmarking Framework
Yuqing Zhu, Jianfeng Zhan, Chuliang Weng +4
Big Data is considered proprietary asset of companies, organizations, and even nations. Turning big data into real treasure requires the support of big data systems. A variety of c…