Zhou Zhao

Investigating Deep Reinforcement Learning Techniques in Personalized Dialogue Generation

2018 · Society for Industrial and Applied Mathematics eBooks

In this paper, we propose a personalized dialogue generation system, which combines reinforcement learning techniques with an attention-based hierarchical recurrent encoderdecoder model. Firstly, we incorporate user-specific information into the decoder to capture user's background information …

Improving automatic source code summarization via deep reinforcement learning

2018

Code summarization provides a high level natural language description of the function performed by code, as it can benefit the software maintenance, code categorization and retrieval. To the best of our knowledge, most state-of-the-art approaches …

An Effective Hybrid Learning Model for Real-Time Event Summarization

2020 · IEEE Transactions on Neural Networks and Learning Systems

Real-time event summarization (RES) aims at extracting a handful of document updates from an overwhelming document stream as the real-time event summary that tracks and summarizes the evolving event of interest. It has been attracting …

UniSinger: Unified End-to-End Singing Voice Synthesis With Cross-Modality Information Matching

2023

Though previous works have shown remarkable achievements in singing voice generation, most existing models focus on one specific application and there is a lack of unified singing voice synthesis models. In addition to low relevance …

MobileSpeech: A Fast and High-Fidelity Framework for Mobile Zero-Shot Text-to-Speech

2024 · arXiv (Cornell University)

Zero-shot text-to-speech (TTS) has gained significant attention due to its powerful voice cloning capabilities, requiring only a few seconds of unseen speaker voice prompts. However, all previous work has been developed for cloud-based systems. Taking …

Multimodal Prompt Learning with Missing Modalities for Sentiment Analysis and Emotion Recognition

2024 · arXiv (Cornell University)

The development of multimodal models has significantly advanced multimodal sentiment analysis and emotion recognition. However, in real-world applications, the presence of various missing modality cases often leads to a degradation in the model's performance. In …

OmniChat: Enhancing Spoken Dialogue Systems with Scalable Synthetic Data for Diverse Scenarios

2025 · arXiv (Cornell University)

With the rapid development of large language models, researchers have created increasingly advanced spoken dialogue systems that can naturally converse with humans. However, these systems still struggle to handle the full complexity of real-world conversations, …

MEMEN: Multi-layer Embedding with Memory Networks for Machine Comprehension

2017 · arXiv (Cornell University)

Machine comprehension(MC) style question answering is a representative problem in natural language processing. Previous methods rarely spend time on the improvement of encoding layer, especially the embedding of syntactic information and name entity of the …

Multilingual Neural Machine Translation with Knowledge Distillation

2019 · arXiv (Cornell University)

Multilingual machine translation, which translates multiple languages with a single model, has attracted much attention due to its efficiency of offline training and online serving. However, traditional multilingual translation usually yields inferior accuracy compared with …

Dialogue Act Recognition via CRF-Attentive Structured Network

2018

Dialogue Act Recognition (DAR) is a challenging problem in dialogue interpretation, which aims to associate semantic labels to utterances and characterize the speaker's intention. Currently, many existing approaches formulate the DAR problem ranging from multi-classification …