Can Xu

Neural Response Generation with Meta-words

2019

We present open domain response generation with meta-words. A meta-word is a structured record that describes various attributes of a response, and thus allows us to explicitly model the one-to-many relationship within open domain dialogues …

Geometric Discriminant Analysis for I-vector Based Speaker Verification

2019

Many i-vector based speaker verification use linear discriminant analysis (LDA) as a post-processing stage. LDA maximizes the arithmetic mean of the Kullback-Leibler (KL) divergences between different pairs of speakers. However, for speaker verification, speakers with …

Weakly Supervised Object Localization With Progressive Activation Diffusion

2025 · IEEE Transactions on Neural Networks and Learning Systems

Weakly supervised object localization (WSOL) aims to locate objects with only image-level labels. Previous works mainly follow the framework of class activation map (CAM), which discovers the objects by estimating the contribution of each pixel …

A Simple "Try Again" Can Elicit Multi-Turn LLM Reasoning

2025 · arXiv (Cornell University)

Multi-turn problem solving is critical yet challenging for Large Reasoning Models (LRMs) to reflect on their reasoning and revise from feedback. Existing Reinforcement Learning (RL) methods train large reasoning models on a single-turn paradigm with …

Latent Cross

2018

The success of recommender systems often depends on their ability to understand and make use of the context of the recommendation request. Significant research has focused on how time, location, interfaces, and a plethora of …

Neural Response Generation With Dynamic Vocabularies

2018 · Proceedings of the AAAI Conference on Artificial Intelligence

We study response generation for open domain conversation in chatbots. Existing methods assume that words in responses are generated from an identical vocabulary regardless of their inputs, which not only makes them vulnerable to generic …

Multi-Representation Fusion Network for Multi-Turn Response Selection in Retrieval-Based Chatbots

2019

We consider context-response matching with multiple types of representations for multi-turn response selection in retrieval-based chatbots. The representations encode semantics of contexts and responses on words, n-grams, and sub-sequences of utterances, and capture both short-term …

One Time of Interaction May Not Be Enough: Go Deep with an Interaction-over-Interaction Network for Response Selection in Dialogues

2019

Currently, researchers have paid great attention to retrieval-based dialogues in opendomain. In particular, people study the problem by investigating context-response matching for multi-turn response selection based on publicly recognized benchmark data sets. State-of-the-art methods require …

Low-Resource Knowledge-Grounded Dialogue Generation

2020 · arXiv (Cornell University)

Responding with knowledge has been recognized as an important capability for an intelligent conversational agent. Yet knowledge-grounded dialogues, as training data for learning such a response generation model, are difficult to obtain. Motivated by the …

Knowledge-Grounded Dialogue Generation with Pre-trained Language Models

2020

We study knowledge-grounded dialogue generation with pre-trained language models. To leverage the redundant external knowledge under capacity constraint, we propose equipping response generation defined by a pretrained language model with a knowledge selection module, and …