Dongyan Zhao

An Ensemble of Retrieval-Based and Generation-Based Human-Computer Conversation Systems

2018

Human-computer conversation systems have attracted much attention in Natural Language Processing. Conversation systems can be roughly divided into two categories: retrieval-based and generation-based systems. Retrieval systems search a user-issued utterance (namely a query ) in …

Jointly Learning Entity and Relation Representations for Entity Alignment

2019

Yuting Wu, Xiao Liu, Yansong Feng, Zheng Wang, Dongyan Zhao. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Meaningful Answer Generation of E-Commerce Question-Answering

2020 · arXiv (Cornell University)

In e-commerce portals, generating answers for product-related questions has become a crucial task. In this paper, we focus on the task of product-aware answer generation, which learns to generate an accurate and complete answer from …

BioGen: Generating Biography Summary under Table Guidance on Wikipedia

2021

Capturing the salient information from an input article has been a long-standing challenge for summarization. On Wikipedia, most of the wiki pages about people contain a factual table that lists the basic properties of the …

RankCSE: Unsupervised Sentence Representations Learning via Learning to Rank

2023 · arXiv (Cornell University)

Unsupervised sentence representation learning is one of the fundamental problems in natural language processing with various downstream applications. Recently, contrastive learning has been widely adopted which derives high-quality sentence representations by pulling similar semantics closer …

Multi-Granularity Information Interaction Framework for Incomplete Utterance Rewriting

2023 · arXiv (Cornell University)

Recent approaches in Incomplete Utterance Rewriting (IUR) fail to capture the source of important words, which is crucial to edit the incomplete utterance, and introduce words from irrelevant utterances. We propose a novel and effective …

Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Sampling

2015

Syntactic features play an essential role in identifying relationship in a sentence. Previous neural network models directly work on raw word sequences or constituent parse trees, thus often suffer from irrelevant information introduced when subjects …

RUBER: An Unsupervised Method for Automatic Evaluation of Open-Domain Dialog Systems

2018 · Proceedings of the AAAI Conference on Artificial Intelligence

Open-domain human-computer conversation has been attracting increasing attention over the past few years. However, there does not exist a standard automatic evaluation metric for open-domain dialog systems; researchers usually resort to human annotation for model …

How to Make Context More Useful? An Empirical Study on Context-Aware Neural Conversational Models

2017

Generative conversational systems are attracting increasing attention in natural language processing (NLP). Recently, researchers have noticed the importance of context information in dialog processing, and built various models to utilize context. However, there is no …

Towards Implicit Content-Introducing for Generative Short-Text Conversation Systems

2017

The study on human-computer conversation systems is a hot research topic nowadays. One of the prevailing methods to build the system is using the generative Sequence-to-Sequence (Seq2Seq) model through neural networks. However, the standard Seq2Seq …

Smarter Response with Proactive Suggestion: A New Generative Neural Conversation Paradigm

2018

Conversational systems are becoming more and more promising by playing an important role in human-computer communications. A conversational system is supposed to be intelligent to enable human-like interactions. The long-term goal of smart human-computer conversations …

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 …

Plan-and-Write: Towards Better Automatic Storytelling

2019 · Proceedings of the AAAI Conference on Artificial Intelligence

Automatic storytelling is challenging since it requires generating long, coherent natural language to describes a sensible sequence of events. Despite considerable efforts on automatic story generation in the past, prior work either is restricted in …

Question Answering on Freebase via Relation Extraction and Textual Evidence

2016

Existing knowledge-based question answering systems often rely on small annotated training data. While shallow methods like relation extraction are robust to data scarcity, they are less expressive than the deep meaning representation methods like semantic …

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 …

VMSMO: Learning to Generate Multimodal Summary for Video-based News Articles

2020

A popular multimedia news format nowadays is providing users with a lively video and a corresponding news article, which is employed by influential news media including CNN, BBC, and social media including Twitter and Weibo. …

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 …