Rui Yan

i, poet: automatic poetry composition through recurrent neural networks with iterative polishing schema

2016 · International Joint Conference on Artificial Intelligence

Part of the long lasting cultural heritage of humanity is the art of classical poems, which are created by fitting words into certain formats and representations. Automatic poetry composition by computers is considered as a …

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 …

AIR: Attentional Intention-Aware Recommender Systems

2019

The capability of extracting sequential patterns from the user-item interaction data is now becoming a key feature of recommender systems. Though it is important to capture the sequential effect, existing methods only focus on modelling …

EnsembleGAN: Adversarial Learning for Retrieval-Generation Ensemble Model on Short-Text Conversation

2020 · arXiv (Cornell University)

Generating qualitative responses has always been a challenge for human-computer dialogue systems. Existing dialogue systems generally derive from either retrieval-based or generative-based approaches, both of which have their own pros and cons. Despite the natural …

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 …

There Are a Thousand Hamlets in a Thousand People’s Eyes: Enhancing Knowledge-grounded Dialogue with Personal Memory

2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Knowledge-grounded conversation (KGC) shows great potential in building an engaging and knowledgeable chatbot, and knowledge selection is a key ingredient in it. However, previous methods for knowledge selection only concentrate on the relevance between knowledge …

Unsupervised Mitigating Gender Bias by Character Components: A Case Study of Chinese Word Embedding

2022

Word embeddings learned from massive text collections have demonstrated significant levels of discriminative biases. However, debiasing on the Chinese language, one of the most spoken languages, has been less explored. Meanwhile, existing literature relies on …

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 …

SSP: Self-Supervised Post-training for Conversational Search

2023

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 conversational retriever.However, these methods, which usually initialize parameters by query reformulation to …

DetermLR: Augmenting LLM-based Logical Reasoning from Indeterminacy to Determinacy

2023 · arXiv (Cornell University)

Recent advances in large language models (LLMs) have revolutionized the landscape of reasoning tasks. To enhance the capabilities of LLMs to emulate human reasoning, prior studies have focused on modeling reasoning steps using various thought …

StreamingDialogue: Prolonged Dialogue Learning via Long Context Compression with Minimal Losses

2024 · arXiv (Cornell University)

Standard Large Language Models (LLMs) struggle with handling dialogues with long contexts due to efficiency and consistency issues. According to our observation, dialogue contexts are highly structured, and the special token of \textit{End-of-Utterance} (EoU) in …

RetroGraph: Retrosynthetic Planning with Graph Search

2023 · VBN Forskningsportal (Aalborg Universitet)

The data and checkpoint for "RetroGraph: Retrosynthetic Planning with Graph Search"

YuLan: An Open-source Large Language Model

2024 · arXiv (Cornell University)

Large language models (LLMs) have become the foundation of many applications, leveraging their extensive capabilities in processing and understanding natural language. While many open-source LLMs have been released with technical reports, the lack of training …

2D-TPE: Two-Dimensional Positional Encoding Enhances Table Understanding for Large Language Models

2024 · arXiv (Cornell University)

Tables are ubiquitous across various domains for concisely representing structured information. Empowering large language models (LLMs) to reason over tabular data represents an actively explored direction. However, since typical LLMs only support one-dimensional~(1D) inputs, existing …

Learning to Respond with Deep Neural Networks for Retrieval-Based Human-Computer Conversation System

2016

To establish an automatic conversation system between humans and computers is regarded as one of the most hardcore problems in computer science, which involves interdisciplinary techniques in information retrieval, natural language processing, artificial intelligence, etc. …

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 …

"Chitty-Chitty-Chat Bot": Deep Learning for Conversational AI

2018

Conversational AI is of growing importance since it enables easy interaction interface between humans and computers. Due to its promising potential and alluring commercial values to serve as virtual assistants and/or social chatbots, major AI, …

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 …

Natural Language Inference by Tree-Based Convolution and Heuristic Matching

2016

In this paper, we propose the TBCNNpair model to recognize entailment and contradiction between two sentences. In our model, a tree-based convolutional neural network (TBCNN) captures sentencelevel semantics; then heuristic matching layers like concatenation, element-wise …

Sequence to Backward and Forward Sequences: A Content-Introducing\n Approach to Generative Short-Text Conversation

2016 · arXiv (Cornell University)

Using neural networks to generate replies in human-computer dialogue systems\nis attracting increasing attention over the past few years. However, the\nperformance is not satisfactory: the neural network tends to generate safe,\nuniversally relevant replies which carry little …

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 …

CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling

2019 · Proceedings of the AAAI Conference on Artificial Intelligence

In real-world applications of natural language generation, there are often constraints on the target sentences in addition to fluency and naturalness requirements. Existing language generation techniques are usually based on recurrent neural networks (RNNs). However, …

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 …