23 citations · 179 across the 26 of their papers we have counts for
23 papers · 1 filter
Exploring Format Consistency for Instruction Tuning
Shihao Liang, Runchu Tian, Kunlun Zhu +6
Instruction tuning has emerged as a promising approach to enhancing large language models in following human instructions. It is shown that increasing the diversity and number of i…
Learning to Break the Loop: Analyzing and Mitigating Repetitions for Neural Text Generation
Jin Xu, Xiaojiang Liu, Jianhao Yan +3
While large-scale neural language models, such as GPT2 and BART, have achieved impressive results on various text generation tasks, they tend to get stuck in undesirable sentence-l…
Dialogue Generation on Infrequent Sentence Functions via Structured Meta-Learning
Yifan Gao, Piji Li, Wei Bi +3
Sentence function is an important linguistic feature indicating the communicative purpose in uttering a sentence. Incorporating sentence functions into conversations has shown impr…
Profile Consistency Identification for Open-domain Dialogue Agents
Haoyu Song, Yan Wang, Wei-Nan Zhang +3
Maintaining a consistent attribute profile is crucial for dialogue agents to naturally converse with humans. Existing studies on improving attribute consistency mainly explored how…
Enhancing Dialogue Generation via Multi-Level Contrastive Learning
Xin Li, Piji Li, Yan Wang +2
Most of the existing works for dialogue generation are data-driven models trained directly on corpora crawled from websites. They mainly focus on improving the model architecture t…
Response-Anticipated Memory for On-Demand Knowledge Integration in Response Generation
Zhiliang Tian, Wei Bi, Dongkyu Lee +4
Neural conversation models are known to generate appropriate but non-informative responses in general. A scenario where informativeness can be significantly enhanced is Conversing…
Generate, Delete and Rewrite: A Three-Stage Framework for Improving Persona Consistency of Dialogue Generation
Haoyu Song, Yan Wang, Wei-Nan Zhang +2
Maintaining a consistent personality in conversations is quite natural for human beings, but is still a non-trivial task for machines. The persona-based dialogue generation task is…
Prototype-to-Style: Dialogue Generation with Style-Aware Editing on Retrieval Memory
Yixuan Su, Yan Wang, Simon Baker +4
The ability of a dialog system to express prespecified language style during conversations has a direct, positive impact on its usability and on user satisfaction. We introduce a n…
Stylistic Dialogue Generation via Information-Guided Reinforcement Learning Strategy
Yixuan Su, Deng Cai, Yan Wang +4
Stylistic response generation is crucial for building an engaging dialogue system for industrial use. While it has attracted much research interest, existing methods often generate…
SongNet: Rigid Formats Controlled Text Generation
Piji Li, Haisong Zhang, Xiaojiang Liu +1
Neural text generation has made tremendous progress in various tasks. One common characteristic of most of the tasks is that the texts are not restricted to some rigid formats when…
The World is Not Binary: Learning to Rank with Grayscale Data for Dialogue Response Selection
Zibo Lin, Deng Cai, Yan Wang +3
Response selection plays a vital role in building retrieval-based conversation systems. Despite that response selection is naturally a learning-to-rank problem, most prior works ta…
Learning to Select Bi-Aspect Information for Document-Scale Text Content Manipulation
Xiaocheng Feng, Yawei Sun, Bing Qin +5
In this paper, we focus on a new practical task, document-scale text content manipulation, which is the opposite of text style transfer and aims to preserve text styles while alter…
Improving Knowledge-aware Dialogue Generation via Knowledge Base Question Answering
Jian Wang, Junhao Liu, Wei Bi +4
Neural network models usually suffer from the challenge of incorporating commonsense knowledge into the open-domain dialogue systems. In this paper, we propose a novel knowledge-aw…
Relevance-Promoting Language Model for Short-Text Conversation
Xin Li, Piji Li, Wei Bi +2
Despite the effectiveness of sequence-to-sequence framework on the task of Short-Text Conversation (STC), the issue of under-exploitation of training data (i.e., the supervision si…
A Discrete CVAE for Response Generation on Short-Text Conversation
Jun Gao, Wei Bi, Xiaojiang Liu +3
Neural conversation models such as encoder-decoder models are easy to generate bland and generic responses. Some researchers propose to use the conditional variational autoencoder(…
Subword ELMo
Jiangtong Li, Hai Zhao, Zuchao Li +2
Embedding from Language Models (ELMo) has shown to be effective for improving many natural language processing (NLP) tasks, and ELMo takes character information to compose word rep…
Fine-Grained Sentence Functions for Short-Text Conversation
Wei Bi, Jun Gao, Xiaojiang Liu +1
Sentence function is an important linguistic feature referring to a user's purpose in uttering a specific sentence. The use of sentence function has shown promising results to impr…
Translating a Math Word Problem to an Expression Tree
Lei Wang, Yan Wang, Deng Cai +2
Sequence-to-sequence (SEQ2SEQ) models have been successfully applied to automatic math word problem solving. Despite its simplicity, a drawback still remains: a math word problem c…
Generating Multiple Diverse Responses for Short-Text Conversation
Jun Gao, Wei Bi, Xiaojiang Liu +2
Neural generative models have become popular and achieved promising performance on short-text conversation tasks. They are generally trained to build a 1-to-1 mapping from the inpu…
Skeleton-to-Response: Dialogue Generation Guided by Retrieval Memory
Deng Cai, Yan Wang, Victoria Bi +4
For dialogue response generation, traditional generative models generate responses solely from input queries. Such models rely on insufficient information for generating a specific…
Language Style Transfer from Sentences with Arbitrary Unknown Styles
Yanpeng Zhao, Wei Bi, Deng Cai +3
Language style transfer is the problem of migrating the content of a source sentence to a target style. In many of its applications, parallel training data are not available and so…
Automatic Article Commenting: the Task and Dataset
Lianhui Qin, Lemao Liu, Victoria Bi +5
Comments of online articles provide extended views and improve user engagement. Automatically making comments thus become a valuable functionality for online forums, intelligent ch…
Generative Stock Question Answering
Zhaopeng Tu, Yong Jiang, Xiaojiang Liu +2
We study the problem of stock related question answering (StockQA): automatically generating answers to stock related questions, just like professional stock analysts providing act…