Search Results for author: Baolin Peng

Found 56 papers, 18 papers with code

#HowYouTagTweets: Learning User Hashtagging Preferences via Personalized Topic Attention

1 code implementation EMNLP 2021 Yuji Zhang, Yubo Zhang, Chunpu Xu, Jing Li, Ziyan Jiang, Baolin Peng

It is hypothesized that one’s interests in a hashtag are related with what they said before (user history) and the existing posts present the hashtag (hashtag contexts).

Self-Checker: Plug-and-Play Modules for Fact-Checking with Large Language Models

no code implementations24 May 2023 Miaoran Li, Baolin Peng, Zhu Zhang

Fact-checking is an essential task in NLP that is commonly utilized for validating the factual accuracy of claims.

Fact Checking

SGP-TOD: Building Task Bots Effortlessly via Schema-Guided LLM Prompting

no code implementations15 May 2023 Xiaoying Zhang, Baolin Peng, Kun Li, Jingyan Zhou, Helen Meng

Building end-to-end task bots and maintaining their integration with new functionalities using minimal human efforts is a long-standing challenge in dialog research.

dialog state tracking

ArK: Augmented Reality with Knowledge Interactive Emergent Ability

no code implementations1 May 2023 Qiuyuan Huang, Jae Sung Park, Abhinav Gupta, Paul Bennett, Ran Gong, Subhojit Som, Baolin Peng, Owais Khan Mohammed, Chris Pal, Yejin Choi, Jianfeng Gao

In this study, we develop an infinite agent that learns to transfer knowledge memory from general foundation models (e. g. GPT4, DALLE) to novel domains or scenarios for scene understanding and generation in the physical or virtual world.

Mixed Reality Scene Generation +1

Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models

1 code implementation19 Apr 2023 Pan Lu, Baolin Peng, Hao Cheng, Michel Galley, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, Jianfeng Gao

Chameleon synthesizes programs by composing various tools (e. g., LLMs, off-the-shelf vision models, web search engines, Python functions, and heuristic-based modules) for accomplishing complex reasoning tasks.

Logical Reasoning Mathematical Reasoning

Instruction Tuning with GPT-4

1 code implementation6 Apr 2023 Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, Jianfeng Gao

Prior work has shown that finetuning large language models (LLMs) using machine-generated instruction-following data enables such models to achieve remarkable zero-shot capabilities on new tasks, and no human-written instructions are needed.

Instruction Following

Interactive Text Generation

no code implementations2 Mar 2023 Felix Faltings, Michel Galley, Baolin Peng, Kianté Brantley, Weixin Cai, Yizhe Zhang, Jianfeng Gao, Bill Dolan

Unfortunately, this means most of the research on text, code, and image generation has focused on non-interactive settings, whereby the model is expected to get everything right without accounting for any input from a user who may be willing to help.

Image Generation Imitation Learning +1

Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback

no code implementations24 Feb 2023 Baolin Peng, Michel Galley, Pengcheng He, Hao Cheng, Yujia Xie, Yu Hu, Qiuyuan Huang, Lars Liden, Zhou Yu, Weizhu Chen, Jianfeng Gao

Large language models (LLMs), such as ChatGPT, are able to generate human-like, fluent responses for many downstream tasks, e. g., task-oriented dialog and question answering.

Informativeness Open-Domain Question Answering

Guiding Large Language Models via Directional Stimulus Prompting

1 code implementation22 Feb 2023 Zekun Li, Baolin Peng, Pengcheng He, Michel Galley, Jianfeng Gao, Xifeng Yan

We introduce a new framework, Directional Stimulus Prompting, that uses a tuneable language model (LM) to provide guidance for the black-box frozen large language model (LLM) on downstream tasks.

Avg Language Modelling +1

DIONYSUS: A Pre-trained Model for Low-Resource Dialogue Summarization

no code implementations20 Dec 2022 Yu Li, Baolin Peng, Pengcheng He, Michel Galley, Zhou Yu, Jianfeng Gao

In this work, we propose DIONYSUS (dynamic input optimization in pre-training for dialogue summarization), a pre-trained encoder-decoder model for summarizing dialogues in any new domain.

Enhancing Task Bot Engagement with Synthesized Open-Domain Dialog

no code implementations20 Dec 2022 Miaoran Li, Baolin Peng, Michel Galley, Jianfeng Gao, Zhu Zhang

To better mimic human-level conversations that usually fuse various dialog modes, it is essential to build a system that can effectively handle both TOD and ODD and access different knowledge sources.

Open-Domain Dialog

Grounded Keys-to-Text Generation: Towards Factual Open-Ended Generation

no code implementations4 Dec 2022 Faeze Brahman, Baolin Peng, Michel Galley, Sudha Rao, Bill Dolan, Snigdha Chaturvedi, Jianfeng Gao

We propose a new grounded keys-to-text generation task: the task is to generate a factual description about an entity given a set of guiding keys, and grounding passages.

Data-to-Text Generation

Explanations from Large Language Models Make Small Reasoners Better

no code implementations13 Oct 2022 Shiyang Li, Jianshu Chen, Yelong Shen, Zhiyu Chen, Xinlu Zhang, Zekun Li, Hong Wang, Jing Qian, Baolin Peng, Yi Mao, Wenhu Chen, Xifeng Yan

Integrating free-text explanations to in-context learning of large language models (LLM) is shown to elicit strong reasoning capabilities along with reasonable explanations.

Explanation Generation Multi-Task Learning

OPERA: Harmonizing Task-Oriented Dialogs and Information Seeking Experience

no code implementations24 Jun 2022 Miaoran Li, Baolin Peng, Jianfeng Gao, Zhu Zhang

Existing studies in conversational AI mostly treat task-oriented dialog (TOD) and question answering (QA) as separate tasks.

Question Answering

Toward Self-learning End-to-End Task-Oriented Dialog Systems

no code implementations SIGDIAL (ACL) 2022 Xiaoying Zhang, Baolin Peng, Jianfeng Gao, Helen Meng

In this paper, we study the problem of automatically adapting task bots to changing environments by learning from human-bot interactions with minimum or zero human annotations.

reinforcement-learning Reinforcement Learning (RL) +1

Knowledge-Grounded Dialogue Generation with a Unified Knowledge Representation

no code implementations NAACL 2022 Yu Li, Baolin Peng, Yelong Shen, Yi Mao, Lars Liden, Zhou Yu, Jianfeng Gao

To address these challenges, we present PLUG, a language model that homogenizes different knowledge sources to a unified knowledge representation for knowledge-grounded dialogue generation tasks.

Dialogue Generation Language Modelling

ValueNet: A New Dataset for Human Value Driven Dialogue System

no code implementations12 Dec 2021 Liang Qiu, Yizhou Zhao, Jinchao Li, Pan Lu, Baolin Peng, Jianfeng Gao, Song-Chun Zhu

To the best of our knowledge, ValueNet is the first large-scale text dataset for human value modeling, and we are the first one trying to incorporate a value model into emotionally intelligent dialogue systems.

Dialogue Generation Emotion Recognition +2

SYNERGY: Building Task Bots at Scale Using Symbolic Knowledge and Machine Teaching

no code implementations21 Oct 2021 Baolin Peng, Chunyuan Li, Zhu Zhang, Jinchao Li, Chenguang Zhu, Jianfeng Gao

We propose SYNERGY, a hybrid learning framework where a task bot is developed in two steps: (i) Symbolic knowledge to neural networks: Large amounts of simulated dialog sessions are generated based on task-specific symbolic knowledge which is represented as a task schema consisting of dialog flows and task-oriented databases.

SocAoG: Incremental Graph Parsing for Social Relation Inference in Dialogues

no code implementations ACL 2021 Liang Qiu, Yuan Liang, Yizhou Zhao, Pan Lu, Baolin Peng, Zhou Yu, Ying Nian Wu, Song-Chun Zhu

Inferring social relations from dialogues is vital for building emotionally intelligent robots to interpret human language better and act accordingly.

Dialog Relation Extraction

Few-Shot Named Entity Recognition: A Comprehensive Study

2 code implementations29 Dec 2020 Jiaxin Huang, Chunyuan Li, Krishan Subudhi, Damien Jose, Shobana Balakrishnan, Weizhu Chen, Baolin Peng, Jianfeng Gao, Jiawei Han

This paper presents a comprehensive study to efficiently build named entity recognition (NER) systems when a small number of in-domain labeled data is available.

Few-Shot Learning named-entity-recognition +2

RADDLE: An Evaluation Benchmark and Analysis Platform for Robust Task-oriented Dialog Systems

no code implementations ACL 2021 Baolin Peng, Chunyuan Li, Zhu Zhang, Chenguang Zhu, Jinchao Li, Jianfeng Gao

For task-oriented dialog systems to be maximally useful, it must be able to process conversations in a way that is (1) generalizable with a small number of training examples for new task domains, and (2) robust to user input in various styles, modalities or domains.

Robust Conversational AI with Grounded Text Generation

no code implementations7 Sep 2020 Jianfeng Gao, Baolin Peng, Chunyuan Li, Jinchao Li, Shahin Shayandeh, Lars Liden, Heung-Yeung Shum

This article provides an overview of this progress and discusses related methods and technologies that can be incorporated for building robust conversational AI systems.

Text Generation

Conversation Learner - A Machine Teaching Tool for Building Dialog Managers for Task-Oriented Dialog Systems

no code implementations ACL 2020 Swadheen Shukla, Lars Liden, Shay, Shahin eh, Eslam Kamal, Jinchao Li, Matt Mazzola, Thomas Park, Baolin Peng, Jianfeng Gao

Traditionally, industry solutions for building a task-oriented dialog system have relied on helping dialog authors define rule-based dialog managers, represented as dialog flows.

Learning Efficient Dialogue Policy from Demonstrations through Shaping

no code implementations ACL 2020 Huimin Wang, Baolin Peng, Kam-Fai Wong

Training a task-oriented dialogue agent with reinforcement learning is prohibitively expensive since it requires a large volume of interactions with users.

Domain Adaptation

Meta Dialogue Policy Learning

no code implementations3 Jun 2020 Yumo Xu, Chenguang Zhu, Baolin Peng, Michael Zeng

Dialog policy determines the next-step actions for agents and hence is central to a dialogue system.

Meta-Learning Transfer Learning

Is Your Goal-Oriented Dialog Model Performing Really Well? Empirical Analysis of System-wise Evaluation

no code implementations SIGDIAL (ACL) 2020 Ryuichi Takanobu, Qi Zhu, Jinchao Li, Baolin Peng, Jianfeng Gao, Minlie Huang

There is a growing interest in developing goal-oriented dialog systems which serve users in accomplishing complex tasks through multi-turn conversations.

Goal-Oriented Dialog

Conversation Learner -- A Machine Teaching Tool for Building Dialog Managers for Task-Oriented Dialog Systems

no code implementations9 Apr 2020 Swadheen Shukla, Lars Liden, Shahin Shayandeh, Eslam Kamal, Jinchao Li, Matt Mazzola, Thomas Park, Baolin Peng, Jianfeng Gao

Traditionally, industry solutions for building a task-oriented dialog system have relied on helping dialog authors define rule-based dialog managers, represented as dialog flows.

Guided Dialog Policy Learning without Adversarial Learning in the Loop

1 code implementation7 Apr 2020 Ziming Li, Sungjin Lee, Baolin Peng, Jinchao Li, Julia Kiseleva, Maarten de Rijke, Shahin Shayandeh, Jianfeng Gao

Reinforcement Learning (RL) methods have emerged as a popular choice for training an efficient and effective dialogue policy.

Reinforcement Learning (RL)

Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space

1 code implementation EMNLP 2020 Chunyuan Li, Xiang Gao, Yuan Li, Baolin Peng, Xiujun Li, Yizhe Zhang, Jianfeng Gao

We hope that our first pre-trained big VAE language model itself and results can help the NLP community renew the interests of deep generative models in the era of large-scale pre-training, and make these principled methods more practical.

Language Modelling Representation Learning +1

Few-shot Natural Language Generation for Task-Oriented Dialog

1 code implementation Findings of the Association for Computational Linguistics 2020 Baolin Peng, Chenguang Zhu, Chunyuan Li, Xiujun Li, Jinchao Li, Michael Zeng, Jianfeng Gao

It is pre-trained on a large set of annotated NLG corpus to acquire the controllable generation ability, and fine-tuned with only a few domain-specific labels to adapt to new domains.

Data-to-Text Generation Few-Shot Learning

ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems

1 code implementation ACL 2020 Qi Zhu, Zheng Zhang, Yan Fang, Xiang Li, Ryuichi Takanobu, Jinchao Li, Baolin Peng, Jianfeng Gao, Xiaoyan Zhu, Minlie Huang

We present ConvLab-2, an open-source toolkit that enables researchers to build task-oriented dialogue systems with state-of-the-art models, perform an end-to-end evaluation, and diagnose the weakness of systems.

Task-Oriented Dialogue Systems

ConvLab: Multi-Domain End-to-End Dialog System Platform

2 code implementations ACL 2019 Sungjin Lee, Qi Zhu, Ryuichi Takanobu, Xiang Li, Yaoqin Zhang, Zheng Zhang, Jinchao Li, Baolin Peng, Xiujun Li, Minlie Huang, Jianfeng Gao

We present ConvLab, an open-source multi-domain end-to-end dialog system platform, that enables researchers to quickly set up experiments with reusable components and compare a large set of different approaches, ranging from conventional pipeline systems to end-to-end neural models, in common environments.

Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning

3 code implementations ACL 2018 Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, Kam-Fai Wong, Shang-Yu Su

During dialogue policy learning, the world model is constantly updated with real user experience to approach real user behavior, and in turn, the dialogue agent is optimized using both real experience and simulated experience.

Reinforcement Learning (RL) Task-Completion Dialogue Policy Learning

Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning

no code implementations31 Oct 2017 Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, Yun-Nung Chen, Kam-Fai Wong

This paper presents a new method --- adversarial advantage actor-critic (Adversarial A2C), which significantly improves the efficiency of dialogue policy learning in task-completion dialogue systems.

Task-Completion Dialogue Policy Learning

An Attentional Neural Conversation Model with Improved Specificity

no code implementations3 Jun 2016 Kaisheng Yao, Baolin Peng, Geoffrey Zweig, Kam-Fai Wong

Experimental results indicate that the model outperforms previously proposed neural conversation architectures, and that using specificity in the objective function significantly improves performances for both generation and retrieval.

Retrieval Specificity

Word Embedding based Correlation Model for Question/Answer Matching

no code implementations15 Nov 2015 Yikang Shen, Wenge Rong, Nan Jiang, Baolin Peng, Jie Tang, Zhang Xiong

With the development of community based question answering (Q&A) services, a large scale of Q&A archives have been accumulated and are an important information and knowledge resource on the web.

Question Answering Translation

Attention with Intention for a Neural Network Conversation Model

no code implementations29 Oct 2015 Kaisheng Yao, Geoffrey Zweig, Baolin Peng

The intention network is a recurrent network that models the dynamics of the intention process.

Language Modelling

Towards Neural Network-based Reasoning

1 code implementation22 Aug 2015 Baolin Peng, Zhengdong Lu, Hang Li, Kam-Fai Wong

For example, it improves the accuracy on Path Finding(10K) from 33. 4% [6] to over 98%.

Recurrent Neural Networks with External Memory for Language Understanding

no code implementations31 May 2015 Baolin Peng, Kaisheng Yao

Recurrent Neural Networks (RNNs) have become increasingly popular for the task of language understanding.


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