Baolin Peng


2023

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SGP-TOD: Building Task Bots Effortlessly via Schema-Guided LLM Prompting
Xiaoying Zhang | Baolin Peng | Kun Li | Jingyan Zhou | Helen Meng
Findings of the Association for Computational Linguistics: EMNLP 2023

Building and maintaining end-to-end task bots using minimal human effort is a long-standing challenge in dialog research. In this work, we introduce SGP-TOD, Schema-Guided Prompting for building Task-Oriented Dialog systems effortlessly based on large language models (LLMs). Utilizing the predefined task schema, i.e., belief instruction and dialog policy, we instruct fixed LLMs to generate appropriate responses on novel tasks, without the need for training data. Specifically, SGP-TOD comprises three components: an LLM for interacting with users, a Dialog State Tracking (DST) Prompter to aid the LLM in tracking dialog states with the given belief instruction, and a Policy Prompter to direct the LLM to generate proper responses adhering to the provided dialog policy. Experimental results on Multiwoz, RADDLE, and STAR datasets show that our training-free strategy, SGP-TOD, yields state-of-the-art (SOTA) zero-shot performance, significantly surpassing the few-shot approaches. In a domain-extension setting, SGP-TOD aptly adapts to new functionalities by merely adding supplementary schema rules. We make our code and data publicly available.

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Interactive Text Generation
Felix Faltings | Michel Galley | Kianté Brantley | Baolin Peng | Weixin Cai | Yizhe Zhang | Jianfeng Gao | Bill Dolan
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Users interact with text, image, code, or other editors on a daily basis. However, machine learning models are rarely trained in the settings that reflect the interactivity between users and their editor. This is understandable as training AI models with real users is not only slow and costly, but what these models learn may be specific to user interface design choices. 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. We introduce a new Interactive Text Generation task that allows training generation models interactively without the costs of involving real users, by using user simulators that provide edits that guide the model towards a given target text. We train our interactive models using Imitation Learning, and our experiments against competitive non-interactive generation models show that models trained interactively are superior to their non-interactive counterparts, even when all models are given the same budget of user inputs or edits.

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ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data Format
Qi Zhu | Christian Geishauser | Hsien-chin Lin | Carel van Niekerk | Baolin Peng | Zheng Zhang | Shutong Feng | Michael Heck | Nurul Lubis | Dazhen Wan | Xiaochen Zhu | Jianfeng Gao | Milica Gasic | Minlie Huang
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Task-oriented dialogue (TOD) systems function as digital assistants, guiding users through various tasks such as booking flights or finding restaurants. Existing toolkits for building TOD systems often fall short in delivering comprehensive arrays of data, model, and experimental environments with a user-friendly experience. We introduce ConvLab-3: a multifaceted dialogue system toolkit crafted to bridge this gap. Our unified data format simplifies the integration of diverse datasets and models, significantly reducing complexity and cost for studying generalization and transfer. Enhanced with robust reinforcement learning (RL) tools, featuring a streamlined training process, in-depth evaluation tools, and a selection of user simulators, ConvLab-3 supports the rapid development and evaluation of robust dialogue policies. Through an extensive study, we demonstrate the efficacy of transfer learning and RL and showcase that ConvLab-3 is not only a powerful tool for seasoned researchers but also an accessible platform for newcomers.

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Enhancing Task Bot Engagement with Synthesized Open-Domain Dialog
Miaoran Li | Baolin Peng | Michel Galley | Jianfeng Gao | Zhu (Drew) Zhang
Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue

The construction of dialog systems for various types of conversations, such as task-oriented dialog (TOD) and open-domain dialog (ODD), has been an active area of research. In order to more closely mimic human-like conversations that often involve the fusion of different dialog modes, it is important to develop systems that can effectively handle both TOD and ODD and access different knowledge sources. In this work, we present a new automatic framework to enrich TODs with synthesized ODDs. We also introduce the PivotBot model, which is capable of handling both TOD and ODD modes and can access different knowledge sources to generate informative responses. Evaluation results indicate the superior ability of the proposed model to switch smoothly between TOD and ODD tasks.

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DIONYSUS: A Pre-trained Model for Low-Resource Dialogue Summarization
Yu Li | Baolin Peng | Pengcheng He | Michel Galley | Zhou Yu | Jianfeng Gao
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Dialogue summarization has recently garnered significant attention due to its wide range of applications. However, existing methods for summarizing dialogues have limitations because they do not take into account the inherent structure of dialogue and rely heavily on labeled data, which can lead to poor performance in new domains. 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. To pre-train DIONYSUS, we create two pseudo summaries for each dialogue example: one from a fine-tuned summarization model and the other from important dialogue turns. We then choose one of these pseudo summaries based on information distribution differences in different types of dialogues. This selected pseudo summary serves as the objective for pre-training DIONYSUS using a self-supervised approach on a large dialogue corpus. Our experiments show that DIONYSUS outperforms existing methods on six datasets, as demonstrated by its ROUGE scores in zero-shot and few-shot settings

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Z-Code++: A Pre-trained Language Model Optimized for Abstractive Summarization
Pengcheng He | Baolin Peng | Song Wang | Yang Liu | Ruochen Xu | Hany Hassan | Yu Shi | Chenguang Zhu | Wayne Xiong | Michael Zeng | Jianfeng Gao | Xuedong Huang
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

This paper presents Z-Code++, a new pre-trained language model optimized for abstractive text summarization. The model extends the state-of-the-art encoder-decoder model using three techniques. First, we use a two-phase pre-training to improve the model’s performance on low-resource summarization tasks. The model is first pre-trained using text corpora for language understanding, then is continually pre-trained on summarization corpora for grounded text generation. Second, we replace self-attention layers in the encoder with disentangled attention layers, where each word is represented using two vectors that encode its content and position, respectively. Third, we use fusion-in-encoder, a simple yet effective method of encoding long sequences in a hierarchical manner. Z-Code++ createsa new state-of-the-art on 9 of 13 text summarization tasks across 5 languages. Our model is parameter-efficient in that it outperforms the 600x larger PaLM540B on XSum, and the finetuned 200x larger GPT3175B on SAMSum. In zero-shot and few-shot settings, our model substantially outperforms the competing models.

2022

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Grounded Keys-to-Text Generation: Towards Factual Open-Ended Generation
Faeze Brahman | Baolin Peng | Michel Galley | Sudha Rao | Bill Dolan | Snigdha Chaturvedi | Jianfeng Gao
Findings of the Association for Computational Linguistics: EMNLP 2022

Large pre-trained language models have recently enabled open-ended generation frameworks (e.g., prompt-to-text NLG) to tackle a variety of tasks going beyond the traditional data-to-text generation. While this framework is more general, it is under-specified and often leads to a lack of controllability restricting their real-world usage. 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. To address this task, we introduce a new dataset, called EntDeGen. Inspired by recent QA-based evaluation measures, we propose an automatic metric, MAFE, for factual correctness of generated descriptions. Our EntDescriptor model is equipped with strong rankers to fetch helpful passages and generate entity descriptions. Experimental result shows a good correlation (60.14) between our proposed metric and human judgments of factuality. Our rankers significantly improved the factual correctness of generated descriptions (15.95% and 34.51% relative gains in recall and precision). Finally, our ablation study highlights the benefit of combining keys and groundings.

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Toward Self-Learning End-to-End Task-oriented Dialog Systems
Xiaoying Zhang | Baolin Peng | Jianfeng Gao | Helen Meng
Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue

End-to-end task bots are typically learned over a static and usually limited-size corpus. However, when deployed in dynamic, changing, and open environments to interact with users, task bots tend to fail when confronted with data that deviate from the training corpus, i.e., out-of-distribution samples. 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. We propose SL-Agent, a novel self-learning framework for building end-to-end task bots. SL-Agent consists of a dialog model and a pre-trained reward model to predict the quality of an agent response. It enables task bots to automatically adapt to changing environments by learning from the unlabeled human-bot dialog logs accumulated after deployment via reinforcement learning with the incorporated reward model. Experimental results on four well-studied dialog tasks show the effectiveness of SL-Agent to automatically adapt to changing environments, using both automatic and human evaluations. We will release code and data for further research.

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Knowledge-Grounded Dialogue Generation with a Unified Knowledge Representation
Yu Li | Baolin Peng | Yelong Shen | Yi Mao | Lars Liden | Zhou Yu | Jianfeng Gao
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Knowledge-grounded dialogue systems are challenging to build due to the lack of training data and heterogeneous knowledge sources. Existing systems perform poorly on unseen topics due to limited topics covered in the training data. In addition, it is challenging to generalize to the domains that require different types of knowledge sources. To address the above challenges, we present PLUG, a language model that homogenizes different knowledge sources to a unified knowledge representation for knowledge-grounded dialogue generation tasks. We first retrieve relevant information from heterogeneous knowledge sources (e.g., wiki, dictionary, or knowledge graph); Then the retrieved knowledge is transformed into text and concatenated with dialogue history to feed into the language model for generating responses. PLUG is pre-trained on a large-scale knowledge-grounded dialogue corpus. The empirical evaluation on two benchmarks shows that PLUG generalizes well across different knowledge-grounded dialogue tasks. It achieves comparable performance with state-of-the-art methods in the fully-supervised setting and significantly outperforms other approaches in zero-shot and few-shot settings.

2021

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#HowYouTagTweets: Learning User Hashtagging Preferences via Personalized Topic Attention
Yuji Zhang | Yubo Zhang | Chunpu Xu | Jing Li | Ziyan Jiang | Baolin Peng
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Millions of hashtags are created on social media every day to cross-refer messages concerning similar topics. To help people find the topics they want to discuss, this paper characterizes a user’s hashtagging preferences via predicting how likely they will post with a hashtag. 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). These factors are married in the deep semantic space built with a pre-trained BERT and a neural topic model via multitask learning. In this way, user interests learned from the past can be customized to match future hashtags, which is beyond the capability of existing methods assuming unchanged hashtag semantics. Furthermore, we propose a novel personalized topic attention to capture salient contents to personalize hashtag contexts. Experiments on a large-scale Twitter dataset show that our model significantly outperforms the state-of-the-art recommendation approach without exploiting latent topics.

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Few-Shot Named Entity Recognition: An Empirical Baseline Study
Jiaxin Huang | Chunyuan Li | Krishan Subudhi | Damien Jose | Shobana Balakrishnan | Weizhu Chen | Baolin Peng | Jianfeng Gao | Jiawei Han
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

This paper presents an empirical study to efficiently build named entity recognition (NER) systems when a small amount of in-domain labeled data is available. Based upon recent Transformer-based self-supervised pre-trained language models (PLMs), we investigate three orthogonal schemes to improve model generalization ability in few-shot settings: (1) meta-learning to construct prototypes for different entity types, (2) task-specific supervised pre-training on noisy web data to extract entity-related representations and (3) self-training to leverage unlabeled in-domain data. On 10 public NER datasets, we perform extensive empirical comparisons over the proposed schemes and their combinations with various proportions of labeled data, our experiments show that (i)in the few-shot learning setting, the proposed NER schemes significantly improve or outperform the commonly used baseline, a PLM-based linear classifier fine-tuned using domain labels. (ii) We create new state-of-the-art results on both few-shot and training-free settings compared with existing methods.

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Engage the Public: Poll Question Generation for Social Media Posts
Zexin Lu | Keyang Ding | Yuji Zhang | Jing Li | Baolin Peng | Lemao Liu
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

This paper presents a novel task to generate poll questions for social media posts. It offers an easy way to hear the voice from the public and learn from their feelings to important social topics. While most related work tackles formal languages (e.g., exam papers), we generate poll questions for short and colloquial social media messages exhibiting severe data sparsity. To deal with that, we propose to encode user comments and discover latent topics therein as contexts. They are then incorporated into a sequence-to-sequence (S2S) architecture for question generation and its extension with dual decoders to additionally yield poll choices (answers). For experiments, we collect a large-scale Chinese dataset from Sina Weibo containing over 20K polls. The results show that our model outperforms the popular S2S models without exploiting topics from comments and the dual decoder design can further benefit the prediction of both questions and answers. Human evaluations further exhibit our superiority in yielding high-quality polls helpful to draw user engagements.

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SocAoG: Incremental Graph Parsing for Social Relation Inference in Dialogues
Liang Qiu | Yuan Liang | Yizhou Zhao | Pan Lu | Baolin Peng | Zhou Yu | Ying Nian Wu | Song-Chun Zhu
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Inferring social relations from dialogues is vital for building emotionally intelligent robots to interpret human language better and act accordingly. We model the social network as an And-or Graph, named SocAoG, for the consistency of relations among a group and leveraging attributes as inference cues. Moreover, we formulate a sequential structure prediction task, and propose an 𝛼-𝛽-𝛾 strategy to incrementally parse SocAoG for the dynamic inference upon any incoming utterance: (i) an 𝛼 process predicting attributes and relations conditioned on the semantics of dialogues, (ii) a 𝛽 process updating the social relations based on related attributes, and (iii) a 𝛾 process updating individual’s attributes based on interpersonal social relations. Empirical results on DialogRE and MovieGraph show that our model infers social relations more accurately than the state-of-the-art methods. Moreover, the ablation study shows the three processes complement each other, and the case study demonstrates the dynamic relational inference.

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RADDLE: An Evaluation Benchmark and Analysis Platform for Robust Task-oriented Dialog Systems
Baolin Peng | Chunyuan Li | Zhu Zhang | Chenguang Zhu | Jinchao Li | Jianfeng Gao
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

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. In pursuit of these goals, we introduce the RADDLE benchmark, a collection of corpora and tools for evaluating the performance of models across a diverse set of domains. By including tasks with limited training data, RADDLE is designed to favor and encourage models with a strong generalization ability. RADDLE also includes a diagnostic checklist that facilitates detailed robustness analysis in aspects such as language variations, speech errors, unseen entities, and out-of-domain utterances. We evaluate recent state-of-the-art systems based on pre-training and fine-tuning, and find that grounded pre-training on heterogeneous dialog corpora performs better than training a separate model per domain. Adversarial training is also proposed to improve model robustness against noisy inputs. Overall, existing models are less than satisfactory in robustness evaluation, which suggests opportunities for future improvement.

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Soloist: Building Task Bots at Scale with Transfer Learning and Machine Teaching
Baolin Peng | Chunyuan Li | Jinchao Li | Shahin Shayandeh | Lars Liden | Jianfeng Gao
Transactions of the Association for Computational Linguistics, Volume 9

We present a new method, Soloist,1 that uses transfer learning and machine teaching to build task bots at scale. We parameterize classical modular task-oriented dialog systems using a Transformer-based auto-regressive language model, which subsumes different dialog modules into a single neural model. We pre-train, on heterogeneous dialog corpora, a task-grounded response generation model, which can generate dialog responses grounded in user goals and real-world knowledge for task completion. The pre-trained model can be efficiently adapted to accomplish new tasks with a handful of task-specific dialogs via machine teaching, where training samples are generated by human teachers interacting with the system. Experiments show that (i)Soloist creates new state-of-the-art on well-studied task-oriented dialog benchmarks, including CamRest676 and MultiWOZ; (ii) in the few-shot fine-tuning settings, Soloist significantly outperforms existing methods; and (iii) the use of machine teaching substantially reduces the labeling cost of fine-tuning. The pre-trained models and codes are available at https://aka.ms/soloist.

2020

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Is Your Goal-Oriented Dialog Model Performing Really Well? Empirical Analysis of System-wise Evaluation
Ryuichi Takanobu | Qi Zhu | Jinchao Li | Baolin Peng | Jianfeng Gao | Minlie Huang
Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue

There is a growing interest in developing goal-oriented dialog systems which serve users in accomplishing complex tasks through multi-turn conversations. Although many methods are devised to evaluate and improve the performance of individual dialog components, there is a lack of comprehensive empirical study on how different components contribute to the overall performance of a dialog system. In this paper, we perform a system-wise evaluation and present an empirical analysis on different types of dialog systems which are composed of different modules in different settings. Our results show that (1) a pipeline dialog system trained using fine-grained supervision signals at different component levels often obtains better performance than the systems that use joint or end-to-end models trained on coarse-grained labels, (2) component-wise, single-turn evaluation results are not always consistent with the overall performance of a dialog system, and (3) despite the discrepancy between simulators and human users, simulated evaluation is still a valid alternative to the costly human evaluation especially in the early stage of development.

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Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space
Chunyuan Li | Xiang Gao | Yuan Li | Baolin Peng | Xiujun Li | Yizhe Zhang | Jianfeng Gao
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

When trained effectively, the Variational Autoencoder (VAE) can be both a powerful generative model and an effective representation learning framework for natural language. In this paper, we propose the first large-scale language VAE model Optimus (Organizing sentences via Pre-Trained Modeling of a Universal Space). A universal latent embedding space for sentences is first pre-trained on large text corpus, and then fine-tuned for various language generation and understanding tasks. Compared with GPT-2, Optimus enables guided language generation from an abstract level using the latent vectors. Compared with BERT, Optimus can generalize better on low-resource language understanding tasks due to the smooth latent space structure. Extensive experimental results on a wide range of language tasks demonstrate the effectiveness of Optimus. It achieves new state-of-the-art on VAE language modeling benchmarks.

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Few-shot Natural Language Generation for Task-Oriented Dialog
Baolin Peng | Chenguang Zhu | Chunyuan Li | Xiujun Li | Jinchao Li | Michael Zeng | Jianfeng Gao
Findings of the Association for Computational Linguistics: EMNLP 2020

As a crucial component in task-oriented dialog systems, the Natural Language Generation (NLG) module converts a dialog act represented in a semantic form into a response in natural language. The success of traditional template-based or statistical models typically relies on heavily annotated data, which is infeasible for new domains. Therefore, it is pivotal for an NLG system to generalize well with limited labelled data in real applications. To this end, we present FewshotWOZ, the first NLG benchmark to simulate the few-shot learning setting in task-oriented dialog systems. Further, we develop the SC-GPT model. 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. Experiments on FewshotWOZ and the large Multi-Domain-WOZ datasets show that the proposed SC-GPT significantly outperforms existing methods, measured by various automatic metrics and human evaluations.

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Guided Dialogue Policy Learning without Adversarial Learning in the Loop
Ziming Li | Sungjin Lee | Baolin Peng | Jinchao Li | Julia Kiseleva | Maarten de Rijke | Shahin Shayandeh | Jianfeng Gao
Findings of the Association for Computational Linguistics: EMNLP 2020

Reinforcement learning methods have emerged as a popular choice for training an efficient and effective dialogue policy. However, these methods suffer from sparse and unstable reward signals returned by a user simulator only when a dialogue finishes. Besides, the reward signal is manually designed by human experts, which requires domain knowledge. Recently, a number of adversarial learning methods have been proposed to learn the reward function together with the dialogue policy. However, to alternatively update the dialogue policy and the reward model on the fly, we are limited to policy-gradient-based algorithms, such as REINFORCE and PPO. Moreover, the alternating training of a dialogue agent and the reward model can easily get stuck in local optima or result in mode collapse. To overcome the listed issues, we propose to decompose the adversarial training into two steps. First, we train the discriminator with an auxiliary dialogue generator and then incorporate a derived reward model into a common reinforcement learning method to guide the dialogue policy learning. This approach is applicable to both on-policy and off-policy reinforcement learning methods. Based on our extensive experimentation, we can conclude the proposed method: (1) achieves a remarkable task success rate using both on-policy and off-policy reinforcement learning methods; and (2) has potential to transfer knowledge from existing domains to a new domain.

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Learning Efficient Dialogue Policy from Demonstrations through Shaping
Huimin Wang | Baolin Peng | Kam-Fai Wong
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Training a task-oriented dialogue agent with reinforcement learning is prohibitively expensive since it requires a large volume of interactions with users. Human demonstrations can be used to accelerate learning progress. However, how to effectively leverage demonstrations to learn dialogue policy remains less explored. In this paper, we present Sˆ2Agent that efficiently learns dialogue policy from demonstrations through policy shaping and reward shaping. We use an imitation model to distill knowledge from demonstrations, based on which policy shaping estimates feedback on how the agent should act in policy space. Reward shaping is then incorporated to bonus state-actions similar to demonstrations explicitly in value space encouraging better exploration. The effectiveness of the proposed Sˆ2Agentt is demonstrated in three dialogue domains and a challenging domain adaptation task with both user simulator evaluation and human evaluation.

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ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems
Qi Zhu | Zheng Zhang | Yan Fang | Xiang Li | Ryuichi Takanobu | Jinchao Li | Baolin Peng | Jianfeng Gao | Xiaoyan Zhu | Minlie Huang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

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. As the successor of ConvLab, ConvLab-2 inherits ConvLab’s framework but integrates more powerful dialogue models and supports more datasets. Besides, we have developed an analysis tool and an interactive tool to assist researchers in diagnosing dialogue systems. The analysis tool presents rich statistics and summarizes common mistakes from simulated dialogues, which facilitates error analysis and system improvement. The interactive tool provides an user interface that allows developers to diagnose an assembled dialogue system by interacting with the system and modifying the output of each system component.

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Conversation Learner - A Machine Teaching Tool for Building Dialog Managers for Task-Oriented Dialog Systems
Swadheen Shukla | Lars Liden | Shahin Shayandeh | Eslam Kamal | Jinchao Li | Matt Mazzola | Thomas Park | Baolin Peng | Jianfeng Gao
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

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. While dialog flows are intuitively interpretable and good for simple scenarios, they fall short of performance in terms of the flexibility needed to handle complex dialogs. On the other hand, purely machine-learned models can handle complex dialogs, but they are considered to be black boxes and require large amounts of training data. In this demonstration, we showcase Conversation Learner, a machine teaching tool for building dialog managers. It combines the best of both approaches by enabling dialog authors to create a dialog flow using familiar tools, converting the dialog flow into a parametric model (e.g., neural networks), and allowing dialog authors to improve the dialog manager (i.e., the parametric model) over time by leveraging user-system dialog logs as training data through a machine teaching interface.

2019

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ConvLab: Multi-Domain End-to-End Dialog System Platform
Sungjin Lee | Qi Zhu | Ryuichi Takanobu | Zheng Zhang | Yaoqin Zhang | Xiang Li | Jinchao Li | Baolin Peng | Xiujun Li | Minlie Huang | Jianfeng Gao
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

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. ConvLab offers a set of fully annotated datasets and associated pre-trained reference models. As a showcase, we extend the MultiWOZ dataset with user dialog act annotations to train all component models and demonstrate how ConvLab makes it easy and effortless to conduct complicated experiments in multi-domain end-to-end dialog settings.

2018

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Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning
Baolin Peng | Xiujun Li | Jianfeng Gao | Jingjing Liu | Kam-Fai Wong
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Training a task-completion dialogue agent via reinforcement learning (RL) is costly because it requires many interactions with real users. One common alternative is to use a user simulator. However, a user simulator usually lacks the language complexity of human interlocutors and the biases in its design may tend to degrade the agent. To address these issues, we present Deep Dyna-Q, which to our knowledge is the first deep RL framework that integrates planning for task-completion dialogue policy learning. We incorporate into the dialogue agent a model of the environment, referred to as the world model, to mimic real user response and generate simulated experience. 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. The effectiveness of our approach is demonstrated on a movie-ticket booking task in both simulated and human-in-the-loop settings.

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Task-oriented Dialogue System for Automatic Diagnosis
Zhongyu Wei | Qianlong Liu | Baolin Peng | Huaixiao Tou | Ting Chen | Xuanjing Huang | Kam-fai Wong | Xiangying Dai
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

In this paper, we make a move to build a dialogue system for automatic diagnosis. We first build a dataset collected from an online medical forum by extracting symptoms from both patients’ self-reports and conversational data between patients and doctors. Then we propose a task-oriented dialogue system framework to make diagnosis for patients automatically, which can converse with patients to collect additional symptoms beyond their self-reports. Experimental results on our dataset show that additional symptoms extracted from conversation can greatly improve the accuracy for disease identification and our dialogue system is able to collect these symptoms automatically and make a better diagnosis.

2017

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Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning
Baolin Peng | Xiujun Li | Lihong Li | Jianfeng Gao | Asli Celikyilmaz | Sungjin Lee | Kam-Fai Wong
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Building a dialogue agent to fulfill complex tasks, such as travel planning, is challenging because the agent has to learn to collectively complete multiple subtasks. For example, the agent needs to reserve a hotel and book a flight so that there leaves enough time for commute between arrival and hotel check-in. This paper addresses this challenge by formulating the task in the mathematical framework of options over Markov Decision Processes (MDPs), and proposing a hierarchical deep reinforcement learning approach to learning a dialogue manager that operates at different temporal scales. The dialogue manager consists of: (1) a top-level dialogue policy that selects among subtasks or options, (2) a low-level dialogue policy that selects primitive actions to complete the subtask given by the top-level policy, and (3) a global state tracker that helps ensure all cross-subtask constraints be satisfied. Experiments on a travel planning task with simulated and real users show that our approach leads to significant improvements over three baselines, two based on handcrafted rules and the other based on flat deep reinforcement learning.

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May I take your order? A Neural Model for Extracting Structured Information from Conversations
Baolin Peng | Michael Seltzer | Y.C. Ju | Geoffrey Zweig | Kam-Fai Wong
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

In this paper we tackle a unique and important problem of extracting a structured order from the conversation a customer has with an order taker at a restaurant. This is motivated by an actual system under development to assist in the order taking process. We develop a sequence-to-sequence model that is able to map from unstructured conversational input to the structured form that is conveyed to the kitchen and appears on the customer receipt. This problem is critically different from other tasks like machine translation where sequence-to-sequence models have been used: the input includes two sides of a conversation; the output is highly structured; and logical manipulations must be performed, for example when the customer changes his mind while ordering. We present a novel sequence-to-sequence model that incorporates a special attention-memory gating mechanism and conversational role markers. The proposed model improves performance over both a phrase-based machine translation approach and a standard sequence-to-sequence model.

2016

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ACE: Automatic Colloquialism, Typographical and Orthographic Errors Detection for Chinese Language
Shichao Dong | Gabriel Pui Cheong Fung | Binyang Li | Baolin Peng | Ming Liao | Jia Zhu | Kam-fai Wong
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations

We present a system called ACE for Automatic Colloquialism and Errors detection for written Chinese. ACE is based on the combination of N-gram model and rule-base model. Although it focuses on detecting colloquial Cantonese (a dialect of Chinese) at the current stage, it can be extended to detect other dialects. We chose Cantonese becauase it has many interesting properties, such as unique grammar system and huge colloquial terms, that turn the detection task extremely challenging. We conducted experiments using real data and synthetic data. The results indicated that ACE is highly reliable and effective.

2015

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Using Content-level Structures for Summarizing Microblog Repost Trees
Jing Li | Wei Gao | Zhongyu Wei | Baolin Peng | Kam-Fai Wong
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing