Lizi Liao


2024

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Actively Learn from LLMs with Uncertainty Propagation for Generalized Category Discovery
Jinggui Liang | Lizi Liao | Hao Fei | Bobo Li | Jing Jiang
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Generalized category discovery faces a key issue: the lack of supervision for new and unseen data categories. Traditional methods typically combine supervised pretraining with self-supervised learning to create models, and then employ clustering for category identification. However, these approaches tend to become overly tailored to known categories, failing to fully resolve the core issue. Hence, we propose to integrate the feedback from LLMs into an active learning paradigm. Specifically, our method innovatively employs uncertainty propagation to select data samples from high-uncertainty regions, which are then labeled using LLMs through a comparison-based prompting scheme. This not only eases the labeling task but also enhances accuracy in identifying new categories. Additionally, a soft feedback propagation mechanism is introduced to minimize the spread of inaccurate feedback. Experiments on various datasets demonstrate our framework’s efficacy and generalizability, significantly improving baseline models at a nominal average cost.

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Mix-Initiative Response Generation with Dynamic Prefix Tuning
Yuxiang Nie | Heyan Huang | Xian-Ling Mao | Lizi Liao
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Mixed initiative serves as one of the key factors in controlling conversation directions. For a speaker, responding passively or leading proactively would result in rather different responses. However, most dialogue systems focus on training a holistic response generation model without any distinction among different initiatives. It leads to the cross-contamination problem, where the model confuses different initiatives and generates inappropriate responses. Moreover, obtaining plenty of human annotations for initiative labels can be expensive. To address this issue, we propose a general mix-Initiative Dynamic Prefix Tuning framework (IDPT) to decouple different initiatives from the generation model, which learns initiative-aware prefixes in both supervised and unsupervised settings. Specifically, IDPT decouples initiative factors into different prefix parameters and uses the attention mechanism to adjust the selection of initiatives in guiding generation dynamically. The prefix parameters can be tuned towards accurate initiative prediction as well as mix-initiative response generation. Extensive experiments on two public dialogue datasets show that the proposed IDPT outperforms previous baselines on both automatic metrics and human evaluations. It also manages to generate appropriate responses with manipulated initiatives.

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SGSH: Stimulate Large Language Models with Skeleton Heuristics for Knowledge Base Question Generation
Shasha Guo | Lizi Liao | Jing Zhang | Yanling Wang | Cuiping Li | Hong Chen
Findings of the Association for Computational Linguistics: NAACL 2024

Knowledge base question generation (KBQG) aims to generate natural language questions from a set of triplet facts extracted from KB. Existing methods have significantly boosted the performance of KBQG via pre-trained language models (PLMs) thanks to the richly endowed semantic knowledge. With the advance of pre-training techniques, large language models (LLMs) (e.g., GPT-3.5) undoubtedly possess much more semantic knowledge. Therefore, how to effectively organize and exploit the abundant knowledge for KBQG becomes the focus of our study. In this work, we propose SGSH — a simple and effective framework to Stimulate GPT-3.5 with Skeleton Heuristics to enhance KBQG. The framework incorporates “skeleton heuristics”, which provides more fine-grained guidance associated with each input to stimulate LLMs to generate optimal questions, encompassing essential elements like the question phrase and the auxiliary verb.More specifically, we devise an automatic data construction strategy leveraging ChatGPT to construct a skeleton training dataset, based on which we employ a soft prompting approach to train a BART model dedicated to generating the skeleton associated with each input.Subsequently, skeleton heuristics are encoded into the prompt to incentivize GPT-3.5 to generate desired questions. Extensive experiments demonstrate that SGSH derives the new state-of-the-art performance on the KBQG tasks.

2023

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DiaASQ: A Benchmark of Conversational Aspect-based Sentiment Quadruple Analysis
Bobo Li | Hao Fei | Fei Li | Yuhan Wu | Jinsong Zhang | Shengqiong Wu | Jingye Li | Yijiang Liu | Lizi Liao | Tat-Seng Chua | Donghong Ji
Findings of the Association for Computational Linguistics: ACL 2023

The rapid development of aspect-based sentiment analysis (ABSA) within recent decades shows great potential for real-world society. The current ABSA works, however, are mostly limited to the scenario of a single text piece, leaving the study in dialogue contexts unexplored. To bridge the gap between fine-grained sentiment analysis and conversational opinion mining, in this work, we introduce a novel task of conversational aspect-based sentiment quadruple analysis, namely DiaASQ, aiming to detect the quadruple of target-aspect-opinion-sentiment in a dialogue. We manually construct a large-scale high-quality DiaASQ dataset in both Chinese and English languages. We deliberately develop a neural model to benchmark the task, which advances in effectively performing end-to-end quadruple prediction, and manages to incorporate rich dialogue-specific and discourse feature representations for better cross-utterance quadruple extraction. We hope the new benchmark will spur more advancements in the sentiment analysis community.

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ClusterPrompt: Cluster Semantic Enhanced Prompt Learning for New Intent Discovery
Jinggui Liang | Lizi Liao
Findings of the Association for Computational Linguistics: EMNLP 2023

The discovery of new intent categories from user utterances is a crucial task in expanding agent skills. The key lies in how to efficiently solicit semantic evidence from utterances and properly transfer knowledge from existing intents to new intents. However, previous methods laid too much emphasis on relations among utterances or clusters for transfer learning, while paying less attention to the usage of semantics. As a result, these methods suffer from in-domain over-fitting and often generate meaningless new intent clusters due to data distortion. In this paper, we present a novel approach called Cluster Semantic Enhanced Prompt Learning (CsePL) for discovering new intents. Our method leverages two-level contrastive learning with label semantic alignment to learn meaningful representations of intent clusters. These learned intent representations are then utilized as soft prompt initializations for discriminating new intents, reducing the dominance of existing intents. Extensive experiments conducted on three public datasets demonstrate the superiority of our proposed method. It not only outperforms existing methods but also suggests meaningful intent labels and enables early detection of new intents.

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Prompting and Evaluating Large Language Models for Proactive Dialogues: Clarification, Target-guided, and Non-collaboration
Yang Deng | Lizi Liao | Liang Chen | Hongru Wang | Wenqiang Lei | Tat-Seng Chua
Findings of the Association for Computational Linguistics: EMNLP 2023

Conversational systems based on Large Language Models (LLMs), such as ChatGPT, show exceptional proficiency in context understanding and response generation. However, they still possess limitations, such as failing to ask clarifying questions to ambiguous queries or refuse users’ unreasonable requests, both of which are considered as key aspects of a conversational agent’s proactivity. This raises the question of whether LLM-based conversational systems are equipped to handle proactive dialogue problems. In this work, we conduct a comprehensive analysis of LLM-based conversational systems, specifically focusing on three key aspects of proactive dialogues: clarification, target-guided, and non-collaborative dialogues. To trigger the proactivity of LLMs, we propose the Proactive Chain-of-Thought prompting scheme, which augments LLMs with the goal planning capability over descriptive reasoning chains. Empirical findings are discussed to promote future studies on LLM-based proactive dialogue systems.

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End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions
Libo Qin | Wenbo Pan | Qiguang Chen | Lizi Liao | Zhou Yu | Yue Zhang | Wanxiang Che | Min Li
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

End-to-end task-oriented dialogue (EToD) can directly generate responses in an end-to-end fashion without modular training, which attracts escalating popularity. The advancement of deep neural networks, especially the successful use of large pre-trained models, has further led to significant progress in EToD research in recent years. In this paper, we present a thorough review and provide a unified perspective to summarize existing approaches as well as recent trends to advance the development of EToD research. The contributions of this paper can be summarized: (1) First survey: to our knowledge, we take the first step to present a thorough survey of this research field; (2) New taxonomy: we first introduce a unified perspective for EToD, including (i) Modularly EToD and (ii) Fully EToD; (3) New Frontiers: we discuss some potential frontier areas as well as the corresponding challenges, hoping to spur breakthrough research in EToD field; (4) Abundant resources: we build a public website, where EToD researchers could directly access the recent progress. We hope this work can serve as a thorough reference for the EToD research community.

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Reinforced Target-driven Conversational Promotion
Huy Dao | Lizi Liao | Dung Le | Yuxiang Nie
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

The ability to proactively engage with users towards pitching products is highly desired for conversational assistants. However, existing conversational recommendation methods overemphasize on acquiring user preferences while ignore the strategic planning for nudging users towards accepting a designated item. Hence, these methods fail to promote specified items with engaging responses. In this work, we propose a Reinforced Target-driven Conversational Promotion (RTCP) framework for conversational promotion. RTCP integrates short-term and long-term planning via a balanced gating mechanism. Inside which, the dialogue actions are predicted via a knowledge-integrated multi-head attention and guided via reinforcement learning rewards. RTCP then employs action-guided prefix tuning to generate relevant responses. Experimental results demonstrate that our model outperforms state-of-the-art models on both automatic metrics and human evaluation. Moreover, RTCP has a strong capability in quickly adapting to unseen scenarios just by updating prefix parameters without re-training the whole model.

2022

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Conversation Disentanglement with Bi-Level Contrastive Learning
Chengyu Huang | Zheng Zhang | Hao Fei | Lizi Liao
Findings of the Association for Computational Linguistics: EMNLP 2022

Conversation disentanglement aims to group utterances into detached sessions, which is a fundamental task in processing multi-party conversations. Existing methods have two main drawbacks. First, they overemphasize pairwise utterance relations but pay inadequate attention to the utterance-to-context relation modeling. Second, huge amount of human annotated data is required for training, which is expensive to obtain in practice. To address these issues, we propose a general disentangle model based on bi-level contrastive learning. It brings closer utterances in the same session while encourages each utterance to be near its clustered session prototypes in the representation space. Unlike existing approaches, our disentangle model works in both supervised setting with labeled data and unsupervised setting when no such data is available. The proposed method achieves new state-of-the-art performance on both settings across several public datasets.

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A Unified Dialogue User Simulator for Few-shot Data Augmentation
Dazhen Wan | Zheng Zhang | Qi Zhu | Lizi Liao | Minlie Huang
Findings of the Association for Computational Linguistics: EMNLP 2022

Pre-trained language models have shown superior performance in task-oriented dialogues. However, existing datasets are on limited scales, which cannot support large-scale pre-training. Fortunately, various data augmentation methods have been developed to augment large-scale task-oriented dialogue corpora. However, they heavily rely on annotated data in the target domain, which require a tremendous amount of data collection and human labeling work. In this paper, we build a unified dialogue user simulation model by pre-training on several publicly available datasets. The model can then be tuned on a target domain with few-shot data. The experiments on a target dataset across multiple domains show that our proposed model brings remarkable performance increases through data augmentation.

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Semi-supervised New Slot Discovery with Incremental Clustering
Yuxia Wu | Lizi Liao | Xueming Qian | Tat-Seng Chua
Findings of the Association for Computational Linguistics: EMNLP 2022

Discovering new slots is critical to the success of dialogue systems. Most existing methods rely on automatic slot induction in unsupervised fashion or perform domain adaptation across zero or few-shot scenarios. They have difficulties in providing high-quality supervised signals to learn clustering-friendly features, and are limited in effectively transferring the prior knowledge from known slots to new slots. In this work, we propose a Semi-supervised Incremental Clustering method (SIC), to discover new slots with the aid of existing linguistic annotation models and limited known slot data. Specifically, we harvest slot value candidates with NLP model cues and innovatively formulate the slot discovery task under an incremental clustering framework. The model gradually calibrate slot representations under the supervision of generated pseudo-labels, and automatically learns to terminate when no more salient slot remains. Our thorough evaluation on five public datasets demonstrates that it significantly outperforms state-of-the-art models.

2021

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Dialogue State Tracking with Incremental Reasoning
Lizi Liao | Le Hong Long | Yunshan Ma | Wenqiang Lei | Tat-Seng Chua
Transactions of the Association for Computational Linguistics, Volume 9

Tracking dialogue states to better interpret user goals and feed downstream policy learning is a bottleneck in dialogue management. Common practice has been to treat it as a problem of classifying dialogue content into a set of pre-defined slot-value pairs, or generating values for different slots given the dialogue history. Both have limitations on considering dependencies that occur on dialogues, and are lacking of reasoning capabilities. This paper proposes to track dialogue states gradually with reasoning over dialogue turns with the help of the back-end data. Empirical results demonstrate that our method outperforms the state-of-the-art methods in terms of joint belief accuracy for MultiWOZ 2.1, a large-scale human–human dialogue dataset across multiple domains.

2020

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Learning Goal-oriented Dialogue Policy with opposite Agent Awareness
Zheng Zhang | Lizi Liao | Xiaoyan Zhu | Tat-Seng Chua | Zitao Liu | Yan Huang | Minlie Huang
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing

Most existing approaches for goal-oriented dialogue policy learning used reinforcement learning, which focuses on the target agent policy and simply treats the opposite agent policy as part of the environment. While in real-world scenarios, the behavior of an opposite agent often exhibits certain patterns or underlies hidden policies, which can be inferred and utilized by the target agent to facilitate its own decision making. This strategy is common in human mental simulation by first imaging a specific action and the probable results before really acting it. We therefore propose an opposite behavior aware framework for policy learning in goal-oriented dialogues. We estimate the opposite agent’s policy from its behavior and use this estimation to improve the target agent by regarding it as part of the target policy. We evaluate our model on both cooperative and competitive dialogue tasks, showing superior performance over state-of-the-art baselines.

2014

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Generating Supplementary Travel Guides from Social Media
Liu Yang | Jing Jiang | Lifu Huang | Minghui Qiu | Lizi Liao
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers