Dialog Action-Aware Transformer for Dialog Policy Learning

Recent works usually address Dialog policy learning DPL by training a reinforcement learning (RL) agent to determine the best dialog action. However, existing works on deep RL require a large volume of agent-user interactions to achieve acceptable performance. In this paper, we propose to make full use of the plain text knowledge from the pre-trained language model to accelerate the RL agent's learning speed. Specifically, we design a dialog action-aware transformer encoder (DaTrans), which integrates a new fine-tuning procedure named masked last action task to encourage DaTrans to be dialog-aware and distils action-specific features. Then, DaTrans is further optimized in an RL setting with ongoing interactions and evolves through exploration in the dialog action space toward maximizing long-term accumulated rewards. The effectiveness and efficiency of the proposed model are demonstrated with both simulator evaluation and human evaluation.Comment: To be appeared in SIGdial 202

MCML: A Novel Memory-based Contrastive Meta-Learning Method for Few Shot Slot Tagging

Meta-learning is widely used for few-shot slot tagging in task of few-shot learning. The performance of existing methods is, however, seriously affected by \textit{sample forgetting issue}, where the model forgets the historically learned meta-training tasks while solely relying on support sets when adapting to new tasks. To overcome this predicament, we propose the \textbf{M}emory-based \textbf{C}ontrastive \textbf{M}eta-\textbf{L}earning (aka, MCML) method, including \textit{learn-from-the-memory} and \textit{adaption-from-the-memory} modules, which bridge the distribution gap between training episodes and between training and testing respectively. Specifically, the former uses an explicit memory bank to keep track of the label representations of previously trained episodes, with a contrastive constraint between the label representations in the current episode with the historical ones stored in the memory. In addition, the \emph{adaption-from-memory} mechanism is introduced to learn more accurate and robust representations based on the shift between the same labels embedded in the testing episodes and memory. Experimental results show that the MCML outperforms several state-of-the-art methods on both SNIPS and NER datasets and demonstrates strong scalability with consistent improvement when the number of shots gets greater

From God's home to people's house: Property struggles of church redevelopment

Religious organizations participate in urban redevelopment in various ways including redeveloping their churches. While the literature has attempted to explain church redevelopment from different perspectives, what has often been forgotten is the fundamental characteristic of churches as property in cities. Drawing on the established scholarship of legal geography, this article argues that the lens of property relations offers an insightful framework to examine church redevelopment. By presenting a case study in Hong Kong, this article unpacks the property struggles of church redevelopment to examine how that resulted from the conflicting property claims and why these claims emerged. This article contrasts and analyzes the religious and market-driven values underlying these claims in the context of a property-led society like Hong Kong. To understand how urban churches transform from God’s home to people’s house, it is necessary to recognize the diverse readings of property. In so doing, this article invites scholars to re-conceptualize urban struggles from the property lens

Comparing the effects of visibility of different neighborhood greenery settings on the preference ratings and noise annoyance responses to road traffic noises

The impact of visual environment on human noise perceptions has always been under scrutiny. Two consecutive sets of laboratory experiments were performed for studying the effect of visual perceptions of different percentages of sea, greenery, and/or road views on noise-induced annoyance responses as well as preference ratings. Both experiments were carried out in a room purposely constructed inside an anechoic chamber to mimic the living room setting of a dwelling in Hong Kong. Video clips were projected consecutively onto the exterior window panel of the living room to simulate neighborhood views containing different percentages of sea, greenery and road. 82 and 58 participants were successfully administered in two experiments. Each participant was presented with 11 video clips and requested to respond to a series of questions regarding perceived noise annoyance and view preferences after presentation of individual clips. The responses collected from each experiment were employed to formulate ordered logit models to predict the probability of evoking a high annoyance response. Findings indicated that participants tended to prefer the presence of sea rather than that of either mountain or trees in views containing a trafficking road. Views containing sea would produce an attenuating effect on noise annoyance while views containing road would produce an aggravating effect. However, the size of the effects did not vary between 0% and 30% sea, or between 30% and 60% road contained in a view. Views containing dense greenery at a close distance would aggravate noise annoyance irrespective of form. However, when the percentage of greenery increased from 30% to 60%, the noise annoyance attenuating effect increased in the case of wooded mountain but decreased in the case of the more transparent tree clumps

JoTR: A Joint Transformer and Reinforcement Learning Framework for Dialog Policy Learning

Dialogue policy learning (DPL) is a crucial component of dialogue modelling. Its primary role is to determine the appropriate abstract response, commonly referred to as the "dialogue action". Traditional DPL methodologies have treated this as a sequential decision problem, using pre-defined action candidates extracted from a corpus. However, these incomplete candidates can significantly limit the diversity of responses and pose challenges when dealing with edge cases, which are scenarios that occur only at extreme operating parameters. To address these limitations, we introduce a novel framework, JoTR. This framework is unique as it leverages a text-to-text Transformer-based model to generate flexible dialogue actions. Unlike traditional methods, JoTR formulates a word-level policy that allows for a more dynamic and adaptable dialogue action generation, without the need for any action templates. This setting enhances the diversity of responses and improves the system's ability to handle edge cases effectively. In addition, JoTR employs reinforcement learning with a reward-shaping mechanism to efficiently finetune the word-level dialogue policy, which allows the model to learn from its interactions, improving its performance over time. We conducted an extensive evaluation of JoTR to assess its effectiveness. Our extensive evaluation shows that JoTR achieves state-of-the-art performance on two benchmark dialogue modelling tasks, as assessed by both user simulators and human evaluators.Comment: Our code, models and other related resources are publicly available at https://github.com/KwanWaiChung/JoT

M4LE: A Multi-Ability Multi-Range Multi-Task Multi-Domain Long-Context Evaluation Benchmark for Large Language Models

Managing long sequences has become an important and necessary feature for large language models (LLMs). However, it is still an open question of how to comprehensively and systematically evaluate the long-sequence capability of LLMs. One of the reasons is that conventional and widely-used benchmarks mainly consist of short sequences. In this paper, we propose M4LE, a Multi-ability, Multi-range, Multi-task, Multi-domain benchmark for Long-context Evaluation. M4LE is based on a diverse NLP task pool comprising 36 NLP datasets, 11 task types and 12 domains. To alleviate the scarcity of tasks with naturally long sequences and incorporate multiple-ability assessment, we propose an automatic approach (but with negligible human annotations) to convert short-sequence tasks into a unified long-sequence scenario where LLMs have to identify single or multiple relevant spans in long contexts based on explicit or semantic hints. Specifically, the scenario includes five different types of abilities: (1) explicit single-span; (2) semantic single-span; (3) explicit multiple-span; (4) semantic multiple-span; and (5) global context understanding. The resulting samples in M4LE are evenly distributed from 1k to 8k input length. We conducted a systematic evaluation on 11 well-established LLMs, especially those optimized for long-sequence inputs. Our results reveal that: 1) Current LLMs struggle to understand long context, particularly when tasks require multiple-span attention. 2) Semantic retrieval task is more difficult for competent LLMs. 3) Models fine-tuned on longer text with position interpolation have comparable performance to those using Neural Tangent Kernel (NTK) aware scaling methods without fine-tuning. We make our benchmark publicly available to encourage future research in this challenging area.Comment: Code and data are available at https://github.com/KwanWaiChung/M4L

The development of universal retirement policy in Hong Kong : a study of political and administrative dynamics

published_or_final_versionPolitics and Public AdministrationMasterMaster of Public Administratio

Large Language Models as Source Planner for Personalized Knowledge-grounded Dialogue

Open-domain dialogue system usually requires different sources of knowledge to generate more informative and evidential responses. However, existing knowledge-grounded dialogue systems either focus on a single knowledge source or overlook the dependency between multiple sources of knowledge, which may result in generating inconsistent or even paradoxical responses. To incorporate multiple knowledge sources and dependencies between them, we propose SAFARI, a novel framework that leverages the exceptional capabilities of large language models (LLMs) in planning, understanding, and incorporating under both supervised and unsupervised settings. Specifically, SAFARI decouples the knowledge grounding into multiple sources and response generation, which allows easy extension to various knowledge sources including the possibility of not using any sources. To study the problem, we construct a personalized knowledge-grounded dialogue dataset \textit{\textbf{K}nowledge \textbf{B}ehind \textbf{P}ersona}~(\textbf{KBP}), which is the first to consider the dependency between persona and implicit knowledge. Experimental results on the KBP dataset demonstrate that the SAFARI framework can effectively produce persona-consistent and knowledge-enhanced responses

Factors contributing to the psychological well-being for Hong Kong Chinese children from low-income families: A qualitative study

© 2016 The Author(s). Background: Despite compelling evidence demonstrating the negative impact of poverty and income disparity on children's psychological well-being, there has been a lack of qualitative information which addresses its contributing factors. This study aimed to shed light on this area by comparing the experiences toward daily life between children living in low- and high-income families. Methods: A qualitative study using a phenomenological approach was conducted from May 2012 to January 2013. A random sample of 42 children aged 10-13, with 25 from low- and 17 from high-income families were asked to voluntarily response to a demographic sheet and undergo individual semi-structured interviews which lasted about 25-30 min. Content analysis was used to analyze the data. Approval for the study was obtained from the Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West Cluster (reference UW 12-237). Results: The findings of this study revealed that the living environment, physical health, social life and ability to function at school of children from low-income families are severely impaired. Conclusions: It fills a gap in the literature by showing how poverty and income disparity affect the daily lives of children from low-income families on different levels. Also, adopting a sedentary lifestyle and unhealthy eating habits are possible factors mediating the effects of poverty and income disparity on the psychological well-being of children from low-income families. It is vital for healthcare professionals going beyond their normal roles to give advice on healthy lifestyles and behaviors by building multidisciplinary partnerships with schools and the community. Additionally, healthcare professionals should also target on these two possible factors to develop and implement appropriate interventions for promoting the psychological well-being among children living in poverty. Trial registration NCT02877719. 19 August 2016 retrospectively registeredpublished_or_final_versio