Dialogue policy learning (DPL) aims to determine an abstract representation (also known as action) to guide what the response should be. Typically, DPL is cast as a sequential decision problem across a series of predefined action candidates. However, such static and narrow actions can limit response diversity and impede the dialogue agent’s adaptability to new scenarios and edge cases. To overcome these challenges, we introduce a novel Joint Transformer Reinforcement Learning framework, coined as JoTR, where a text-to-text Transformer-based model is employed to directly generate dialogue actions. More concretely, JoTR formulates a token-grained policy, facilitating more dynamic and adaptable dialogue action generation without the need for predefined action candidates. This method not only enhances the diversity of responses but also significantly improves the system’s capability to manage unfamiliar scenarios. Furthermore, JoTR utilizes Reinforcement Learning with a reward-shaping mechanism to efficiently fine-tune the token-grained policy. This allows the model to evolve through interactions, thereby enhancing its performance over time. Our extensive evaluation demonstrates that JoTR surpasses previous state-of-the-art models, showing improvements of 9% and 13% in success rate, and 34% and 37% in the diversity of dialogue actions across two benchmark dialogue modeling tasks respectively. These results have been validated by both user simulators and human evaluators. Code and data are available at ://github.com/KwanWaiChung/JoTR.

Artificial intelligence (AI)-aided disease prediction has gained extensive research interest due to its capability to support clinical decision-making. Existing works mainly formulate disease prediction as a multi-label classification problem and use historical Electronic Medical Records (EMR) to train supervised models. However, in real-world clinics, such purely data-driven approaches pose two main challenges: 1) long tail problem: there are excessive EMRs for common diseases and insufficient EMRs for rare diseases, thus training over an imbalanced data set could result in a biased model that ignores rare diseases in diagnosis; 2) easily misdiagnosed diseases: some diseases can be easily distinguished while others sharing analogous conditions are much more difficult. General classification models without emphasizing easily misdiagnosed diseases may generate incorrect predictions. To tackle these two problems, we propose a Medical Knowledge-Enhanced Contrastive Learning (MKeCL) approach to disease diagnosis in this paper. MKeCL incorporates medical knowledge graphs and medical licensing exams in modeling in order to compensate for the insufficient information on rare diseases; To handle hard-to-diagnose diseases, MKeCL introduces a contrastive learning strategy to separate diseases that are easily misdiagnosed. Moreover, we establish a new benchmark, named Jarvis-D, which contains clinical EMRs collected from various hospitals. Experiments on real clinical EMRs show that the proposed MKeCL outperforms existing disease prediction approaches, especially in the setting of few-shot and zero-shot scenarios.

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 distill 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.

Automatic diagnosis (AD), a critical application of AI in healthcare, employs machine learning techniques to assist doctors in gathering patient symptom information for precise disease diagnosis. The Transformer-based method utilizes an input symptom sequence, predicts itself through auto-regression, and employs the hidden state of the final symptom to determine the disease. Despite its simplicity and superior performance demonstrated, a decline in disease diagnosis accuracy is observed caused by 1) a mismatch between symptoms observed during training and generation, and 2) the effect of different symptom orders on disease prediction. To address the above obstacles, we introduce the CoAD, a novel disease and symptom collaborative generation framework, which incorporates several key innovations to improve AD: 1) aligning sentence-level disease labels with multiple possible symptom inquiry steps to bridge the gap between training and generation; 2) expanding symptom labels for each sub-sequence of symptoms to enhance annotation and eliminate the effect of symptom order; 3) developing a repeated symptom input schema to effectively and efficiently learn the expanded disease and symptom labels. We evaluate the CoAD framework using four datasets, including three public and one private, and demonstrate that it achieves an average 2.3% improvement over previous state-of-the-art results in automatic disease diagnosis. For reproducibility, we release the code and data at https://github.com/KwanWaiChung/coad.

Prior study has shown that pretrained language models (PLM) can boost the performance of text-based recommendation. In contrast to previous works that either use PLM to encode user history as a whole input text, or impose an additional aggregation network to fuse multi-turn history representations, we propose a unified local- and global-attention Transformer encoder to better model two-level contexts of user history. Moreover, conditioned on user history encoded by Transformer encoders, our framework leverages Transformer decoders to estimate the language perplexity of candidate text items, which can serve as a straightforward yet significant contrastive signal for user-item text matching. Based on this, our framework, UniTRec, unifies the contrastive objectives of discriminative matching scores and candidate text perplexity to jointly enhance text-based recommendation. Extensive evaluation shows that UniTRec delivers SOTA performance on three text-based recommendation tasks.

Most reinforcement learning methods for dialog policy learning train a centralized agent that selects a predefined joint action concatenating domain name, intent type, and slot name. The centralized dialog agent suffers from a great many user-agent interaction requirements due to the large action space. Besides, designing the concatenated actions is laborious to engineers and maybe struggled with edge cases. To solve these problems, we model the dialog policy learning problem with a novel multi-agent framework, in which each part of the action is led by a different agent. The framework reduces labor costs for action templates and decreases the size of the action space for each agent. Furthermore, we relieve the non-stationary problem caused by the changing dynamics of the environment as evolving of agents’ policies by introducing a joint optimization process that makes agents can exchange their policy information. Concurrently, an independent experience replay buffer mechanism is integrated to reduce the dependence between gradients of samples to improve training efficiency. The effectiveness of the proposed framework is demonstrated in a multi-domain environment with both user simulator evaluation and human evaluation.

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.