The prevalence of the powerful multilingual models, such as Whisper, has significantly advanced the researches on speech recognition. However, these models often struggle with handling the code-switching setting, which is essential in multilingual speech recognition. Recent studies have attempted to address this setting by separating the modules for different languages to ensure distinct latent representations for languages. Some other methods considered the switching mechanism based on language identification. In this study, a new attention-guided adaptation is proposed to conduct parameter-efficient learning for bilingual ASR. This method selects those attention heads in a model which closely express language identities and then guided those heads to be correctly attended with their corresponding languages. The experiments on the Mandarin-English code-switching speech corpus show that the proposed approach achieves a 14.2% mixed error rate, surpassing state-of-the-art method, where only 5.6% additional parameters over Whisper are trained.

The advent of large language models (LLMs), such as GPT 3.5, has demonstrated significant potential, especially when paired with the prompt engineering techniques. However, while this setting excels in zero-shot or few-shot scenario, the direct utilization of LLMs in multi-domain task-oriented dialogue (TOD) systems often falls short compared to smaller task-specific models in standard evaluations. This indicates the need for further exploration on how to harness the power of LLMs effectively to multi-domain TOD systems. This paper addresses the aforementioned challenge by introducing a novel prompting strategy to enhance the robustness of the existing text-based multi-domain TOD systems. This strategy aims to revise the outputs of natural language understanding (NLU) component through a series of prompting steps. By capitalizing on NLU outputs, a simple and straightforward prompt design can be carried out. Experimental results illustrate the benefit of the proposed strategy in improving robustness of the multi-domain TOD system.

Dialogue policy is a crucial component of dialogue systems, responsible for determining system responses based on user inputs. While reinforcement learning (RL) can effectively optimize the dialogue policy, the system performance in real-world settings is heavily influenced by an earlier component for natural language understanding (NLU). Once the NLU produces a wrong information, the dialogue policy will be affected to degrade the performance. To enhance the robustness of dialogue policy, this paper proposes integrating RL optimization with a noisy student-teacher learning, taming the noise generated by NLU. To prevent overconfidence during knowledge transfer from the teacher, we introduce a dual-teacher mechanism where knowledge distillation is carried out by using dynamic changes in the samples stored in the replay buffer which leverages the exploration-exploitation paradigm from RL. Evaluations on multi-domain multi-turn dialogue tasks demonstrate the effectiveness of this approach which shows the increased robustness to noisy NLU outputs and accordingly the improved overall system performance.

The prompt-based learning, as used in GPT-3, has become a popular approach to extract knowledge from a powerful pre-trained language model (PLM) for natural language understanding tasks. However, either applying the hard prompt for sentences by defining a collection of human-engineering prompt templates or directly optimizing the soft or continuous prompt with labeled data may not really generalize well for unseen domain data. To cope with this issue, this paper presents a new prompt-based unsupervised domain adaptation where the learned soft prompt is able to boost the frozen pre-trained language model to deal with the input tokens from unseen domains. Importantly, the meta learning and optimization is developed to carry out the domain agnostic soft prompt where the loss for masked language model is minimized. The experiments on multi-domain natural language understanding tasks show the merits of the proposed method.

Achieving high performance in a multi-domain dialogue system with low computation is undoubtedly challenging. Previous works applying an end-to-end approach have been very successful. However, the computational cost remains a major issue since the large-sized language model using GPT-2 is required. Meanwhile, the optimization for individual components in the dialogue system has not shown promising result, especially for the component of dialogue management due to the complexity of multi-domain state and action representation. To cope with these issues, this article presents an efficient guidance learning where the imitation learning and the hierarchical reinforcement learning (HRL) with human-in-the-loop are performed to achieve high performance via an inexpensive dialogue agent. The behavior cloning with auxiliary tasks is exploited to identify the important features in latent representation. In particular, the proposed HRL is designed to treat each goal of a dialogue with the corresponding sub-policy so as to provide efficient dialogue policy learning by utilizing the guidance from human through action pruning and action evaluation, as well as the reward obtained from the interaction with the simulated user in the environment. Experimental results on ConvLab-2 framework show that the proposed method achieves state-of-the-art performance in dialogue policy optimization and outperforms the GPT-2 based solutions in end-to-end system evaluation.

This paper presents a new language processing and understanding where an adaptive data augmentation strategy for individual documents is proposed instead of using one universal policy for the whole dataset. Importantly, a reinforcement learning and understanding method is exploited for document classification where the document encoder, augmenter and classifier are jointly optimized. In particular, a new reward function based on the consistency loss maximization is presented to assure the diversity of the generated documents. Using this method, the reward for adaptive augmentation policy is immediately calculated for every augmented instance without the need of waiting the child model performance metrics as the reward. The experiments on various classification tasks with a strong baseline model show that the augmentation strategy optimization can improve the model training process by providing meaningful augmentation data which eventually result in desirable evaluation performance. Furthermore, the extensive studies on the behavior of policy in different settings are provided in order to assure the diversity of the augmented data that was obtained by the proposed method.

In the task-oriented dialogue system, dialog policy plays an important role since it determines the suitable actions based on the user's goals. However, in real situations, user's goals are varying so that the system needs to deal with the complex optimization problem for dialog policy. This paper presents a novel approach to build the multi-domain dialog system based on the multitask generative adversarial imitation learning (MGAIL). MGAIL combines hierarchical reinforcement learning and generative adversarial imitation learning where a mixture of generators are represented for multitask learning. Unlike the traditional imitation learning, this method decomposes each of complex tasks into several subtasks and builds the policy in a hierarchical way to relax the agent in handling multiple complex tasks. Experiments on a multi-domain dialogue system using MultiWOZ 2.1 under ConvLab-2 frame-work show that the proposed method outperforms the other reinforcement learning methods in system-wise evaluation in terms of complete rate, success rate and book rate.

Establishing robust dialogue policy with low computation cost is challenging, especially for multi-domain task-oriented dialogue management due to the high complexity in state and action spaces. The previous works mostly using the deterministic policy optimization only attain moderate performance. Meanwhile, state-of-the-art result that uses end-to-end approach is computationally demanding since it utilizes a large-scaled language model based on the generative pre-trained transformer-2 (GPT-2). In this study, a new learning procedure consisting of three learning stages is presented to improve multi-domain dialogue management with corrective guidance. Firstly, the behavior cloning with an auxiliary task is developed to build a robust pre-trained model by mitigating the causal confusion problem in imitation learning. Next, the pre-trained model is rectified by using reinforcement learning via the proximal policy optimization. Lastly, human-in-the-loop learning strategy is fulfilled to enhance the agent performance by directly providing corrective feedback from rule-based agent so that the agent is prevented to trap in confounded states. The experiments on end-to-end evaluation show that the proposed learning method achieves state-of-the-art result by performing nearly identical to the rule-based agent. This method outperforms the second place of 9th dialog system technology challenge (DSTC9) track 2 that uses GPT-2 as the core model in dialogue management.