Spoken Language Understanding (SLU) consists of two sub-tasks: intent detection (ID) and slot filling (SF). Given its broad range of real-world applications, enhancing SLU for practical deployment is increasingly critical. Profile-based SLU addresses ambiguous user utterances by incorporating context awareness (CA), user profiles (UP), and knowledge graphs (KG) to support disambiguation, thereby advancing SLU research toward real-world applicability. However, existing SLU datasets still fall short in representing real-world scenarios. Specifically, (1) CA uses one-hot vectors for representation, which is overly idealized, and (2) models typically focuses solely on predicting intents and slot labels, neglecting the reasoning process that could enhance performance and interpretability. To overcome these limitations, we introduce VRSLU, a novel SLU dataset that integrates both Visual images and explicit Reasoning. For over-idealized CA, we use GPT-4o and FLUX.1-dev to generate images reflecting users’ environments and statuses, followed by human verification to ensure quality. For reasoning, GPT-4o is employed to generate explanations for predicted labels, which are then refined by human annotators to ensure accuracy and coherence. Additionally, we propose an instructional template, LR-Instruct, which first predicts labels and then generates corresponding reasoning. This two-step approach helps mitigate the influence of reasoning bias on label prediction. Experimental results confirm the effectiveness of incorporating visual information and highlight the promise of explicit reasoning in advancing SLU.

Dialogue state tracking (DST) is a crucial component of task-oriented dialogue systems, as it aims to accurately track the user’s goals throughout the dialogue history. However, DST models struggle with new domains due to limited annotated data, leading to poor performance. To solve this key challenge in DST, we propose a model called Fact-aware Summarization model for few-shot DST (FaS-DST), which introduces a "Summarize, Extract, and Select" pattern. Specifically, we decompose DST into three sub-tasks: generating candidate summaries, extracting dialogue states, and scoring the candidates to select the most accurate one. Contrastive learning is incorporated to train a candidate scorer, which improves faithfulness and factuality in dialogue summarization. Additionally, we employ two strategies namely data augmentation and summary & state concatenation to improve the model’s training effectiveness. Experimental results demonstrate that FaS-DST outperforms state-of-the-art models on both MultiWOZ 2.0 and MultiWOZ 2.1 datasets in few-shot settings.

Large-scale pre-trained neural language model has facilitated to achieve the state-of-the-art performance on Dialogue State Tracking (DST) tasks. One of the existing works models the semantic correlation between the dialogue context and (domain, slot) pair encoded by BERT and make the prediction. Despite the effectiveness, they ignore the fact that there is no perfect semantic correspondence between (domain, slot) pair and the dialogue context. In this paper, we propose a domain-slot aware contrastive learning framework to solve this problem, which proposes three methods to bridge the semantic gap between the dialogue context and the (domain, slot) by constructing training sample pairs to fine-tune the BERT model and use it for base DST model. The experiments demonstrate that our proposed method has improved the performance of the baseline model on the MultiWOZ2.1 and MultiWOZ2.4 datasets, yielding competitive results.

Dialogue state tracking is responsible for predicting the user’s dialogue state during the whole dialogue process. In practical applications, values for different slots exist in individual utterances of the dialog history. With the accumulation of the dialogue history, it becomes extremely difficult to accurately predict slots and corresponding values from the lengthy dialogue history. To solve the problem of the interference caused by lengthy dialogue history, we propose a dual hierarchical selective augmentation method, which makes use of two hierarchical level information selection strategy to generate slot values. In the encoding phase, we first extract word-level matching features between the slot and each dialogue turn, and then build turn-level context relevance. In the decoding phase, first of all, from a global perspective, the dialogue turn information is selected multiple according to the dialogue context and slot, so that the model focuses more on the turn containing slot value. Secondly, our model performs weighted context attention to capture the critical words of dialogue turn from the local view. This dual hierarchical context selection alleviates the interference caused by excessive redundant information in the dialogue history and enhances the judgment ability of the model for vital turns and words. Furthermore, to enhance the copying ability of the model, we use the turn selection-guided pointer network to copy slot values from the dialogue. Experimental results show that our model significantly outperforms multiple baselines on the released MultiWOZ benchmark.

The Dialogue State Tracking (DST) module tracks the user’s intent by populating multiple predefined slots related to the dialogue task. In recent years, various graph neural network-based DST methods have been proposed to establish graph structures capturing the correlations between domains and slots, thereby enhancing model performance. However, these methods may involve redundant connections in the graph structure. To better construct relationships between domains and slots, we introduce a graph neural network-based dialogue state tracking method called Dynamic Domain Selection Graph DST (DDSG-DST). Specifically, (1) we employ Graphormer to establish hierarchical relationships between domains and slots; (2) we propose an additional domain prediction auxiliary task to predict the domain relevant to the dialogue context; (3) based on the predicted relevant domain from the auxiliary task, we dynamically select domain node information in the graph and perform dialogue state prediction. Experimental results demonstrate that we effectively establish hierarchical relationships between domains and slots, mitigate the negative impact of redundant connections in the graph structure, and enhance model performance.

Dialogue state error correction has recently been proposed to correct wrong slot values in predicted dialogue states, thereby mitigating the error propagation problem for dialogue state tracking (DST).These approaches, though effective, are heavily intertwined with specific DST models, limiting their applicability to other DST models.To solve this problem, we propose Scalable Dialogue State Correction (Scalable-DSC), which can correct wrong slot values in the dialogue state predicted by any DST model.Specifically, we propose a Structural Template Prompt (STP) that converts predicted dialogue state from any DST models into a standardized natural language sequence as a part of the historical context, associates them with dialogue history information, and generates a corrected dialogue state sequence based on predefined template options.We further enhance Scalable-DSC by introducing two training strategies.The first employs a predictive state simulator to simulate the predicted dialogue states as the training data to enhance the generalization ability of the model.The second involves using the dialogue state predicted by DST as the training data, aiming at mitigating the inconsistent error type distribution between the training and inference.Experiments confirm that our model achieves state-of-the-art results on MultiWOZ 2.0-2.4 △ .

Hongyan Xie, Haoxiang Su, Shuangyong Song, Hao Huang, Bo Zou, Kun Deng, Jianghua Lin, Zhihui Zhang, Xiaodong He. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 2022.