Noise is a pervasive issue in recommendation systems, which can stem from user behaviors that do not align with their intentions. As a result, noise reduction has become a prominent area of research in the field of recommendation systems. However, existing noise reduction techniques in recommendation tend to compromise the performance of certain task objectives. Moreover, they require modifying the structure of the model, which introduces inference latency and additional space cost. In this paper, we propose a straightforward yet powerful approach, Multi-layer Tensor trace Norm (MTN), to address noise-related challenges. Our method achieves this by promoting information sharing across different tasks using tensor trace norms. By leveraging norms, MTN effectively reduces noise without modifying the model’s structure or incurring substantial time and space complexities. Extensive experiments on public datasets and generated noisy datasets demonstrate the effectiveness of MTN on several of the most popular multi-task models.

Multi-label unknown intent detection is a challenging task where each utterance may contain not only multiple known but also unknown intents. To tackle this challenge, pioneers proposed to predict the intent number of the utterance first, then compare it with the results of known intent matching to decide whether the utterence contains unknown intent(s). Though they have made remarkable progress on this task, their methods still suffer from two important issues: (1) It is inadequate to extract multiple intents using only utterance encoding; (2) Optimizing two sub-tasks (intent number prediction and known intent matching) independently leads to inconsistent predictions. In this article, we propose to incorporate segment augmentation rather than only use utterance encoding to better detect multiple intents. We also design a prediction consistency module to bridge the gap between the two sub-tasks. Empirical results on MultiWOZ2.3 and MixSNIPS datasets show that our method achieves state-of-the-art performance and significantly improves the best baseline.

Multi-label unknown intent detection is a challenging task where each utterance may contain not only multiple known but also unknown intents. To tackle this challenge, pioneers proposed to predict the intent number of the utterance first, then compare it with the results of known intent matching to decide whether the utterance contains unknown intent(s). Though they have made remarkable progress on this task, their method still suffers from two important issues: 1) It is inadequate to extract multiple intents using only utterance encoding; 2) Optimizing two sub-tasks (intent number prediction and known intent matching) independently leads to inconsistent predictions. In this paper, we propose to incorporate segment augmentation rather than only use utterance encoding to better detect multiple intents. We also design a prediction consistency module to bridge the gap between the two sub-tasks. Empirical results on MultiWOZ2.3 show that our method achieves state-of-the-art performance and improves the best baseline significantly.

Session-based Recommendation (SR) aims to predict users' next click based on their behavior within a short period, which is crucial for online platforms. However, most existing SR methods somewhat ignore the fact that user preference is not necessarily strongly related to the order of interactions. Moreover, they ignore the differences in importance between different samples, which limits the model-fitting performance. To tackle these issues, we put forward the method, Mining Interest Trends and Adaptively Assigning Sample Weight, abbreviated as MTAW. Specifically, we model users' instant interest based on their present behavior and all their previous behaviors. Meanwhile, we discriminatively integrate instant interests to capture the changing trend of user interest to make more personalized recommendations. Furthermore, we devise a novel loss function that dynamically weights the samples according to their prediction difficulty in the current epoch. Extensive experimental results on two benchmark datasets demonstrate the effectiveness and superiority of our method.

For task-oriented dialogue systems, Natural Language Generation (NLG) is the last and vital step which aims at generating an appropriate response according to the dialogue act (DA). While end-to-end neural networks have achieved promising performances on this task, the existing models still struggle to avoid slot mistakes. To address this challenge, we propose a novel segmented generation approach in this paper. The proposed method operates by progressively generating text for the span between two adjacent keywords (act type and slots) in semantically ordered DA. This procedure is recursively applied from left to right until a response is completed. Besides, a retrieval mechanism is utilized to better match the diversity and fluency in human language. Experimental results on four datasets demonstrate that our model achieves state-of-the-art slot error rate and also gets competitive performance on BLEU score with all strong baselines.