Discovering New Intents via Constrained Deep Adaptive Clustering with Cluster Refinement

Identifying new user intents is an essential task in the dialogue system. However, it is hard to get satisfying clustering results since the definition of intents is strongly guided by prior knowledge. Existing methods incorporate prior knowledge by intensive feature engineering, which not only leads to overfitting but also makes it sensitive to the number of clusters. In this paper, we propose constrained deep adaptive clustering with cluster refinement (CDAC+), an end-to-end clustering method that can naturally incorporate pairwise constraints as prior knowledge to guide the clustering process. Moreover, we refine the clusters by forcing the model to learn from the high confidence assignments. After eliminating low confidence assignments, our approach is surprisingly insensitive to the number of clusters. Experimental results on the three benchmark datasets show that our method can yield significant improvements over strong baselines.Comment: Accepted by AAAI202

Deep Open Intent Classification with Adaptive Decision Boundary

Open intent classification is a challenging task in dialogue systems. On the one hand, it should ensure the quality of known intent identification. On the other hand, it needs to detect the open (unknown) intent without prior knowledge. Current models are limited in finding the appropriate decision boundary to balance the performances of both known intents and the open intent. In this paper, we propose a post-processing method to learn the adaptive decision boundary (ADB) for open intent classification. We first utilize the labeled known intent samples to pre-train the model. Then, we automatically learn the adaptive spherical decision boundary for each known class with the aid of well-trained features. Specifically, we propose a new loss function to balance both the empirical risk and the open space risk. Our method does not need open intent samples and is free from modifying the model architecture. Moreover, our approach is surprisingly insensitive with less labeled data and fewer known intents. Extensive experiments on three benchmark datasets show that our method yields significant improvements compared with the state-of-the-art methods. The codes are released at https://github.com/thuiar/Adaptive-Decision-Boundary.Comment: Accepted by AAAI 2021 (Main Track, Long Paper

Discovering New Intents with Deep Aligned Clustering

Discovering new intents is a crucial task in dialogue systems. Most existing methods are limited in transferring the prior knowledge from known intents to new intents. They also have difficulties in providing high-quality supervised signals to learn clustering-friendly features for grouping unlabeled intents. In this work, we propose an effective method, Deep Aligned Clustering, to discover new intents with the aid of the limited known intent data. Firstly, we leverage a few labeled known intent samples as prior knowledge to pre-train the model. Then, we perform k-means to produce cluster assignments as pseudo-labels. Moreover, we propose an alignment strategy to tackle the label inconsistency problem during clustering assignments. Finally, we learn the intent representations under the supervision of the aligned pseudo-labels. With an unknown number of new intents, we predict the number of intent categories by eliminating low-confidence intent-wise clusters. Extensive experiments on two benchmark datasets show that our method is more robust and achieves substantial improvements over the state-of-the-art methods. The codes are released at https://github.com/thuiar/DeepAligned-Clustering.Comment: Accepted by AAAI 2021 (Main Track, Long Paper

Deep Refinement-Based Joint Source Channel Coding over Time-Varying Channels

In recent developments, deep learning (DL)-based joint source-channel coding (JSCC) for wireless image transmission has made significant strides in performance enhancement. Nonetheless, the majority of existing DL-based JSCC methods are tailored for scenarios featuring stable channel conditions, notably a fixed signal-to-noise ratio (SNR). This specialization poses a limitation, as their performance tends to wane in practical scenarios marked by highly dynamic channels, given that a fixed SNR inadequately represents the dynamic nature of such channels. In response to this challenge, we introduce a novel solution, namely deep refinement-based JSCC (DRJSCC). This innovative method is designed to seamlessly adapt to channels exhibiting temporal variations. By leveraging instantaneous channel state information (CSI), we dynamically optimize the encoding strategy through re-encoding the channel symbols. This dynamic adjustment ensures that the encoding strategy consistently aligns with the varying channel conditions during the transmission process. Specifically, our approach begins with the division of encoded symbols into multiple blocks, which are transmitted progressively to the receiver. In the event of changing channel conditions, we propose a mechanism to re-encode the remaining blocks, allowing them to adapt to the current channel conditions. Experimental results show that the DRJSCC scheme achieves comparable performance to the other mainstream DL-based JSCC models in stable channel conditions, and also exhibits great robustness against time-varying channels

Characteristics of mass-forming autoimmune pancreatitis commonly misdiagnosed as a malignant tumor

ObjectiveThis study aimed to explore the clinical characteristics and differential diagnosis of patients with autoimmune pancreatitis (AIP) and pancreatic cancer to prevent misdiagnosis and mistreatment.MethodsThe clinical data of patients with AIP with suspected pancreatic or bile duct malignancy and pancreatic cancer were retrospectively analyzed. The risk factors and the diagnostic value of IgG4 and Tbil levels before treatment for AIP was investigated. Moreover, the imaging features and response to hormone therapy were analyzed.ResultsAIP was commonly observed in men. Compared to patients with pancreatic cancer, the incidence of poor appetite and weight loss and carbohydrate antigen 19-9 (CA19-9) level was lower in patients with AIP, while the immunoglobulin G4 (IgG4) level was higher (p < 0.05). After treatment, the IgG4 and CA19-9 levels in patients with AIP were decreased (p < 0.001). IgG4 level before treatment (OR = 2.452, 95%CI: 1.180–5.096, P = 0.016) and total bilirubin (Tbil) level before treatment (OR = 0.992, 95%CI: 0.985–0.998, P = 0.013) were independent risk factors of AIP. Furthermore, the diagnostic value of IgG4 level before treatment, Tbil level before treatment, IgG4/Tbil before treatment, and a combination of these indicators was high. Moreover, 15 (68.18%) patients with AIP had space-occupying lesions of the pancreas, and 16 (72.73%) had autoimmune cholangitis. Most patients with AIP were sensitive to hormone therapy.ConclusionsThe Tbil and IgG4 levels, imaging findings, and hormone therapy reactivity could differentiate AIP from pancreatic cancer. A combination of IgG4, Tbil, and IgG4/Tbil before treatment might be a promising diagnostic biomarker for AIP

Constrained Self-Supervised Clustering for Discovering New Intents (Student Abstract)

Discovering new user intents is an emerging task in the dialogue system. In this paper, we propose a self-supervised clustering method that can naturally incorporate pairwise constraints as prior knowledge to guide the clustering process and does not require intensive feature engineering. Extensive experiments on three benchmark datasets show that our method can yield significant improvements over strong baselines

Learning Discriminative Representations and Decision Boundaries for Open Intent Detection

Open intent detection is a significant problem in natural language understanding, which aims to detect the unseen open intent with the prior knowledge of only known intents. Current methods have two core challenges in this task. On the one hand, they have limitations in learning friendly representations to detect the open intent. On the other hand, there lacks an effective approach to obtaining specific and compact decision boundaries for known intents. To address these issues, this paper introduces an original framework, DA-ADB, which successively learns distance-aware intent representations and adaptive decision boundaries for open intent detection. Specifically, we first leverage distance information to enhance the distinguishing capability of the intent representations. Then, we design a novel loss function to obtain appropriate decision boundaries by balancing both empirical and open space risks. Extensive experiments show the effectiveness of distance-aware and boundary learning strategies. Compared with the state-of-the-art methods, our method achieves substantial improvements on three benchmark datasets. It also yields robust performance with different proportions of labeled data and known categories. The full data and codes are available at https://github.com/thuiar/TEXTOIRComment: 13 pages, 7 figure

A FAST-BRISK Feature Detector with Depth Information

RGB-D cameras offer both color and depth images of the surrounding environment, making them an attractive option for robotic and vision applications. This work introduces the BRISK_D algorithm, which efficiently combines Features from Accelerated Segment Test (FAST) and Binary Robust Invariant Scalable Keypoints (BRISK) methods. In the BRISK_D algorithm, the keypoints are detected by the FAST algorithm and the location of the keypoint is refined in the scale and the space. The scale factor of the keypoint is directly computed with the depth information of the image. In the experiment, we have made a detailed comparative analysis of the three algorithms SURF, BRISK and BRISK_D from the aspects of scaling, rotation, perspective and blur. The BRISK_D algorithm combines depth information and has good algorithm performance