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 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

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

Effects of Ox-LDL on Macrophages NAD(P)H Autofluorescence Changes by Two-photon Microscopy

Ox-LDL uptakes by macrophage play a critical role in the happening of atherosclerosis. Because of its low damage on observed cells and better signal-to- background ratio, two-photon excitation fluorescence microscopy is used to observe NAD(P)H autofluorescence of macrophage under difference cultured conditions- bare cover glass, coated with fibronectin or poly-D-lysine. The results show that the optimal condition is fibronectin coated surface, on which, macrophages profile can be clearly identified on NAD(P)H autofluorescence images collected by two-photon microscopy. Moreover, different morphology and intensities of autofluorescence under different conditions were observed as well. In the future, effects of ox-LDL on macrophages will be investigated by purposed system to research etiology of atherosclerosis.Comment: Submitted on behalf of TIMA Editions (http://irevues.inist.fr/tima-editions

UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognition

Multimodal sentiment analysis (MSA) and emotion recognition in conversation (ERC) are key research topics for computers to understand human behaviors. From a psychological perspective, emotions are the expression of affect or feelings during a short period, while sentiments are formed and held for a longer period. However, most existing works study sentiment and emotion separately and do not fully exploit the complementary knowledge behind the two. In this paper, we propose a multimodal sentiment knowledge-sharing framework (UniMSE) that unifies MSA and ERC tasks from features, labels, and models. We perform modality fusion at the syntactic and semantic levels and introduce contrastive learning between modalities and samples to better capture the difference and consistency between sentiments and emotions. Experiments on four public benchmark datasets, MOSI, MOSEI, MELD, and IEMOCAP, demonstrate the effectiveness of the proposed method and achieve consistent improvements compared with state-of-the-art methods.Comment: Accepted to EMNLP 2022 main conferenc

UniSA: Unified Generative Framework for Sentiment Analysis

Sentiment analysis is a crucial task that aims to understand people's emotional states and predict emotional categories based on multimodal information. It consists of several subtasks, such as emotion recognition in conversation (ERC), aspect-based sentiment analysis (ABSA), and multimodal sentiment analysis (MSA). However, unifying all subtasks in sentiment analysis presents numerous challenges, including modality alignment, unified input/output forms, and dataset bias. To address these challenges, we propose a Task-Specific Prompt method to jointly model subtasks and introduce a multimodal generative framework called UniSA. Additionally, we organize the benchmark datasets of main subtasks into a new Sentiment Analysis Evaluation benchmark, SAEval. We design novel pre-training tasks and training methods to enable the model to learn generic sentiment knowledge among subtasks to improve the model's multimodal sentiment perception ability. Our experimental results show that UniSA performs comparably to the state-of-the-art on all subtasks and generalizes well to various subtasks in sentiment analysis.Comment: Accepted to ACM MM 202

Metronidazole-Induced Irreversible Optic Neuropathy

Metronidazole-induced optic neuropathy is a rare complication. Most patients have excellent visual recovery. In this study, we report a patient who presented with a sudden onset of severe visual loss after a 1-week course of metronidazole. Myelitis developed simultaneously. The vision and the accompanying neurological deficiency of the patient did not improve even after metronidazole was discontinued immediately and various treatments were given