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Dawei Song

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16 papers

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TCS Journal 2026 Journal Article

Quantum-inspired fuzzy matching network with morphology-enhanced word embeddings

Chenchen Zhang
Qiuchi Li
Dawei Song
Prayag Tiwari

Quantum Natural Language Processing (QNLP) has demonstrated significant advantages in addressing the uncertainty and vagueness in language understanding, with many related works relying on quantum-inspired neural networks. However, most existing quantum-inspired neural networks are based on word-level embeddings, which fail to effectively capture the complexity of language and simulate real human cognitive processes. Recognizing that the introduction of morphological information can inject prior semantic and syntactic knowledge, thereby enhancing the quality of the embeddings, and that the incorporation of fuzzy logic can further alleviate the inherent vagueness in language, we propose a quantum-inspired fuzzy matching network with morphology-enhanced word embeddings (QFNMWE). Our proposed model leverages morpheme-level and word-level embeddings to learn a richer multilevel semantic representation, followed by a fuzzy fusion and fuzzy measurement. Experimental results on various benchmarking datasets demonstrate that our QFNMWE outperforms a wide range of state-of-the-art baselines in different downstream tasks.

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YNIMG Journal 2025 Journal Article

Frequency- and state-dependent dynamics of EEG microstates during propofol anesthesia

Yun Zhang
Haidong Wang
Fei Yan
Dawei Song
Qiang Wang
Yubo Wang
Liyu Huang

Electroencephalography microstate analysis has emerged as a powerful tool for investigating brain dynamics during anesthesia-induced unconsciousness. However, existing studies typically analyze EEG signals across broad frequency bands, leaving the frequency-specific temporal characteristics of microstates poorly understood. In this study, we investigated frequency-specific EEG microstate features in the delta (0.5-4 Hz) and EEG-without-delta (4-30 Hz) frequency bands during propofol anesthesia. Sixty-channel EEG recordings were collected from 18 healthy male participants during wakefulness and propofol-induced unconsciousness. Microstate analysis was conducted separately for delta and EEG-without-delta frequency bands and microstate features were compared across frequency bands and conscious states. Our results revealed eight consistent microstate classes (MS1-MS8) with high topographic similarity across frequency bands, while global explained variance (GEV), mean duration (MeanDur), occurrence (Occ), and coverage (Cov) exhibited significant frequency- and state-dependent variations during propofol anesthesia. In the delta band, propofol-induced unconsciousness was associated with significantly longer MeanDur for microstate classes of MS4, MS5, and MS6 (p < 0.05). In the EEG-without-delta band, GEV, Cov, and Occ significantly increased for MS1 and MS3 (p < 0.01) and decreased for MS2 and MS4 (p < 0.05) during unconsciousness. Notably, microstate features in the EEG-without-delta band showed better sensitivity for discriminating conscious states, achieving a classification accuracy of 0.944. These findings emphasize the importance of frequency-specific microstate analysis in unraveling the neural dynamics of anesthesia-induced unconsciousness and highlight its potential clinical applications for improving anesthesia depth monitoring.

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EAAI Journal 2025 Journal Article

Quantum-inspired semantic matching based on neural networks with the duality of density matrices

Chenchen Zhang
Qiuchi Li
Dawei Song
Prayag Tiwari

Social media text can be semantically matched in different ways, viz paraphrase identification, answer selection, community question answering, and so on. The performance of the above semantic matching tasks depends largely on the ability of language modeling. Neural network based language models and probabilistic language models are two main streams of language modeling approaches. However, few prior work has managed to unify them in a single framework on the premise of preserving probabilistic features during the neural network learning process. Motivated by recent advances of quantum-inspired neural networks for text representation learning, we fill the gap by resorting to density matrices, a key concept describing a quantum state as well as a quantum probability distribution. The state and probability views of density matrices are mapped respectively to the neural and probabilistic aspects of language models. Concretizing this state-probability duality to the semantic matching task, we build a unified neural-probabilistic language model through a quantum-inspired neural network. Specifically, we take the state view to construct a density matrix representation of sentence, and exploit its probabilistic nature by extracting its main semantics, which form the basis of a legitimate quantum measurement. When matching two sentences, each sentence is measured against the main semantics of the other. Such a process is implemented in a neural structure, facilitating an end-to-end learning of parameters. The learned density matrix representation reflects an authentic probability distribution over the semantic space throughout the training process. Experiments show that our model significantly outperforms a wide range of prominent classical and quantum-inspired baselines.

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NeurIPS Conference 2023 Conference Paper

CMMA: Benchmarking Multi-Affection Detection in Chinese Multi-Modal Conversations

Yazhou Zhang
Yang Yu
Qing Guo
Benyou Wang
Dongming Zhao
Sagar Uprety
Dawei Song
Qiuchi Li

Human communication has a multi-modal and multi-affection nature. The inter-relatedness of different emotions and sentiments poses a challenge to jointly detect multiple human affections with multi-modal clues. Recent advances in this field employed multi-task learning paradigms to render the inter-relatedness across tasks, but the scarcity of publicly available resources sets a limit to the potential of works. To fill this gap, we build the first Chinese Multi-modal Multi-Affection conversation (CMMA) dataset, which contains 3, 000 multi-party conversations and 21, 795 multi-modal utterances collected from various styles of TV-series. CMMA contains a wide variety of affection labels, including sentiment, emotion, sarcasm and humor, as well as the novel inter-correlations values between certain pairs of tasks. Moreover, it provides the topic and speaker information in conversations, which promotes better modeling of conversational context. On the dataset, we empirically analyze the influence of different data modalities and conversational contexts on different affection analysis tasks, and exhibit the practical benefit of inter-task correlations. The full dataset will be publicly available for research\footnote{https: //github. com/annoymity2022/Chinese-Dataset}

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YNIMG Journal 2023 Journal Article

EEG spectral slope: A reliable indicator for continuous evaluation of consciousness levels during propofol anesthesia

Yun Zhang
Yubo Wang
Huanhuan Cheng
Fei Yan
Dingning Li
Dawei Song
Qiang Wang
Liyu Huang

The level of consciousness undergoes continuous alterations during anesthesia. Prior to the onset of propofol-induced complete unconsciousness, degraded levels of behavioral responsiveness can be observed. However, a reliable index to monitor altered consciousness levels during anesthesia has not been sufficiently investigated. In this study, we obtained 60-channel EEG data from 24 healthy participants during an ultra-slow propofol infusion protocol starting with an initial concentration of 1 μg/ml and a stepwise increase of 0.2 μg/ml in concentration. Consecutive auditory stimuli were delivered every 5 to 6 s, and the response time to the stimuli was used to assess the responsiveness levels. We calculated the spectral slope in a time-resolved manner by extracting 5-second EEG segments at each auditory stimulus and estimated their correlation with the corresponding response time. Our results demonstrated that during slow propofol infusion, the response time to external stimuli increased, while the EEG spectral slope, fitted at 15-45 Hz, became steeper, and a significant negative correlation was observed between them. Moreover, the spectral slope further steepened at deeper anesthetic levels and became flatter during anesthesia recovery. We verified these findings using an external dataset. Additionally, we found that the spectral slope of frontal electrodes over the prefrontal lobe had the best performance in predicting the response time. Overall, this study used a time-resolved analysis to suggest that the EEG spectral slope could reliably track continuously altered consciousness levels during propofol anesthesia. Furthermore, the frontal spectral slope may be a promising index for clinical monitoring of anesthesia depth.

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IJCAI Conference 2021 Conference Paper

An Entanglement-driven Fusion Neural Network for Video Sentiment Analysis

Dimitris Gkoumas
Qiuchi Li
Yijun Yu
Dawei Song

Video data is multimodal in its nature, where an utterance can involve linguistic, visual and acoustic information. Therefore, a key challenge for video sentiment analysis is how to combine different modalities for sentiment recognition effectively. The latest neural network approaches achieve state-of-the-art performance, but they neglect to a large degree of how humans understand and reason about sentiment states. By contrast, recent advances in quantum probabilistic neural models have achieved comparable performance to the state-of-the-art, yet with better transparency and increased level of interpretability. However, the existing quantum-inspired models treat quantum states as either a classical mixture or as a separable tensor product across modalities, without triggering their interactions in a way that they are correlated or non-separable (i. e. , entangled). This means that the current models have not fully exploited the expressive power of quantum probabilities. To fill this gap, we propose a transparent quantum probabilistic neural model. The model induces different modalities to interact in such a way that they may not be separable, encoding crossmodal information in the form of non-classical correlations. Comprehensive evaluation on two benchmarking datasets for video sentiment analysis shows that the model achieves significant performance improvement. We also show that the degree of non-separability between modalities optimizes the post-hoc interpretability.

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AAAI Conference 2021 Conference Paper

Quantum Cognitively Motivated Decision Fusion for Video Sentiment Analysis

Dimitris Gkoumas
Qiuchi Li
Shahram Dehdashti
Massimo Melucci
Yijun Yu
Dawei Song

Video sentiment analysis as a decision-making process is inherently complex, involving the fusion of decisions from multiple modalities and the so-caused cognitive biases. Inspired by recent advances in quantum cognition, we show that the sentiment judgment from one modality could be incompatible with the judgment from another, i. e. , the order matters and they cannot be jointly measured to produce a final decision. Thus the cognitive process exhibits “quantum-like” biases that cannot be captured by classical probability theories. Accordingly, we propose a fundamentally new, quantum cognitively motivated fusion strategy for predicting sentiment judgments. In particular, we formulate utterances as quantum superposition states of positive and negative sentiment judgments, and uni-modal classifiers as mutually incompatible observables, on a complex-valued Hilbert space with positive-operator valued measures. Experiments on two benchmarking datasets illustrate that our model significantly outperforms various existing decision level and a range of state-of-the-art content-level fusion approaches. The results also show that the concept of incompatibility allows effective handling of all combination patterns, including those extreme cases that are wrongly predicted by all uni-modal classifiers.

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AAAI Conference 2019 Conference Paper

A Generalized Language Model in Tensor Space

Lipeng Zhang
Peng Zhang
Xindian Ma
Shuqin Gu
Zhan Su
Dawei Song

Sentence semantic matching requires an agent to determine the semantic relation between two sentences, which is widely used in various natural language tasks such as Natural Language Inference (NLI) and Paraphrase Identification (PI). Among all matching methods, attention mechanism plays an important role in capturing the semantic relations and properly aligning the elements of two sentences. Previous methods utilized attention mechanism to select important parts of sentences at one time. However, the important parts of the sentence during semantic matching are dynamically changing with the degree of sentence understanding. Selecting the important parts at one time may be insufficient for semantic understanding. To this end, we propose a Dynamic Re-read Network (DRr-Net) approach for sentence semantic matching, which is able to pay close attention to a small region of sentences at each step and re-read the important words for better sentence semantic understanding. To be specific, we first employ Attention Stack-GRU (ASG) unit to model the original sentence repeatedly and preserve all the information from bottom-most word embedding input to up-most recurrent output. Second, we utilize Dynamic Re-read (DRr) unit to pay close attention to one important word at one time with the consideration of learned information and re-read the important words for better sentence semantic understanding. Extensive experiments on three sentence matching benchmark datasets demonstrate that DRr-Net has the ability to model sentence semantic more precisely and significantly improve the performance of sentence semantic matching. In addition, it is very interesting that some of finding in our experiments are consistent with the findings of psychological research.

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NeurIPS Conference 2019 Conference Paper

A Tensorized Transformer for Language Modeling

Xindian Ma
Peng Zhang
Shuai Zhang
Nan Duan
Yuexian Hou
Ming Zhou
Dawei Song

Latest development of neural models has connected the encoder and decoder through a self-attention mechanism. In particular, Transformer, which is solely based on self-attention, has led to breakthroughs in Natural Language Processing (NLP) tasks. However, the multi-head attention mechanism, as a key component of Transformer, limits the effective deployment of the model to a resource-limited setting. In this paper, based on the ideas of tensor decomposition and parameters sharing, we propose a novel self-attention model (namely Multi-linear attention) with Block-Term Tensor Decomposition (BTD). We test and verify the proposed attention method on three language modeling tasks (i. e. , PTB, WikiText-103 and One-billion) and a neural machine translation task (i. e. , WMT-2016 English-German). Multi-linear attention can not only largely compress the model parameters but also obtain performance improvements, compared with a number of language modeling approaches, such as Transformer, Transformer-XL, and Transformer with tensor train decomposition.

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IJCAI Conference 2019 Conference Paper

Quantum-Inspired Interactive Networks for Conversational Sentiment Analysis

Yazhou Zhang
Qiuchi Li
Dawei Song
Peng Zhang
Panpan Wang

Conversational sentiment analysis is an emerging, yet challenging Artificial Intelligence (AI) subtask. It aims to discover the affective state of each participant in a conversation. There exists a wealth of interaction information that affects the sentiment of speakers. However, the existing sentiment analysis approaches are insufficient in dealing with this task due to ignoring the interactions and dependency relationships between utterances. In this paper, we aim to address this issue by modeling intrautterance and inter-utterance interaction dynamics. We propose an approach called quantum-inspired interactive networks (QIN), which leverages the mathematical formalism of quantum theory (QT) and the long short term memory (LSTM) network, to learn such interaction dynamics. Specifically, a density matrix based convolutional neural network (DM-CNN) is proposed to capture the interactions within each utterance (i. e. , the correlations between words), and a strong-weak influence model inspired by quantum measurement theory is developed to learn the interactions between adjacent utterances (i. e. , how one speaker influences another). Extensive experiments are conducted on the MELD and IEMOCAP datasets. The experimental results demonstrate the effectiveness of the QIN model.

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TCS Journal 2018 Journal Article

A quantum-inspired multimodal sentiment analysis framework

Yazhou Zhang
Dawei Song
Peng Zhang
Panpan Wang
Jingfei Li
Xiang Li
Benyou Wang

Multimodal sentiment analysis aims to capture diversified sentiment information implied in data that are of different modalities (e. g. , an image that is associated with a textual description or a set of textual labels). The key challenge is rooted on the “semantic gap” between different low-level content features and high-level semantic information. Existing approaches generally utilize a combination of multimodal features in a somehow heuristic way. However, how to employ and combine multiple information from different sources effectively is still an important yet largely unsolved problem. To address the problem, in this paper, we propose a Quantum-inspired Multimodal Sentiment Analysis (QMSA) framework. The framework consists of a Quantum-inspired Multimodal Representation (QMR) model (which aims to fill the “semantic gap” and model the correlations between different modalities via density matrix), and a Multimodal decision Fusion strategy inspired by Quantum Interference (QIMF) in the double-slit experiment (in which the sentiment label is analogous to a photon, and the data modalities are analogous to slits). Extensive experiments are conducted on two large scale datasets, which are collected from the Getty Images and Flickr photo sharing platform. The experimental results show that our approach significantly outperforms a wide range of baselines and state-of-the-art methods.

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AAAI Conference 2018 Conference Paper

End-to-End Quantum-like Language Models with Application to Question Answering

Peng Zhang
Jiabin Niu
Zhan Su
Benyou Wang
Liqun Ma
Dawei Song

Language Modeling (LM) is a fundamental research topic in a range of areas. Recently, inspired by quantum theory, a novel Quantum Language Model (QLM) has been proposed for Information Retrieval (IR). In this paper, we aim to broaden the theoretical and practical basis of QLM. We develop a Neural Network based Quantum-like Language Model (NNQLM) and apply it to Question Answering. Speciﬁcally, based on word embeddings, we design a new density matrix, which represents a sentence (e. g. , a question or an answer) and encodes a mixture of semantic subspaces. Such a density matrix, together with a joint representation of the question and the answer, can be integrated into neural network architectures (e. g. , 2-dimensional convolutional neural networks). Experiments on the TREC-QA and WIKIQA datasets have veriﬁed the effectiveness of our proposed models.

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TIST Journal 2017 Journal Article

A Distribution Separation Method Using Irrelevance Feedback Data for Information Retrieval

Peng Zhang
Qian Yu
Yuexian Hou
Dawei Song
Jingfei Li
Bin Hu

In many research and application areas, such as information retrieval and machine learning, we often encounter dealing with a probability distribution that is mixed by one distribution that is relevant to our task in hand and the other that is irrelevant and that we want to get rid of. Thus, it is an essential problem to separate the irrelevant distribution from the mixture distribution. This article is focused on the application in Information Retrieval, where relevance feedback is a widely used technique to build a refined query model based on a set of feedback documents. However, in practice, the relevance feedback set, even provided by users explicitly or implicitly, is often a mixture of relevant and irrelevant documents. Consequently, the resultant query model (typically a term distribution) is often a mixture rather than a true relevance term distribution, leading to a negative impact on the retrieval performance. To tackle this problem, we recently proposed a Distribution Separation Method (DSM), which aims to approximate the true relevance distribution by separating a seed irrelevance distribution from the mixture one. While it achieved a promising performance in an empirical evaluation with simulated explicit irrelevance feedback data, it has not been deployed in the scenario where one should automatically obtain the irrelevance feedback data. In this article, we propose a substantial extension of the basic DSM from two perspectives: developing a further regularization framework and deploying DSM in the automatic irrelevance feedback scenario. Specifically, in order to avoid the output distribution of DSM drifting away from the true relevance distribution when the quality of seed irrelevant distribution (as the input to DSM) is not guaranteed, we propose a DSM regularization framework to constrain the estimation for the relevance distribution. This regularization framework includes three algorithms, each corresponding to a regularization strategy incorporated in the objective function of DSM. In addition, we exploit DSM in automatic (i.e., pseudo) irrelevance feedback, by automatically detecting the seed irrelevant documents via three different document reranking methods. We have carried out extensive experiments based on various TREC datasets, in order to systematically evaluate the proposed methods. The experimental results demonstrate the effectiveness of our proposed approaches in comparison with various strong baselines.

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AAAI Conference 2016 Conference Paper

Iterative Project Quasi-Newton Algorithm for Training RBM

Shuai Mi
Xiaozhao Zhao
Yuexian Hou
Peng Zhang
Wenjie Li
Dawei Song

The restricted Boltzmann machine (RBM) has been used as building blocks for many successful deep learning models, e. g. , deep belief networks (DBN) and deep Boltzmann machine (DBM) etc. The training of RBM can be extremely slow in pathological regions. The second order optimization methods, such as quasi-Newton methods, were proposed to deal with this problem. However, the non-convexity results in many obstructions for training RBM, including the infeasibility of applying second order optimization methods. In order to overcome this obstruction, we introduce an em-like iterative project quasi-Newton (IPQN) algorithm. Speciﬁcally, we iteratively perform the sampling procedure where it is not necessary to update parameters, and the sub-training procedure that is convex. In sub-training procedures, we apply quasi-Newton methods to deal with the pathological problem. We further show that Newton’s method turns out to be a good approximation of the natural gradient (NG) method in RBM training. We evaluate IPQN in a series of density estimation experiments on the artiﬁcial dataset and the MNIST digit dataset. Experimental results indicate that IPQN achieves an improved convergent performance over the traditional CD method.

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IJCAI Conference 2015 Conference Paper

Modeling Quantum Entanglements in Quantum Language Models

Mengjiao Xie
Yuexian Hou
Peng Zhang
Jingfei Li
Wenjie Li
Dawei Song

Recently, a Quantum Language Model (QLM) was proposed to model term dependencies upon Quantum Theory (QT) framework and successively applied in Information Retrieval (IR). Nevertheless, QLM’s dependency is based on co-occurrences of terms and has not yet taken into account the Quantum Entanglement (QE), which is a key quantum concept and has a significant cognitive implication. In QT, an entangled state can provide a more complete description for the nature of realities, and determine intrinsic correlations of considered objects globally, rather than those co-occurrences on the surface. It is, however, a real challenge to decide and measure QE using the classical statistics of texts in a post-measurement configuration. In order to circumvent this problem, we theoretically prove the connection between QE and statistically Unconditional Pure Dependence (UPD). Since UPD has an implementable deciding algorithm, we can in turn characterize QE by extracting the UPD patterns from texts. This leads to a measurable QE, based on which we further advance the existing QLM framework. We empirically compare our model with related models, and the results demonstrate the effectiveness of our model.

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IJCAI Conference 2013 Conference Paper

A Global Constrained Optimization Method for Designing Road Networks with Small Diameters

Teng Ma
Yuexian Hou
Xiaozhao Zhao
Dawei Song

The road network design problem is to optimize the road network by selecting paths to improve or adding paths in the existing road network, under certain constraints, e. g. , the weighted sum of modifying costs. Since its multi-objective nature, the road network design problem is often challenging for designers. Empirically, the smaller diameter a road network has, the more connected and efficient the road network is. Based on this observation, we propose a set of constrained convex models for designing road networks with small diameters. To be specific, we theoretically prove that the diameter of the road network, which is evaluated w. r. t the travel times in the network, can be bounded by the algebraic connectivity in spectral graph theory since that the upper and lower bounds of diameter are inversely proportional to algebraic connectivity. Then we can focus on increasing the algebraic connectivity instead of reducing the network diameter, under the budget constraints. The above formulation leads to a semi-definite program, in which we can get its global solution easily. Then, we present some simulation experiments to show the correctness of our method. At last, we compare our method with an existing method based on the genetic algorithm.

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