Title: Toward Robust Incomplete Multimodal Sentiment Analysis via Hierarchical Representation Learning
Venue: NeurIPS 2024
Accepted: yes
Keywords: Human intention understanding

modality missing
Abstract: Multimodal Sentiment Analysis (MSA) is an important research area that aims to understand and recognize human sentiment through multiple modalities. The complementary information provided by multimodal fusion promotes better sentiment analysis compared to utilizing only a single modality. Nevertheless, in real-world applications, many unavoidable factors may lead to situations of uncertain modality missing, thus hindering the effectiveness of multimodal modeling and degrading the model’s performance. To this end, we propose a Hierarchical Representation Learning Framework (HRLF) for the MSA task under uncertain missing modalities. Specifically, we propose a fine-grained representation factorization module that sufficiently extracts valuable sentiment information by factorizing modality into sentiment-relevant and modality-specific representations through crossmodal translation and sentiment semantic reconstruction. Moreover, a hierarchical mutual information maximization mechanism is introduced to incrementally maximize the mutual information between multi-scale representations to align and reconstruct the high-level semantics in the representations. Ultimately, we propose a hierarchical adversarial learning mechanism that further aligns and adapts the latent distribution of sentiment-relevant representations to produce robust joint multimodal representations. Comprehensive experiments on three datasets demonstrate that HRLF significantly improves MSA performance under uncertain modality missing cases.

Toward Robust Incomplete Multimodal Sentiment Analysis via Hierarchical Representation Learning

Abstract

Multimodal Sentiment Analysis (MSA) is an important research area that aims to understand and recognize human sentiment through multiple modalities. The complementary information provided by multimodal fusion promotes better sentiment analysis compared to utilizing only a single modality. Nevertheless, in real-world applications, many unavoidable factors may lead to situations of uncertain modality missing, thus hindering the effectiveness of multimodal modeling and degrading the model's performance. To this end, we propose a Hierarchical Representation Learning Framework (HRLF) for the MSA task under uncertain missing modalities. Specifically, we propose a fine-grained representation factorization module that sufficiently extracts valuable sentiment information by factorizing modality into sentiment-relevant and modality-specific representations through crossmodal translation and sentiment semantic reconstruction. Moreover, a hierarchical mutual information maximization mechanism is introduced to incrementally maximize the mutual information between multi-scale representations to align and reconstruct the high-level semantics in the representations. Ultimately, we propose a hierarchical adversarial learning mechanism that further aligns and adapts the latent distribution of sentiment-relevant representations to produce robust joint multimodal representations. Comprehensive experiments on three datasets demonstrate that HRLF significantly improves MSA performance under uncertain modality missing cases.

1 Introduction

Multimodal sentiment analysis (MSA) has attracted wide attention in recent years. Unlike unimodal emotion recognition tasks [9, 63, 64, 53, 56], MSA understands and recognizes human emotions through multiple modalities, including language, audio, and visual [31, 58]. Previous studies have shown that combining complementary information among different modalities facilitates valuable semantic generation [41, 40, 61, 55, 62]. MSA has been well studied so far under the assumption that all modalities are available in the training and inference phases [12, 66, 54, 57, 56, 25, 59, 60]. Nevertheless, in real-world applications, modalities may be missing due to security concerns, background noises, sensor limitations and so on. Ultimately, these incomplete multimodal data significantly hinder the performance of MSA. For instance, as shown in Figure 1, the entire visual

modality and some frame-level features in the language and audio modalities are missing, leading to an incorrect prediction.

In recent years, many studies [8, 28, 26, 49, 37, 50, 76, 74, 68, 27, 23, 22] attempt to address the problem of missing modalities in MSA. For example, SMIL [29] estimates the latent features of the missing modality data via Bayesian Meta-Learning. However, these methods are constrained by the following factors: (i) Implementing complex feature interactions for incomplete modalities leads to a large amount of information redundancy and cumulative errors, resulting in ineffective extraction of sentiment semantics. (ii) Lacking consideration of semantic and distributional alignment of representations, causing imprecise feature reconstruction and nonrobust joint representations.

To address the above issues, we propose a Hierarchical Representation Learning Framework (HRLF) for the MSA task under uncertain missing modalities. HRLF has three core contributions: (i) We present a fine-grained representation factorization module that sufficiently extracts valuable sentiment information by factorizing modality into sentiment-relevant and modality-specific representations through intraand inter-modality translations and sentiment semantic reconstruction. (ii) Furthermore, a hierarchical mutual information maximization mechanism is introduced to incrementally align the high-level semantics by maximizing the mutual information of the multi-scale representations of both networks in knowledge distillation. (iii) Eventually, we propose a hierarchical adversarial learning mechanism to progressively align

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the latent distributions of representations leveraging multi-scale adversarial learning. Based on these components, HRLF significantly improves MSA performance under uncertain modality missing cases on three multimodal benchmarks.

2 Related Work

2.1 Multimodal Sentiment Analysis

Multimodal Sentiment Analysis (MSA) seeks to comprehend and analyze human sentiment by utilizing diverse modalities. Unlike conventional single-modality sentiment recognition, MSA poses greater challenges owing to the intricate nature of processing and analyzing heterogeneous data across modalities. Mainstream studies in MSA [69, 70, 44, 12, 11, 42, 25] focus on designing complex fusion paradigms and interaction mechanisms to improve MSA performance. For instance, CubeMLP [42] employes three distinct multi-layer perceptron units for feature amalgamation along three axes. However, these methods rely on complete modalities and thus are impractical for realworld deployment. There are two primary approaches for addressing the missing modality problem in MSA: (1) Generative methods [8, 28, 26, 49] and (2) joint learning methods [37, 50, 76, 74, 68, 27]. Generative methods aim to regenerate missing features and semantics within modalities by leveraging the distributions of available modalities. For example, TFR-Net [67] employes a feature reconstruction module to guide the extractor to reconstruct missing semantics. Joint learning methods focus on deriving cohesive joint multimodal representations based on inter-modality correlations. For instance, MMIN [76] produces robust joint multimodal representations via cross-modality imagination. However, these methods cannot extract rich sentiment information from incomplete modalities due to their inefficient interaction. In contrast, our learning paradigm achieves effective extraction and precise reconstruction of sentiment semantics through complete modality factorization.

2.2 Factorized Representation Learning

The fundamental goal of learning factorized representations is to disentangle representations that have different semantics and distributions. This separation enables the model to more effectively capture intrinsic information and yield favorable modality representations. Previous methods of factorized

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representation learning primarily rely on auto-encoders [3] and generative adversarial networks [32]. For example, FactorVA [18] is introduced to achieve factorization by leveraging the characteristic that representations are both factorial and independent in dimension. Recently, factorization learning has been progressively utilized in MSA tasks [54, 25, 57]. For instance, FDMER [54] utilizes consistency and discreteness constraints between modalities to disentangle modalities into modality-invariant and modality-private features. DMD [25] disentangles each modality into modality-independent and modality-exclusive representations and then implements a knowledge distillation strategy among the representations with dynamic graphs. MFSA [57] refines multimodal representations and learns complementary representations across modalities by learning modality-specific and modality-agnostic representations. Despite the progress these studies have brought to MSA, certain limitations persist: (i) The supervision of the factorization process is coarse-grained and insufficient. (ii) Focusing solely on factorizing distinct representations at the modality level, without taking into account sentimentally beneficial and relevant representations. By contrast, the proposed method decomposes sentimentrelevant representations precisely through intra- and inter-modality translation and sentiment semantic reconstruction. Furthermore, hierarchical mutual information maximization and adversarial learning paradigms are employed to refine and optimize the representation of factorization at the semantic level and the distributional level, respectively, thus yielding robust joint multimodal representations.

2.3 Knowledge Distillation

Knowledge distillation leverages additional supervisory signals from a pre-trained teacher network to aid in training a student network [15]. There are generally two categories of knowledge distillation methods: distillation from intermediate features [13, 14, 19, 33, 35, 39, 45, 43, 65, 73] and distillation from logits [6, 10, 30, 52, 75]. Many studies [5, 17, 38, 21, 47, 51] utilize knowledge distillation for MSA tasks with missing modalities. These approaches aim to transfer dark knowledge from teacher networks trained on complete modalities to student networks trained by missing modalities. The teacher network typically provides richer and more comprehensive feature representations than the student network. For instance, KD-Net [17] utilizes a teacher network with complete modalities to supervise the unimodal student network at both the feature and logits levels. Despite their promising results, these methods neglect precise supervision of representations, resulting in low-quality knowledge transfer. To this end, we implement hierarchical semantic and distributional alignment of the multi-scale representations of both networks to transfer knowledge effectively.

3 Methodology

3.1 Problem Formulation

Given a multimodal video segment with three modalities as S = [ X L , X A , X V ] , where X L ∈ R T L × d L , X A ∈ R T A × d A , and X V ∈ R T V × d V denote language, audio, and visual modalities,

respectively. µ = { L, A, V } denotes the set of modality types. T m ( · ) is the sequence length and d m ( · ) is the embedding dimension, where m ∈ µ . We define two missing modality cases to simulate the most natural and holistic challenges in real-world scenarios: (1) intra-modality missingness , which indicates some frame-level features in the modality sequences are missing. (2) inter-modality missingness , which denotes some modalities are entirely missing. We aim to recognize the utterancelevel sentiments using incomplete multimodal data.

3.2 Overall Framework

Figure 2 illustrates the main workflow of HRLF. The teacher and student networks adopt a consistent structure but have different parameters. During the training phase, the workflow of our HRLF is as follows: (i) We first train the teacher network with complete-modality samples and their sentiment labels. (ii) Given a video segment sample S , we generate a missing-modality sample ˆ S with the Modality Stochastic Missing (MSM) strategy. MSM simultaneously performs intra-modality missingness and inter-modality missingness. S and ˆ S are fed into the pre-trained teacher network and the initialized student network, respectively. (iii) We input each sample into the FRF module, to factorize each modality into a sentiment-relevant representation Q m and a modality-specific representation U m , where m ∈ µ . (iv) Sequences [ C L , C A , C V ] and [ C ′ L , C ′ A , C ′ V ] are generated by concatenating Q m and U m from all modalities in the teacher and student networks. Each element of the sequences is concatenated to yield the joint multimodal representations H t and H s . (v) The multi-scale representations of both networks are obtained by passing H t and H s through the fully-connected layers. The proposed HMI and HAL are used to align the semantics and distribution between the multiscale representations. (vi) The outputs ˜ H t and ˜ H s of the fully-connected layers are fed into the task-specific classifier to get logits L t and L s . We constrain the consistency between logits and utilize L s to implement the sentiment prediction. In the inference phase, testing samples are only fed into the student network for downstream tasks.

3.3 Fine-grained Representation Factorization

Modality missing leads to ambiguous sentiment cues in the modality and information redundancy in multimodal fusion. It hinders the model from capturing valuable sentiment semantics and filtering sentiment irrelevant information. Although previous studies in MSA [12, 54] decompose the taskrelevant semantics contained in the modality to some extent via simple auto-encoder networks with reconstruction constraints, their purification of sentiment semantics is inadequate, and they cannot be applied to modality missing scenarios. Therefore, we propose a Fine-grained Representation Factorization (FRF) module to capture sentiment semantics in modalities. The core idea is to factorize each modality representation into two types of representations: (1) sentiment-relevant representation, which contains the holistic sentiment semantics of the sample. It is modality-independent, shared across all modalities of the same subject, and robust to modality missing situations. (2) modalityspecific representation, which represents modality-specific task-independent information.

As shown in Figure 2, FRF receives the multimodal sequences [ X L , X A , X V ] with modality number n = 3 . The modality X α with α ∈ µ passes through a 1D temporal convolutional layer with kernel size 3 × 3 and adds the positional embedding [46] to obtain the preliminary representations, denoted as R α = W 3 × 3 ( X α ) + PE ( T α , d ) ∈ R T α × d . The R α is fed into a Transformer [46] encoder F α ( · ) , and the last element of its output is denoted as Z α = F α ( R α ) ∈ R d . The Z α ∈ Z α is the low-level modality representation of the modality α . We aim to factorize modality representation Z α into a sentiment-relevant representation Q α by a sentiment encoder Q α = E S α ( Z α ) and a modalityspecific representation U α by a modality encoder U α = E α M ( Z α ) . E S α ( · ) and E α M ( · ) are composed of multi-layer perceptrons with the ReLU activation. The following two processes ensure adequate factorization and semantic reinforcement of the above two representations.

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Intra- and Inter-modality Translation. The proposed FRF effectively decouples sentiment-relevant and modality-specific representations by simultaneously performing intra- and inter-modality translations. Given a pair of representations Q α and U β factorized by Z α and Z β with α, β ∈ µ , the decoder D r ( · ) is supposed to translate and synthesize the representation Z αβ , whose reconstructed domain corresponds to the modality representation Z β ∈ Z β . The D r ( · ) consists of feed-forward neural layers. The modality translations include intra-modality translation ( i.e. , α = β ) and inter-modality translation ( i.e. , α = β ), whose losses are respectively denoted as:

̸

where Z αβ = D r ( E S α ( Z α ) , E β M ( Z β )) . The translation loss is denoted as: L trans = L self trans + L cross trans .

Sentiment Semantic Reconstruction. To ensure that the reconstructed modality still contains the sentiment semantics from the original modality, we encourage both to maintain the consistency of sentiment-relevant semantics and utilize the following loss for constraints, denoted as:

where Q βα = E S α ( D r ( E S β ( Z β ) , E α M ( Z α ) )) is the sentiment-relevant representation derived from the reconstructed modality representation. Consequently, the final loss of the FRF is denoted as:

3.4 Hierarchical Mutual Information Maximization

The underlying assumption of knowledge distillation is that layers in the pre-trained teacher network can represent certain attributes of given inputs that exist in the task [15]. For successful knowledge transfer, the student network must learn to incorporate such attributes into its own learning. Nevertheless, previous studies [17, 38, 21] based on knowledge distillation simply constrain the consistency between the features of both networks and lack consideration of the intrinsic semantics and inherent properties of the features, leading to semantic misalignment. From the perspective of information theory [1], semantic alignment and attribute mining of representations can be characterized as maintaining high mutual information among the layers of the teacher and student networks. We construct a Hierarchical Mutual Information (HMI) maximization mechanism to implement sufficient semantic alignment and maximize mutual information. The core idea is to progressively align the semantics of representations through a hierarchical learning paradigm.

Specifically, the sentiment-relevant and modality-specific representations Q m and U m of all modalities for teacher and student networks are concatenated to obtain the sequences [ C L , C A , C V ] and [ C ′ L , C ′ A , C ′ V ] . Each element of the sequences is concatenated to yield the joint multimodal representations H t and H s . The fully-connected layers are utilized to refine the representation H w ∈ R 3 d with w ∈ { t, s } , yielding ˜ H w ∈ R 3 d . Moreover, we obtain the intermediate multi-scale representations of all layers, denoted as I w 1 ∈ R 2 d , I w 2 ∈ R d , and I w 3 ∈ R 2 d . For the above five representations, we concatenate features of the same scale to obtain multi-scale representations E w 1 ∈ R 3 d , E w 2 ∈ R 2 d , and E w 3 ∈ R d , which are utilized in the subsequent computation.

To estimate and compute the mutual information between representations, we define two random variables X and Y . The P ( X ) and P ( Y ) are the marginal probability density function of X and Y . The joint probability density function of X and Y is denoted as P ( X , Y ) . The mutual information of the random variables X and Y is represented as:

We only need to obtain the maximum value of the mutual information, without focusing on its exact value. Referring to Deep InfoMax [16], we estimate the mutual information between variables based on the Jensen-Shannon Divergence (JSD). The mutual information maximization issue translates into minimizing the JSD between the joint distribution p ( x , y ) and the marginal distribution p ( x ) p ( y ) .

where m = 1 2 ( p ( x , y ) + p ( x ) p ( y )) and D KL is Kullback-Leibler divergence. Mutual information maximization is achieved by maximizing the dyadic lower bound of JSD, denoted as:

where sp ( w ) = log(1 + e w ) and T θ ( x , y ) is the statistics network which is a neural network with parameters θ . HMI maximizes the mutual information between hierarchical representations in knowledge distillation, whose optimization loss is expressed as:

3.5 Hierarchical Adversarial Learning

Considering that the teacher network has more robust and stable representation distributions, we also need to encourage the alignment of representation distributions in the latent space. Traditional methods [38, 17, 21] simply minimize the KL divergence between both networks, which easily disturbs the underlying learning of the student network in the deep layers, leading to confounded distributions and unrobust joint multimodal representations.

To this end, we propose a Hierarchical Adversarial Learning (HAL) mechanism for incrementally aligning the latent distributions between representations of student and teacher networks. The central principle is that the student network tries to generate representations to mislead the discriminator D e ( · ) , while D e ( · ) discriminates between the representations of the student and teacher networks. In practice, D e ( · ) is the fully-connected layers. Specifically, given multi-scale representations of E w 1 ∈ R 3 d , E w 2 ∈ R 2 d , and E w 3 ∈ R d with w ∈ { t, s } , we implement adversarial learning on the same-scale representations of the teacher and student networks to hierarchically supervise consistency. The objective function of HAL is formatted as:

3.6 Optimization Objectives

The ˜ H t and ˜ H s of the teacher and student networks are fed into their task-specific classifiers to produce logits L t and L s , respectively, and the consistency of both is constrained with KL divergence loss, denoted as L KL = KL ( L t , L s ) . The L s is used for sentiment recognition and supervised with task loss, represented as L task . For the classification and regression tasks, we use cross-entropy and MSE loss as the task losses, respectively. The overall training objective L total is expressed as L total = L task + L FRF + L HMI + L HAL + L KL .

4 Experiments

4.1 Datasets and Evaluation Metrics

We conduct our experiments on three MSA benchmarks, including MOSI [71], MOSEI [72], and IEMOCAP [4]. The experiments are performed under the word-aligned setting. MOSI is a realistic dataset for MSA. It comprises 2,199 short monologue video clips taken from 93 YouTube movie review videos. There are 1,284, 229, and 686 video clips in train, valid, and test data, respectively. MOSEI is a dataset consisting of 22,856 movie review video clips, which has 16,326, 1,871, and 4,659 samples in train, valid, and test data. Each sample of MOSI and MOSEI is labelled by human annotators with a sentiment score of -3 (strongly negative) to +3 (strongly positive). On the MOSI and MOSEI datasets, we utilize two evaluation metrics, including the Mean Absolute Error (MAE) and F1 score computed for positive/negative classification results. The IEMOCAP dataset consists of 4,453 samples of video clips. Its predetermined data partition has 2,717, 798, and 938 samples in train, valid, and test data. As recommended by [48], four emotions ( i.e., happy, sad, angry, and neutral) are selected for emotion recognition. The F1 score is used as the metric.

4.2 Implementation Details

Feature Extraction. The Glove embedding [36] is used to convert the video transcripts to obtain a 300-dimensional vector for the language modality. For the audio modality, we employ the COVAREP

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toolkit [7] to extract 74-dimensional acoustic features, including 12 Mel-frequency cepstral coefficients, voiced/unvoiced segmenting features, and glottal source parameters. For the visual modality, we utilize the Facet [2] to indicate 35 facial action units that record facial movement.

Experimental Setup. Regarding the MOSI [71] and MOSEI [72] datasets, we use the aligned multimodal sequences therein ( e.g. , all sequences of modalities have length 300) as the original input for the HRLF. All models are built on the Pytorch [34] toolbox with four NVIDIA Tesla V100 GPUs. The Adam optimizer [20] is employed for network optimization. For MOSI, MOSEI, and IEMOCAP, the detailed hyper-parameter settings are as follows: the learning rates are { 1 e -3 , 2 e -3 , 4 e -3 } , the batch sizes are { 128 , 16 , 32 } , the epoch numbers are { 50 , 20 , 30 } , and the attention heads are { 10 , 8 , 10 } . The embedding dimension is 40 on all three datasets. The raw features at the modality missing positions are replaced by zero vectors. For a fair comparison, we re-implement the StateOf-The-Art (SOTA) methods and combine them with our experimental paradigms. All experimental results are averaged over multiple experiments using five different random seeds.

4.3 Comparison with State-of-the-art Methods

We conduct a comparison between HRLF and eight representative, reproducible state-of-the-art (SOTA) methods, including complete-modality methods: Self-MM [66], CubeMLP [42], and DMD [25], and missing-modality methods: 1) joint learning methods ( i.e. , MCTN [37], TransM [50], and CorrKD [24]), and 2) generative methods ( i.e. , SMIL [29] and GCNet [26]). The extensive experiments are designed to comprehensively assess the robustness and effectiveness of HRLF in scenarios involving both intra-modality and inter-modality missingness.

Robustness to Intra-modality Missingness. We simulate intra-modality missingness by randomly discarding frame-level features in sequences with ratio p ∈ { 0 . 1 , 0 . 2 , · · · , 1 . 0 } . To visualize the robustness of all models, Figure 3 and 4 show the performance curves of the models for different ratios p . We have the following important observations. (i) As the ratio p increases, the performance of all models declines. This phenomenon demonstrates that intra-modality missingness leads to significant sentiment semantic loss and fragile multimodal representations.

(ii) Compared to complete-modality methods, our HRLF demonstrates notable performance advantages in missing-modality testing conditions and competitive performance in complete-modality testing conditions. This is because complete-modality methods rely on the assumption of data completeness, while training paradigms for missing modalities excel in capturing and reconstructing valuable sentiment semantics from incomplete multimodal data. (iii) In contrast to the missing-modality methods, our HRLF demonstrates the highest

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level of robustness. Through the purification of sentiment semantics and the dual alignment of representations, the student network masters the core competencies of precisely reconstructing missing semantics and generating robust multimodal representations.

Robustness to Inter-modality Missingness. To simulate the case of inter-modality missingness, we remove certain entire modalities from the samples. Tables 1 and 2 contrast the models' resilience to inter-modality missingness. The notation ' { l } ' signifies that only the language modality is available,

while the audio and visual modalities are missing. ' { l, a, v } ' denotes the complete-modality testing condition where all modalities are available. 'Avg.' indicates the average performance across six missing-modality testing conditions. We have the following key findings: (i) The inter-modality missingness leads to a decline in performance for all models, indicating that integrating complementary information from diverse modalities enhances the sentiment semantics within joint representations. (ii) Across all six testing conditions involving inter-modality missingness, our HRLF consistently demonstrates superior performance among the majority of metrics, affirming its robustness. For example, on the MOSI dataset, HRLF's average F1 score is improved by 2 . 05% compared to CorrKD, and in particular by 3 . 87% in the testing condition where only visual modality is available ( i.e. , { v } ). The advantage comes from its learning of fine-grained representation factorization and the hierarchical semantic alignment and distributional alignment. (iii) In unimodal testing scenarios, HRLF's performance using only the language modality significantly exceeds other configurations, showing performance similar to that of the complete-modality setup. In bimodal testing scenarios, configurations involving the language modality exhibit superior performance, even outperforming the complete-modality setup in specific metrics. This phenomenon underscores the richness of sentiment semantics within the language modality and its dominance in sentiment inference and missing semantic reconstruction processes.

4.4 Ablation Studies

To affirm the effectiveness and indispensability of the module and mechanisms and strategies proposed in HRLF, we perform ablation experiments under two missing-modality scenarios on the MOSI dataset, as shown in Table 3 and Figure 5. We have the following important observations: (i) First, when the FRF is removed, sentiment-relevant and modalityspecific information in the modalities are confused, hindering sentiment recognition and leading to significant performance degradation. This phenomenon demonstrates the effectiveness of the proposed representation factorization paradigm for adequate capture of valuable sentiment semantics. (ii) When our HMI

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is eliminated, the worse performance demonstrates that aligning the high-level semantics in the representation by maximizing mutual information can generate favorable joint representations for the student network. (iii) Finally, we remove HAL, and the declined results illustrate that multi-scale adversarial learning can effectively align the representation distributions of student and teacher networks, thus effectively constraining the consistency across representations. This paradigm facilitates the recovery of missing semantics.

4.5 Qualitative Analysis

To intuitively show the robustness of the proposed framework against modality missingness, we randomly select 100 samples in each emotion category on the IEMOCAP testing set to perform the visualization evaluation. The comparison models include CubeMLP [42] (complete-modality method), TransM [50] (joint learning-based missing-modality method), and GCNet [26] (generationbased missing-modality method). (i) As shown in Figure 6, CubeMLP fails to cope with the missing modality challenge because representations with different emotion categories are heavily confounded,

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Figure 6: Visualization of representations from different methods with four emotion categories on the IEMOCAP testing set. The default testing conditions contain intra-modality missingness ( i.e. , missing rate p = 0 . 5 ) and inter-modality missingness ( i.e. , only the language modality is available).

leading to the worst results. (ii) Although TransM and GCNet mitigate the indistinguishable emotion semantics to some extent, their performance is sub-optimal since the distribution boundaries of the different emotion representations are generally ambiguous and coupled. (iii) In comparison, our HRLF enables representations belonging to the same emotion category to form compact clusters, while representations of different categories are well separated. The above phenomenon benefits from the effective extraction of sentiment semantics and the precise filtering of task redundant information by the proposed hierarchical representation learning framework, which results in better joint multimodal representations. This further confirms the robustness and superiority of our framework.

5 Conclusion and Discussion

In this paper, we present a Hierarchical Representation Learning Framework (HRLF) to address diverse missing modality dilemmas in the MSA task. Specifically, we mine sentiment-relevant representations through a fine-grained representation factorization module. Additionally, the hierarchical mutual information maximization mechanism and the hierarchical adversarial learning mechanism are proposed for semantic and distributional alignment of representations of student and teacher networks to accurately reconstruct missing semantics and produce robust joint multimodal representations. Comprehensive experiments validate the superiority of our framework.

Discussion of Limitation and Future Work. The current method defines the modality missing cases as both inter-modality missingness and intra-modality missingness. Nevertheless, in real-world applications, modality missing cases may be very intricate and difficult to simulate. Consequently, the proposed method may suffer some minor performance loss when applied to real-world scenarios. In the future, we will explore more intricate modality missing cases and design suitable algorithms to compensate for this deficiency.

Discussion of Broad Impacts. The positive impact of our approach lies in the ability to significantly improve the robustness and stability of multimodal sentiment analysis systems against heterogeneous modality missingness in real-world applications. Nevertheless, this technology may have a negative impact when it falls into the wrong hands, e.g. , the proposed model is used for malicious purposes by injecting biased priors to recognize the emotions of specific groups.

References

Sungsoo Ahn, Shell Xu Hu, Andreas Damianou, Neil D Lawrence, and Zhenwen Dai. Variational information distillation for knowledge transfer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 9163-9171, 2019. 5

Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency. Openface: an open source facial behavior analysis toolkit. In 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) , pages 1-10. IEEE, 2016. 7

Konstantinos Bousmalis, George Trigeorgis, Nathan Silberman, Dilip Krishnan, and Dumitru Erhan. Domain separation networks. Conference on Neural Information Processing Systems (NeurIPS) , 29, 2016. 3

Carlos Busso, Murtaza Bulut, Chi-Chun Lee, Abe Kazemzadeh, Emily Mower, Samuel Kim, Jeannette N Chang, Sungbok Lee, and Shrikanth S Narayanan. Iemocap: Interactive emotional dyadic motion capture database. Language Resources and Evaluation , 42:335-359, 2008. 6

Jae Won Cho, Dong-Jin Kim, Jinsoo Choi, Yunjae Jung, and In So Kweon. Dealing with missing modalities in the visual question answer-difference prediction task through knowledge distillation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 1592-1601, 2021. 3

Jang Hyun Cho and Bharath Hariharan. On the efficacy of knowledge distillation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , pages 4794-4802, 2019. 3

Gilles Degottex, John Kane, Thomas Drugman, Tuomo Raitio, and Stefan Scherer. Covarep-a collaborative voice analysis repository for speech technologies. In 2014 Ieee International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages 960-964. IEEE, 2014. 7

Changde Du, Changying Du, Hao Wang, Jinpeng Li, Wei-Long Zheng, Bao-Liang Lu, and Huiguang He. Semi-supervised deep generative modelling of incomplete multi-modality emotional data. In Proceedings of the 26th ACM international conference on Multimedia (ACM MM) , pages 108-116, 2018. 2

Yangtao Du, Dingkang Yang, Peng Zhai, Mingchen Li, and Lihua Zhang. Learning associative representation for facial expression recognition. In IEEE International Conference on Image Processing (ICIP) , pages 889-893, 2021. 1

Tommaso Furlanello, Zachary Lipton, Michael Tschannen, Laurent Itti, and Anima Anandkumar. Born again neural networks. In International Conference on Machine Learning (ICML) , pages 1607-1616. PMLR, 2018. 3

Wei Han, Hui Chen, and Soujanya Poria. Improving multimodal fusion with hierarchical mutual information maximization for multimodal sentiment analysis. arXiv preprint arXiv:2109.00412 , 2021. 2

Devamanyu Hazarika, Roger Zimmermann, and Soujanya Poria. Misa: Modality-invariant and-specific representations for multimodal sentiment analysis. In Proceedings of the 28th ACM International Conference on Multimedia (ACM MM) , pages 1122-1131, 2020. 1, 2, 4

Byeongho Heo, Jeesoo Kim, Sangdoo Yun, Hyojin Park, Nojun Kwak, and Jin Young Choi. A comprehensive overhaul of feature distillation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , pages 1921-1930, 2019. 3

Byeongho Heo, Minsik Lee, Sangdoo Yun, and Jin Young Choi. Knowledge transfer via distillation of activation boundaries formed by hidden neurons. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) , volume 33, pages 3779-3787, 2019. 3

Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 , 2015. 3, 5

R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio. Learning deep representations by mutual information estimation and maximization. arXiv preprint arXiv:1808.06670 , 2018. 5

Minhao Hu, Matthis Maillard, Ya Zhang, Tommaso Ciceri, Giammarco La Barbera, Isabelle Bloch, and Pietro Gori. Knowledge distillation from multi-modal to mono-modal segmentation networks. In Medical Image Computing and Computer Assisted Intervention-MICCAI 2020: 23rd International Conference, Lima, Peru, October 4-8, 2020, Proceedings, Part I 23 , pages 772-781. Springer, 2020. 3, 5, 6

Hyunjik Kim and Andriy Mnih. Disentangling by factorising. In International Conference on Machine Learning (ICML) , pages 2649-2658. PMLR, 2018. 3

Jangho Kim, SeongUk Park, and Nojun Kwak. Paraphrasing complex network: Network compression via factor transfer. Advances in Neural Information Processing Systems (NeurIPS) , 31, 2018. 3

Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 , 2014. 7

Saurabh Kumar, Biplab Banerjee, and Subhasis Chaudhuri. Online sensor hallucination via knowledge distillation for multimodal image classification. arXiv preprint arXiv:1908.10559 , 2019. 3, 5, 6

Mingcheng Li, Dingkang Yang, Yuxuan Lei, Shunli Wang, Shuaibing Wang, Liuzhen Su, Kun Yang, Yuzheng Wang, Mingyang Sun, and Lihua Zhang. A unified self-distillation framework for multimodal sentiment analysis with uncertain missing modalities. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) , volume 38, pages 10074-10082, 2024. 2

Mingcheng Li, Dingkang Yang, and Lihua Zhang. Towards robust multimodal sentiment analysis under uncertain signal missing. IEEE Signal Processing Letters , 30:1497-1501, 2023. 2

Mingcheng Li, Dingkang Yang, Xiao Zhao, Shuaibing Wang, Yan Wang, Kun Yang, Mingyang Sun, Dongliang Kou, Ziyun Qian, and Lihua Zhang. Correlation-decoupled knowledge distillation for multimodal sentiment analysis with incomplete modalities. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 1245812468, 2024. 7, 8, 9

Yong Li, Yuanzhi Wang, and Zhen Cui. Decoupled multimodal distilling for emotion recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 6631-6640, 2023. 1, 2, 3, 7, 8, 9

Zheng Lian, Lan Chen, Licai Sun, Bin Liu, and Jianhua Tao. Gcnet: graph completion network for incomplete multimodal learning in conversation. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2023. 2, 7, 8, 9

Zhizhong Liu, Bin Zhou, Dianhui Chu, Yuhang Sun, and Lingqiang Meng. Modality translationbased multimodal sentiment analysis under uncertain missing modalities. Information Fusion , 101:101973, 2024. 2

Wei Luo, Mengying Xu, and Hanjiang Lai. Multimodal reconstruct and align net for missing modality problem in sentiment analysis. In International Conference on Multimedia Modeling , pages 411-422. Springer, 2023. 2

Mengmeng Ma, Jian Ren, Long Zhao, Sergey Tulyakov, Cathy Wu, and Xi Peng. Smil: Multimodal learning with severely missing modality. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) , volume 35, pages 2302-2310, 2021. 2, 7, 8, 9

Seyed Iman Mirzadeh, Mehrdad Farajtabar, Ang Li, Nir Levine, Akihiro Matsukawa, and Hassan Ghasemzadeh. Improved knowledge distillation via teacher assistant. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) , volume 34, pages 5191-5198, 2020. 3

Louis-Philippe Morency, Rada Mihalcea, and Payal Doshi. Towards multimodal sentiment analysis: Harvesting opinions from the web. In Proceedings of the 13th International Conference on Multimodal Interfaces , pages 169-176, 2011. 1

Augustus Odena, Christopher Olah, and Jonathon Shlens. Conditional image synthesis with auxiliary classifier gans. In International Conference on Machine Learning (ICML) , pages 2642-2651. PMLR, 2017. 3

Wonpyo Park, Dongju Kim, Yan Lu, and Minsu Cho. Relational knowledge distillation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 3967-3976, 2019. 3

Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. Automatic differentiation in pytorch. 2017. 7

Baoyun Peng, Xiao Jin, Jiaheng Liu, Dongsheng Li, Yichao Wu, Yu Liu, Shunfeng Zhou, and Zhaoning Zhang. Correlation congruence for knowledge distillation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , pages 5007-5016, 2019. 3

Jeffrey Pennington, Richard Socher, and Christopher D Manning. Glove: Global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) , pages 1532-1543, 2014. 6

Hai Pham, Paul Pu Liang, Thomas Manzini, Louis-Philippe Morency, and Barnabás Póczos. Found in translation: Learning robust joint representations by cyclic translations between modalities. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) , volume 33, pages 6892-6899, 2019. 2, 7, 8, 9

Masoomeh Rahimpour, Jeroen Bertels, Ahmed Radwan, Henri Vandermeulen, Stefan Sunaert, Dirk Vandermeulen, Frederik Maes, Karolien Goffin, and Michel Koole. Cross-modal distillation to improve mri-based brain tumor segmentation with missing mri sequences. IEEE Transactions on Biomedical Engineering , 69(7):2153-2164, 2021. 3, 5, 6

Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio. Fitnets: Hints for thin deep nets. arXiv preprint arXiv:1412.6550 , 2014. 3

Roee Shraga, Haggai Roitman, Guy Feigenblat, and Mustafa Cannim. Web table retrieval using multimodal deep learning. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval , pages 1399-1408, 2020. 1

Matthias Springstein, Eric Müller-Budack, and Ralph Ewerth. Quti! quantifying text-image consistency in multimodal documents. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval , pages 2575-2579, 2021. 1

Hao Sun, Hongyi Wang, Jiaqing Liu, Yen-Wei Chen, and Lanfen Lin. Cubemlp: An mlp-based model for multimodal sentiment analysis and depression estimation. In Proceedings of the 30th ACM International Conference on Multimedia (ACM MM) , pages 3722-3729, 2022. 2, 7, 8, 9

Yonglong Tian, Dilip Krishnan, and Phillip Isola. Contrastive representation distillation. arXiv preprint arXiv:1910.10699 , 2019. 3

Yao-Hung Hubert Tsai, Shaojie Bai, Paul Pu Liang, J Zico Kolter, Louis-Philippe Morency, and Ruslan Salakhutdinov. Multimodal transformer for unaligned multimodal language sequences. In Proceedings of the conference. Association for Computational Linguistics. Meeting , volume 2019, page 6558. NIH Public Access, 2019. 2

Frederick Tung and Greg Mori. Similarity-preserving knowledge distillation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , pages 1365-1374, 2019. 3

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in Neural Information Processing Systems (NeurIPS) , 30, 2017. 4

Hu Wang, Congbo Ma, Jianpeng Zhang, Yuan Zhang, Jodie Avery, Louise Hull, and Gustavo Carneiro. Learnable cross-modal knowledge distillation for multi-modal learning with missing modality. In International Conference on Medical Image Computing and Computer-Assisted Intervention , pages 216-226. Springer, 2023. 3

Yansen Wang, Ying Shen, Zhun Liu, Paul Pu Liang, Amir Zadeh, and Louis-Philippe Morency. Words can shift: Dynamically adjusting word representations using nonverbal behaviors. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) , volume 33, pages 72167223, 2019. 6

Yuanzhi Wang, Zhen Cui, and Yong Li. Distribution-consistent modal recovering for incomplete multimodal learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , pages 22025-22034, 2023. 2

Zilong Wang, Zhaohong Wan, and Xiaojun Wan. Transmodality: An end2end fusion method with transformer for multimodal sentiment analysis. In Proceedings of The Web Conference 2020 , pages 2514-2520, 2020. 2, 7, 8, 9

Wenke Xia, Xingjian Li, Andong Deng, Haoyi Xiong, Dejing Dou, and Di Hu. Robust crossmodal knowledge distillation for unconstrained videos. arXiv preprint arXiv:2304.07775 , 2023. 3

Chenglin Yang, Lingxi Xie, Chi Su, and Alan L Yuille. Snapshot distillation: Teacher-student optimization in one generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 2859-2868, 2019. 3

Dingkang Yang, Zhaoyu Chen, Yuzheng Wang, Shunli Wang, Mingcheng Li, Siao Liu, Xiao Zhao, Shuai Huang, Zhiyan Dong, Peng Zhai, and Lihua Zhang. Context de-confounded emotion recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 19005-19015, June 2023. 1

Dingkang Yang, Shuai Huang, Haopeng Kuang, Yangtao Du, and Lihua Zhang. Disentangled representation learning for multimodal emotion recognition. In Proceedings of the 30th ACM International Conference on Multimedia (ACM MM) , pages 1642-1651, 2022. 1, 3, 4

Dingkang Yang, Shuai Huang, Yang Liu, and Lihua Zhang. Contextual and cross-modal interaction for multi-modal speech emotion recognition. IEEE Signal Processing Letters , 29:2093-2097, 2022. 1

Dingkang Yang, Shuai Huang, Shunli Wang, Yang Liu, Peng Zhai, Liuzhen Su, Mingcheng Li, and Lihua Zhang. Emotion recognition for multiple context awareness. In Computer VisionECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XXXVII , pages 144-162. Springer, 2022. 1

Dingkang Yang, Haopeng Kuang, Shuai Huang, and Lihua Zhang. Learning modality-specific and-agnostic representations for asynchronous multimodal language sequences. In Proceedings of the 30th ACM International Conference on Multimedia (ACM MM) , pages 1708-1717, 2022. 1, 3

Dingkang Yang, Haopeng Kuang, Kun Yang, Mingcheng Li, and Lihua Zhang. Towards asynchronous multimodal signal interaction and fusion via tailored transformers. IEEE Signal Processing Letters , 2024. 1

Dingkang Yang, Mingcheng Li, Linhao Qu, Kun Yang, Peng Zhai, Song Wang, and Lihua Zhang. Asynchronous multimodal video sequence fusion via learning modality-exclusive and-agnostic representations. IEEE Transactions on Circuits and Systems for Video Technology , 2024. 1

Dingkang Yang, Mingcheng Li, Dongling Xiao, Yang Liu, Kun Yang, Zhaoyu Chen, Yuzheng Wang, Peng Zhai, Ke Li, and Lihua Zhang. Towards multimodal sentiment analysis debiasing via bias purification. In Proceedings of the European Conference on Computer Vision (ECCV) , 2024. 1

Dingkang Yang, Yang Liu, Can Huang, Mingcheng Li, Xiao Zhao, Yuzheng Wang, Kun Yang, Yan Wang, Peng Zhai, and Lihua Zhang. Target and source modality co-reinforcement for emotion understanding from asynchronous multimodal sequences. Knowledge-Based Systems , 265:110370, 2023. 1

Dingkang Yang, Dongling Xiao, Ke Li, Yuzheng Wang, Zhaoyu Chen, Jinjie Wei, and Lihua Zhang. Towards multimodal human intention understanding debiasing via subjectdeconfounding. arXiv preprint arXiv:2403.05025 , 2024. 1

Dingkang Yang, Kun Yang, Haopeng Kuang, Zhaoyu Chen, Yuzheng Wang, and Lihua Zhang. Towards context-aware emotion recognition debiasing from a causal demystification perspective via de-confounded training. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2024. 1

Dingkang Yang, Kun Yang, Mingcheng Li, Shunli Wang, Shuaibing Wang, and Lihua Zhang. Robust emotion recognition in context debiasing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 12447-12457, 2024. 1

Junho Yim, Donggyu Joo, Jihoon Bae, and Junmo Kim. A gift from knowledge distillation: Fast optimization, network minimization and transfer learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 4133-4141, 2017. 3

Wenmeng Yu, Hua Xu, Ziqi Yuan, and Jiele Wu. Learning modality-specific representations with self-supervised multi-task learning for multimodal sentiment analysis. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) , volume 35, pages 10790-10797, 2021. 1, 7, 8, 9

Ziqi Yuan, Wei Li, Hua Xu, and Wenmeng Yu. Transformer-based feature reconstruction network for robust multimodal sentiment analysis. In Proceedings of the 29th ACM International Conference on Multimedia (ACM MM) , pages 4400-4407, 2021. 2

Ziqi Yuan, Yihe Liu, Hua Xu, and Kai Gao. Noise imitation based adversarial training for robust multimodal sentiment analysis. IEEE Transactions on Multimedia , 2023. 2

Amir Zadeh, Minghai Chen, Soujanya Poria, Erik Cambria, and Louis-Philippe Morency. Tensor fusion network for multimodal sentiment analysis. arXiv preprint arXiv:1707.07250 , 2017. 2

Amir Zadeh, Paul Pu Liang, Navonil Mazumder, Soujanya Poria, Erik Cambria, and LouisPhilippe Morency. Memory fusion network for multi-view sequential learning. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) , volume 32, 2018. 2

Amir Zadeh, Rowan Zellers, Eli Pincus, and Louis-Philippe Morency. Mosi: multimodal corpus of sentiment intensity and subjectivity analysis in online opinion videos. arXiv preprint arXiv:1606.06259 , 2016. 6, 7

AmirAli Bagher Zadeh, Paul Pu Liang, Soujanya Poria, Erik Cambria, and Louis-Philippe Morency. Multimodal language analysis in the wild: Cmu-mosei dataset and interpretable dynamic fusion graph. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages 2236-2246, 2018. 6, 7

Sergey Zagoruyko and Nikos Komodakis. Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer. arXiv preprint arXiv:1612.03928 , 2016. 3

Jiandian Zeng, Tianyi Liu, and Jiantao Zhou. Tag-assisted multimodal sentiment analysis under uncertain missing modalities. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval , pages 1545-1554, 2022. 2

Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia. Pyramid scene parsing network. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 2881-2890, 2017. 3

Jinming Zhao, Ruichen Li, and Qin Jin. Missing modality imagination network for emotion recognition with uncertain missing modalities. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) , pages 2608-2618, 2021. 2

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The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.

If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).

11. Safeguards

Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?

Answer: [NA]

Justification: Our paper poses no such risks.

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The answer NA means that the paper poses no such risks.

Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.

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We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort.

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Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?

Answer: [Yes]

Justification: The MOSI, MOSEI and IEMOCAP datasets and the Pytorch toolbox in this paper are existing assets and we cite the references.

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The answer NA means that the paper does not use existing assets.

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The answer NA means that the paper does not release new assets.

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The paper should discuss whether and how consent was obtained from people whose asset is used.

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Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?

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The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Including this information in the supplemental material is fine, but if the main contribution of the paper involves human subjects, then as much detail as possible should be included in the main paper.

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The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper addresses the issue of missing modality information in multimodal systems and proposes solutions for two key problems:\n1. Excessively complex feature interactions lead to information redundancy and cumulative errors.\n2. Previous works did not align representations semantically.\n\nIn enhancing multimodal representations, the core approach utilizes the transformer’s encoder and decoder. It strengthens inter-modal interactions post-encoder to ensure semantic relevance is maintained in the decoder's reconstruction.\nThe model achieves state-of-the-art (SOTA) performance and includes detailed ablation studies and experimental analyses.\n\nSTRENGTHS:\n1. The model considers the differences between semantic and modality representations, utilizing the transformer’s encoder and decoder processes for reconstruction to facilitate inter-modal interactions.\n2. It accounts for the impact of Mutual Information (MI) on the learned representations, using MI to enhance the quality of these representations.\n3. The paper conducts thorough experiments, addressing the model's robustness when adjusting the modality missing ratio.\n\nWEAKNESSES:\n1. The use of numerous symbols in both the figures and text increases the complexity and time required for understanding. DIFFICULT TO READ\n2. In the introduction, the problem is not clearly stated; it is too brief. It only specifically mentions SMIL's approach without thoroughly analyzing the issues present in previous methods.\n3. It appears that the model relies heavily on MRM (Modality Stochastic Missing) for masking certain information to generate task-specific data. This raises concerns about the model's dependency on MRM. If MRM focuses on masking emotional words, does it hinder the model's understanding?\n4. The paper does not consider using large models to address this problem, nor does it compare the performance with that of large models.\n5. The HMI (Hierarchical Mutual Information) module applies Bengio's MI concepts rather straightforwardly, lacking in innovation.\n6. HAL (Hierarchical Adversarial Learning), presented as a separate contribution, does not make a significant impact in terms of performance or design. The hierarchical aspect merely reflects multi-scale representation.\n\nQUESTIONS:\nApplication Scenario: In the introductory example, if the crucial information \"bored\" is missing in L (Linguistic), A (Acoustic), and V (Visual) modalities, how is it still relevant to determine the task? What basis is used for this determination?\nModel Design: Why is it necessary to include a modality encoder in the model? Ensuring semantic interactions across different modalities should be sufficient.\nDid the baselines you compared against also use the SAME masking strategy as you did?\n\nLIMITATIONS:\nSee Weaknesses and Questions.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-04"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper addresses the challenge of data incompleteness in Multimodal Sentiment Analysis (MSA). It introduces a novel approach called the Language-dominated Noise-resistant Learning Network (LNLN). The LNLN leverages the dense sentiment information in the language modality, considered the dominant modality, to improve robustness across various noise scenarios. It features two main components: a dominant modality correction (DMC) module and a dominant modality-based multimodal learning (DMML) module, which enhance the quality of the dominant modality representations. The model's performance was evaluated using datasets like MOSI, MOSEI, and SIMS, demonstrating superior robustness and accuracy compared to existing baselines. The comprehensive experiments provide new insights and a thorough comparative analysis in the context of incomplete data, advancing the field of MSA.\n\nSTRENGTHS:\nThe introduction of the Language-dominated Noise-resistant Learning Network (LNLN) is innovative, addressing the issue of data incompleteness effectively by prioritizing the language modality, which is typically rich in sentiment information.\n\nThe authors conduct thorough experiments on well-known datasets (MOSI, MOSEI, SIMS), adding credibility to their findings. The detailed comparison with existing methods under diverse noise scenarios is particularly valuable.\n\nThe use of the Dominant Modality Correction (DMC) module and Dominant Modality Based Multimodal Learning (DMML) module is well-justified and systematically enhances the model’s robustness by ensuring the quality of dominant modality representations.\n\nWEAKNESSES:\nThe focus on language as the dominant modality, while justified, may not generalize well to scenarios where other modalities (like visual or auditory) are equally or more critical. This could limit the applicability of the model to certain types of data or tasks.\n\nQUESTIONS:\nExtending the approach to consider scenarios where visual or auditory data might be dominant could improve the versatility and applicability of the model.\n\nMore detailed ablation studies on MOSEI and SIMS would provide deeper insights into the workings and benefits of the proposed model.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 8, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 4, "DATE": "2024-07-06"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents the Representation Factorization and Alignment (ReFA) framework for Multimodal Sentiment Analysis (MSA) under uncertain missing modalities. ReFA employs a fine-grained representation factorization module to extract sentiment-relevant and modality-specific representations through crossmodal translation and sentiment semantic reconstruction. It introduces a hierarchical mutual information maximization mechanism to align and reconstruct high-level semantics incrementally. Additionally, a hierarchical adversarial learning mechanism progressively aligns latent distributions to create robust joint multimodal representations. Experiments on three datasets show that ReFA significantly enhances MSA performance under both uncertain missing-modality and complete-modality conditions.\n\nSTRENGTHS:\nStrength:\n\n1. One of the challenges in multimodal sentiment analysis is the potential for missing modality information in real-world scenarios. This study addresses this practical issue by proposing an effective algorithm with notable real-world applicability.\n\n2. The motivation for the research is clearly articulated, pinpointing the shortcomings of existing studies with strong logical coherence.\n\n3. The proposed algorithm achieves state-of-the-art (SOTA) results across relevant datasets, which validates its effectiveness to a significant extent.\n\nWEAKNESSES:\nWeakness:\n\n1. The idea and design of Intra- and Inter-modality Translations are sound; however, the implementation of the translation loss is overly simplistic and lacks a task-specific approach, making the methodology appear somewhat naive.\n\n2. Similarly, the Sentiment Semantic Reconstruction section suffers from the same issue, with a basic and unrefined approach that fails to leverage the complexity of the task.\n\n3. Both sections give the impression that while Translations and Reconstruction are being performed, the methods are indistinguishable aside from their goals. This indicates a lack of differentiation in handling the unique characteristics of each type of information.\n\n4. The HMI and HAL components seem to merely apply two loss functions to the multi-scale features of the teacher-student network. This approach is quite common in knowledge distillation and thus lacks significant innovation.\n\n5. Moreover, the paper lacks relevant case studies to validate the effectiveness of the proposed algorithm. There is also an absence of error analysis to identify the limitations and shortcomings of the method.\n\nQUESTIONS:\n1. The idea and design of Intra- and Inter-modality Translations are sound; however, the implementation of the translation loss is overly simplistic and lacks a task-specific approach, making the methodology appear somewhat naive.\n\n2. Similarly, the Sentiment Semantic Reconstruction section suffers from the same issue, with a basic and unrefined approach that fails to leverage the complexity of the task.\n\n3. Both sections give the impression that while Translations and Reconstruction are being performed, the methods are indistinguishable aside from their goals. This indicates a lack of differentiation in handling the unique characteristics of each type of information.\n\n4. The HMI and HAL components seem to merely apply two loss functions to the multi-scale features of the teacher-student network. This approach is quite common in knowledge distillation and thus lacks significant innovation.\n\n5. Moreover, the paper lacks relevant case studies to validate the effectiveness of the proposed algorithm. There is also an absence of error analysis to identify the limitations and shortcomings of the method.\n\nLIMITATIONS:\nThe current distillation framework relies on a teacher network trained with complete modality data. A potential limitation of this approach is that its applicability to scenarios with missing modalities is inherently constrained by the performance ceiling of the teacher network. This dependency may limit the effectiveness and generalizability of the framework in handling diverse cases of missing modality data.", "TITLE": "Official Review", "RECOMMENDATION": 8, "REVIEWER_CONFIDENCE": 3, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-06"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper addresses the challenges of multimodal sentiment analysis (MSA) in real-world applications, particularly when some modalities may be missing, which can hinder the effectiveness of the analysis. The authors propose a framework called Representation Factorization and Alignment (ReFA) to tackle the issue of uncertain missing modalities in MSA. \n\nThe ReFA framework consists of three core components:\n\n1. Fine-grained Representation Factorization (FRF) module: This module extracts valuable sentiment information by factorizing each modality into sentiment-relevant and modality-specific representations through cross-modal translation and sentiment semantic reconstruction.\n\n2. Hierarchical Mutual Information (HMI) maximization mechanism: This mechanism incrementally maximizes the mutual information between multi-scale representations to align and reconstruct the high-level semantics in the representations.\n\n3. Hierarchical Adversarial Learning (HAL) mechanism: This mechanism progressively aligns and adapts the latent distributions of the representations to produce robust joint multimodal representations.\n\nThe authors conducted comprehensive experiments on three datasets, demonstrating that the ReFA framework significantly improves MSA performance under both uncertain missing-modality and complete-modality testing conditions.\n\nSTRENGTHS:\nOriginality:\n1. The paper proposes a Representation Factorization and Alignment (ReFA) framework to address multimodal sentiment analysis under uncertain missing modalities.\n2. Introduces innovative components like fine-grained representation factorization, hierarchical mutual information maximization, and hierarchical adversarial learning.\n\nQuality:\n1. Comprehensive experiments on three datasets (MOSI, MOSEI, IEMOCAP) demonstrate significant performance improvements.\n2. Ablation studies validate the effectiveness of each proposed component.\n3. Qualitative analysis with visualizations provides intuitive understanding of the framework's robustness.\n\nClarity:\n1. The paper is well-structured, with clear sections on related work, methodology, and experiments.\n2. Figures and tables effectively illustrate the framework and results.\n\nSignificance:\n1. Addresses an important real-world challenge of missing modalities in multimodal sentiment analysis.\n2. Shows consistent performance improvements over state-of-the-art methods across different missing modality scenarios.\n3. The framework's robustness to both intra-modality and inter-modality missingness enhances its practical applicability.\n\nWEAKNESSES:\n1. The paper lacks a detailed discussion on the computational complexity and runtime performance of the proposed framework compared to existing methods.\n2. While the proposed ReFA framework is innovative, the individual components (such as mutual information maximization and adversarial learning) have been explored in other contexts. The novelty primarily lies in their specific combination and application to MSA with missing modalities.\n3. The paper did not mention the models that were used for the final classification or regression, only mentioned feature extraction models. \n4. The paper doesn't discuss potential limitations of the approach or cases where it might not perform well.\n5. There's no discussion on the framework's generalizability to other multimodal tasks beyond sentiment analysis.\n\nQUESTIONS:\n1. Can the authors provide more details on the computational requirements and training time of ReFA compared to baseline methods?\n2. How does the performance of ReFA change with varying amounts of training data? Is there a minimum data requirement for the framework to be effective?\n3. Have the authors explored the applicability of ReFA to other multimodal tasks beyond sentiment analysis? If not, what modifications might be needed?\n4. Could the authors provide insights into why the language modality seems to be particularly effective in unimodal scenarios?\n5. Are there any scenarios or types of data where ReFA might not perform well? It would be helpful to discuss potential limitations.\n6. How does the framework handle noisy data within the available modalities? Can the authors provide experimental results or discussions on the impact of noisy data on the performance of ReFA?\n\nLIMITATIONS:\nThe paper does not explicitly address limitations or potential negative societal impacts of the work. Some suggestions for improvement:\n1. Societal Impact: While the paper mentions broader impacts and limitations in the appendix, the main text lacks a detailed discussion on potential negative societal impacts. The authors should consider elaborating on ethical concerns, such as the potential misuse of sentiment analysis in sensitive applications or privacy issues related to multimodal data collection.\n2. Discuss potential biases in the datasets used and how they might affect the model's performance across different demographic groups.\n3. Acknowledge any limitations in the generalizability of the results to real-world, non-curated data.", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-06"}