Toward Robust Incomplete Multimodal Sentiment Analysis via Hierarchical Representation Learning

Authors: mingcheng  li
dingkang  yang
yang  liu
shunli  wang
jiawei  chen
shuaibing  wang
jinjie  wei
yue  jiang
qingyao  xu
xiaolu  hou
mingyang  sun
ziyun  qian
dongliang  kou
lihua  zhang
Venue: NeurIPS_cc_2024_Conference
Published: 2024-05-10 15:33:02

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.

Main paper: 
 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 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.Language2 Related WorkQuestion: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?Answer: [Yes] Justification: Please refer to the "Abstract" and "1 Introduction" for our paper's contributions and scopes.Guidelines:• The answer NA means that the abstract and introduction do not include the claims made in the paper. • The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers. • The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings. It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper. 
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.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 But for me I was just a little bored. 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.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.Question: Does the paper discuss the limitations of the work performed by the authors?Answer: [Yes] Justification: Please refer to the "5 Conclusion and Discussion" section for the limitations of our work.Guidelines:• The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper. • The authors are encouraged to create a separate "Limitations" section in their paper.• The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be. • The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated. • The authors should reflect on the factors that influence the performance of the approach.For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon. • The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size. • If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness. • While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations. 
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.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 Ŝ with the Modality Stochastic Missing (MSM) strategy. MSM simultaneously performs intra-modality missingness and inter-modality missingness. S and Ŝ 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 Ht and Hs 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.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 ) + P E(T α , d) ∈ R Tα×d . The R α is fed into a Transformer [46] encoder F α (•), and the last element of its output is denoted asZ α = F α (R α ) ∈ R d . The Z α ∈ Z αis 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 M α (Z α ). E S α (•) and E M α (•) 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.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:EQUATIONEQUATIONwhereZ αβ = D r (E S α (Z α ), E M β (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:EQUATIONwhereQ βα = E S α D r E S β (Z β ) , E M α (Z α )is the sentiment-relevant representation derived from the reconstructed modality representation. Consequently, the final loss of the FRF is denoted as:EQUATIONThe 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 3d with w ∈ {t, s}, yielding Hw ∈ R 3d . Moreover, we obtain the intermediate multi-scale representations of all layers, denoted asI w 1 ∈ R 2d , I w 2 ∈ R d , and I w 3 ∈ R 2d .For the above five representations, we concatenate features of the same scale to obtain multi-scale representationsE w 1 ∈ R 3d , E w 2 ∈ R 2d, 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:EQUATIONWe 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).EQUATIONwhere 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:EQUATIONwhere 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:EQUATIONConsidering 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 ofE w 1 ∈ R 3d , E w 2 ∈ R 2d, 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:EQUATIONThe Ht and Hs 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 asL total = L task + L F RF + L HM I + L HAL + L KL .4 ExperimentsQuestion: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof? • The answer NA means that there is no societal impact of the work performed.• If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.• Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations. • The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster. • 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). 
datasets and evaluation metrics: 
 We conduct our experiments on three MSA benchmarks, including MOSI [71] , MOSEI [72] , and IEMOCAP [4] . [48] , four emotions (i.e., happy, sad, angry, and neutral) are selected for emotion recognition. The F1 score is used as the metric.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 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 {1e - 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 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. 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 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.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,  
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.NeurIPS Paper Checklist 
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 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.Guidelines:• 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. • Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images. • 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.12. Licenses for existing assets 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.Guidelines:• The answer NA means that the paper does not use existing assets.• The authors should cite the original paper that produced the code package or dataset.• The authors should state which version of the asset is used and, if possible, include a URL. • The name of the license (e.g., CC-BY 4.0) should be included for each asset.• For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.• If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswithcode.com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset. • For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided. • If this information is not available online, the authors are encouraged to reach out to the asset's creators. 
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 Appendix:  


Decision: The paper studies the problem of incomplete multimodal sentiment analysis, where some modalities may be missing. The authors propose Representation Factorization and Alignment (ReFA) to tackle the issue of uncertain missing modalities in multimodal sentiment analysis (MSA). The proposed ReFA includes three components: Fine-grained Representation Factorization (FRF) module, Hierarchical Mutual Information (HMI) maximization mechanism, and Hierarchical Adversarial Learning (HAL) mechanism. Experiments are conducted on three datasets, demonstrating that the ReFA framework significantly improves MSA performance under both uncertain missing-modality and complete-modality testing conditions.

This paper received four reviews, including 2 strong accept, 1 accept, and 1 borderline accept. The reviewers recognized the novelty and contributions of this paper, such as practical issues, effective algorithms, comprehensive experiments, and good structure. 

Meanwhile, the reviewers pointed out some concerns and suggestions to further improve the quality of this paper, including but not limited to: lack of a detailed discussion on the computational complexity and runtime performance of the proposed framework, missing discussion of potential limitations, incremental technical contributions, lack of differentiation in handling the unique characteristics of each type of information, lack of relevant case studies, and too brief problem definition. The presentation of this paper also needs to be improved.

The authors provided meaningful responses to the reviewers' comments and concerns. I recommend acceptance of this paper and strongly recommend the authors take the reviewers' comments and the promised revisions into consideration when preparing the final version.

Reviews: review: paper_summary: paper_summary: The 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. 

The ReFA framework consists of three core components:

1. 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.

2. 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.

3. Hierarchical Adversarial Learning (HAL) mechanism: This mechanism progressively aligns and adapts the latent distributions of the representations to produce robust joint multimodal representations.

The 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.
strength_weakness: strength_weakness: 1. The paper lacks a detailed discussion on the computational complexity and runtime performance of the proposed framework compared to existing methods.
2. 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.
3. The paper did not mention the models that were used for the final classification or regression, only mentioned feature extraction models. 
4. The paper doesn't discuss potential limitations of the approach or cases where it might not perform well.
5. There's no discussion on the framework's generalizability to other multimodal tasks beyond sentiment analysis.
questions: questions: 1. Can the authors provide more details on the computational requirements and training time of ReFA compared to baseline methods?
2. 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?
3. Have the authors explored the applicability of ReFA to other multimodal tasks beyond sentiment analysis? If not, what modifications might be needed?
4. Could the authors provide insights into why the language modality seems to be particularly effective in unimodal scenarios?
5. Are there any scenarios or types of data where ReFA might not perform well? It would be helpful to discuss potential limitations.
6. 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?
limitations: limitations: The paper does not explicitly address limitations or potential negative societal impacts of the work. Some suggestions for improvement:
1. 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.
2. Discuss potential biases in the datasets used and how they might affect the model's performance across different demographic groups.
3. Acknowledge any limitations in the generalizability of the results to real-world, non-curated data.
paper_summary: paper_summary: The 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.
strength_weakness: strength_weakness: Weakness:

1. 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.

2. 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.

3. 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.

4. 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.

5. 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.
questions: questions: 1. 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.

2. 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.

3. 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.

4. 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.

5. 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.
limitations: limitations: The 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.
paper_summary: paper_summary: The 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.
strength_weakness: strength_weakness: The 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.
questions: questions: Extending the approach to consider scenarios where visual or auditory data might be dominant could improve the versatility and applicability of the model.

More detailed ablation studies on MOSEI and SIMS would provide deeper insights into the workings and benefits of the proposed model.
limitations: limitations: yes
paper_summary: paper_summary: The paper addresses the issue of missing modality information in multimodal systems and proposes solutions for two key problems:
1. Excessively complex feature interactions lead to information redundancy and cumulative errors.
2. Previous works did not align representations semantically.

In 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.
The model achieves state-of-the-art (SOTA) performance and includes detailed ablation studies and experimental analyses.
strength_weakness: strength_weakness: 1. The use of numerous symbols in both the figures and text increases the complexity and time required for understanding. DIFFICULT TO READ
2. 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.
3. 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?
4. The paper does not consider using large models to address this problem, nor does it compare the performance with that of large models.
5. The HMI (Hierarchical Mutual Information) module applies Bengio's MI concepts rather straightforwardly, lacking in innovation.
6. 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.
questions: questions: Application 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?
Model Design: Why is it necessary to include a modality encoder in the model? Ensuring semantic interactions across different modalities should be sufficient.
Did the baselines you compared against also use the SAME masking strategy as you did?
limitations: limitations: See Weaknesses and Questions.

Comments: title: Response to Reviewer MG71 zmpp
Maintaining Positive Assessment After Thorough Rebuttal
Response to Reviewer JfYH
comment: Thank you for the valuable suggestions and recognition of our work. 

We promise to add the following to the revision: additional experiments, a description of the models used for classification or regression, a discussion of potential societal impacts, an analysis of potential biases in the dataset, and an acknowledgement of the generalizability of the results. 

We will endeavor to make more valuable contributions to the multimodal community.
Thank you for your comprehensive rebuttal. I appreciate the time and effort you have invested in addressing each point raised in my review. After carefully considering your responses, I still hold a positive assessment of the paper.

Regarding computational complexity (Q1), I am pleased to see that you have included a comparison of parameters and FLOPs in the Appendix, which effectively addresses my concerns about computational requirements. On the topic of classification and regression models (Q3), I appreciate the details provided about the models used. Including this information in the main text will undoubtedly enhance clarity.

I am also glad to hear that you will be adding discussions on social impacts and dataset bias. These additions will significantly improve the comprehensiveness of the paper. Your acknowledgment of potential performance loss in more complex real-world scenarios demonstrates a balanced perspective on the result's generalizability.

Overall, your response has effectively addressed my concerns and questions. The additional experiments and planned revisions will further strengthen the paper, resulting in a more comprehensive and impactful contribution to the field of multimodality. In light of your thorough response and planned changes, I maintain my original assessment of the paper.
We thank the reviewer for the meticulous advice!
Thank you for your response and thorough explanations. I have revised my rating to 8.