| Challenge: | Existing systems that estimate user preferences only in static manners or exploit interaction history are inadequate to accurately assess user preferences. |
| Approach: | They propose to integrate rank consistent ordinal regression into a speech-only sentiment prediction task performed by ResNet-like systems and use speaker verification extractors trained on larger datasets as low-level feature extractor. |
| Outcome: | The proposed system beats state-of-the-art unimodal systems on multimodal Opinion Sentiment and Emotion Intensity databases. |
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Eliminating Sentiment Bias for Aspect-Level Sentiment Classification with Unsupervised Opinion Extraction (2021.findings-emnlp)
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| Challenge: | Aspect-level sentiment classification (ALSC) is a practical setting in aspect-based sentiment analysis due to no opinion term labeling needed, but it fails to interpret why a sentiment polarity is derived for the aspect. |
| Approach: | They propose a span-based anti-bias aspect representation learning framework that eliminates the sentiment bias in the aspect embedding by adversarial learning against aspects’ prior sentiment. |
| Outcome: | The proposed framework achieves state-of-the-art performance on five benchmarks, with the capability of unsupervised opinion extraction. |
Sentiment Analysis for Emotional Speech Synthesis in a News Dialogue System (2020.coling-main)
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| Challenge: | In smart speakers and conversational robots, the demand for expressive speech synthesis has increased. |
| Approach: | They propose to annotate a news dataset with emotion labels for each sentence and to evaluate its effectiveness using the constructed dataset. |
| Outcome: | The proposed method improves the performance of the proposed model by preferentially annotating news articles with low confidence in the human-in-the-loop machine learning framework. |
Self-supervised Cross-modal Pretraining for Speech Emotion Recognition and Sentiment Analysis (2022.findings-emnlp)
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| Challenge: | Existing approaches to multimodal speech emotion recognition and sentiment analysis have not improved results due to their relatively simple fusion mechanisms and lack of proper cross-modal pretraining. |
| Approach: | They propose a deep-fused audio-text bi-modal transformer with carefully designed cross-modal fusion mechanism and stage-wise cross-mod pretraining scheme to facilitate cross-modulation. |
| Outcome: | The proposed method exceeds benchmarks on public IEMOCAP emotion and CMU-MOSEI sentiment datasets by a large margin. |
Syntactically Aware Cross-Domain Aspect and Opinion Terms Extraction (2020.coling-main)
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| Challenge: | Supervised-learning approaches fail to scale across domains where labeled data is lacking. |
| Approach: | They propose a method for incorporating external linguistic knowledge into a self-attention mechanism coupled with a transformer-based model. |
| Outcome: | The proposed method enables leveraging syntactic knowledge from transformer-based models to bridge the gap between domains. |
Sentiment Word Aware Multimodal Refinement for Multimodal Sentiment Analysis with ASR Errors (2022.findings-acl)
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| Challenge: | Existing models for multimodal sentiment analysis are limited in their capacity to be deployed in the real world. |
| Approach: | They propose a model that can dynamically refine erroneous sentiment words by leveraging multimodal sentiment clues. |
| Outcome: | The proposed model surpasses the state-of-the-art models on three datasets. |
Opinion Tree Parsing for Aspect-based Sentiment Analysis (2023.findings-acl)
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| Challenge: | Existing generative models for aspect-based sentiment analysis lack structure well-formedness guarantees and built-in elements alignments. |
| Approach: | They propose an opinion tree parsing model which parses all sentiment elements from an opinion-tree. |
| Outcome: | The proposed model is much faster than previous models and can explore correlations among sentiment elements. |
AMOA: Global Acoustic Feature Enhanced Modal-Order-Aware Network for Multimodal Sentiment Analysis (2022.coling-1)
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| Challenge: | Existing methods treat three modal features equally, without distinguishing the importance of different modalities. Existing models split the video into frames, leading to missing the global acoustic information. |
| Approach: | They propose a global Acoustic feature enhanced Modal-Order-Aware network to address these problems. |
| Outcome: | The proposed model outperforms state-of-the-art models on two public datasets. |
Contextual Inter-modal Attention for Multi-modal Sentiment Analysis (D18-1)
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Deepanway Ghosal, Md Shad Akhtar, Dushyant Chauhan, Soujanya Poria, Asif Ekbal, Pushpak Bhattacharyya
| Challenge: | Existing methods for multi-modal sentiment analysis are limited due to the use of text, visual and acoustic inputs. |
| Approach: | They propose a recurrent neural network based multi-modal attention framework that leverages contextual information for utterance-level sentiment prediction. |
| Outcome: | The proposed framework performs better on two multi-modal sentiment analysis benchmark datasets with accuracies of 82.31% and 79.80% for the MOSI and MOSEI datasets. |
A Challenge Dataset and Effective Models for Aspect-Based Sentiment Analysis (D19-1)
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| Challenge: | Existing ABSA methods only use one aspect or multiple aspects with the same sentiment polarity . recent studies show that neural network methods can be trained end-to-end and automatically learn important features. |
| Approach: | They propose a large-scale multi-aspect multi-sentiment dataset with two different aspects with different sentiment polarities. |
| Outcome: | The proposed model outperforms the state-of-the-art models on the large-scale dataset . it is based on a novel neural network approach that can be trained end-to-end . |
Efficient Low-rank Multimodal Fusion With Modality-Specific Factors (P18-1)
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Zhun Liu, Ying Shen, Varun Bharadhwaj Lakshminarasimhan, Paul Pu Liang, AmirAli Bagher Zadeh, Louis-Philippe Morency
| Challenge: | Multimodal research is a growing field of artificial intelligence, and fusion is one of the main research problems. |
| Approach: | They propose a low-rank multimodal fusion method which integrates multiple unimodal representations into one compact multimodal representation. |
| Outcome: | The proposed method achieves competitive results on multimodal sentiment analysis, speaker trait analysis, and emotion recognition tasks while reducing computational complexity. |