| Challenge: | Social media are a gold mine for researchers in many domains and especially in natural language processing . license restrictions make it difficult to strictly reproduce research results on Twitter data . |
| Approach: | They propose to annotate a Twitter-like corpus from a decentralized social network with permissive licenses that are compatible with reproducible experiments. |
| Outcome: | The proposed method shows that transfer learning can be efficiently achieved between tasks. |
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| Challenge: | a huge amount of content is being generated every day due to the pervasiveness of social media. |
| Approach: | They firstly create a multi-domain tweet sentiment corpora and then establish a deep neural network based baseline framework to address the above mentioned issues. |
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An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis (P19-1)
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| Challenge: | Aspect-based sentiment analysis produces a list of aspect terms and their corresponding sentiments for a sentence. |
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Towards Sentiment and Emotion aided Multi-modal Speech Act Classification in Twitter (2021.naacl-main)
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| Challenge: | Speech Act Classification determining the communicative intent of an utterance has been investigated widely over the years as a standalone task. |
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DARER: Dual-task Temporal Relational Recurrent Reasoning Network for Joint Dialog Sentiment Classification and Act Recognition (2022.findings-acl)
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| Challenge: | Dialog sentiment classification (DSC) and dialog act recognition (DAR) aims to predict the sentiment label and act label for each utterance in a dialog. |
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Dialogue Act Classification with Context-Aware Self-Attention (N19-1)
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| Challenge: | Recent work in Dialogue Act classification has treated the task as a sequence labeling problem using hierarchical deep neural networks. |
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Multi-Task Learning Framework for Mining Crowd Intelligence towards Clinical Treatment (N18-2)
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| Challenge: | In recent past, social media has emerged as an active platform in the context of healthcare and medicine. |
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Modeling Local Contexts for Joint Dialogue Act Recognition and Sentiment Classification with Bi-channel Dynamic Convolutions (2020.coling-main)
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| Challenge: | a novel context-aware dynamic convolution network is proposed to better leverage the local contexts when dynamically generating convolution kernels. |
| Approach: | They propose a dynamic convolution network to leverage local contexts when generating convolution kernels. |
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Re-framing Incremental Deep Language Models for Dialogue Processing with Multi-task Learning (2020.coling-main)
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| Challenge: | Using a multi-task learning framework, we train a universal incremental dialogue processing model with four tasks of disfluency detection, language modelling, part-of-speech tagging and utterance segmentation in a simple deep recurrent setting. |
| Approach: | They propose a multi-task learning framework to train a universal incremental dialogue processing model with four tasks of disfluency detection, language modelling, part-of-speech tagging and utterance segmentation in a simple deep recurrent setting. |
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Emotion Detection and Classification in a Multigenre Corpus with Joint Multi-Task Deep Learning (C18-1)
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| Challenge: | Sentence-level emotion detection is a challenging task due to subjectivity of emotion. |
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Towards Emotion-aided Multi-modal Dialogue Act Classification (2020.acl-main)
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| Challenge: | Considerable work on Dialogue Act Classification (DAC) has been done on textual inputs. |
| Approach: | They propose to use a multimodal Emotion aware Dialogue Act dataset to explore the role of multi-modality and emotion recognition in DAC. |
| Outcome: | The proposed dataset shows that multi-modality and emotion recognition improves DAC performance compared to uni-modal and single task DAC variants. |