| Challenge: | Existing methods for predicting how events induce emotions ignore the semantics of the labels themselves. |
| Approach: | They propose that the semantics of emotion labels can guide a model’s attention when representing the input story. |
| Outcome: | The proposed model can model the semantics of emotion labels and track correlations on unlabeled data. |
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Towards Label-Agnostic Emotion Embeddings (2021.emnlp-main)
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| Challenge: | Existing representation schemes for emotion analysis are based on label formats, natural languages, and even disparate model architectures. |
| Approach: | They propose a training scheme that learns a shared latent representation of emotion independent from different label formats, natural languages, and even disparate model architectures. |
| Outcome: | The proposed model performs well on a wide range of datasets without penalizing prediction quality. |
SRL4E – Semantic Role Labeling for Emotions: A Unified Evaluation Framework (2022.acl-long)
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| Challenge: | Existing datasets for emotion detection are heterogeneous in size, domain, format, splits, emotion categories and role labels, hampering progress in this area. |
| Approach: | They propose a framework for annotating emotions manually using a common labeling scheme to unify several datasets tagged with emotions and semantic roles. |
| Outcome: | The proposed framework unifies datasets tagged with emotions and semantic roles by using a common labeling scheme. |
Guilt by Association: Emotion Intensities in Lexical Representations (2021.emnlp-main)
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| Challenge: | linguistic models have a higher correlation with human ground truth ratings than labeled data . word vectors have often been evaluated on standard word relatedness benchmarks . |
| Approach: | They propose to use unsupervised, supervised, and finally supervised methods to extract emotional associations from pretrained vectors and models. |
| Outcome: | The proposed method shows higher correlation with ground truth ratings than state-of-the-art lexicons based on labeled data. |
Language Models (Mostly) Do Not Consider Emotion Triggers When Predicting Emotion (2024.naacl-short)
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| Challenge: | Existing work has sought to identify what triggers or causes a particular emotion, but the relationship between those triggers and the prediction of emotion detection models is little understood. |
| Approach: | They propose a dataset to evaluate the ability of large language models to identify emotion triggers . they compare features considered important for emotion prediction models to those considered less salient . |
| Outcome: | The proposed dataset compares large language models and fine-tuned models on social media posts . it shows that emotion triggers are not considered salient features for emotion prediction models . |
x-enVENT: A Corpus of Event Descriptions with Experiencer-specific Emotion and Appraisal Annotations (2022.lrec-1)
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| Challenge: | Emotion classification is often formulated as the task to categorize texts into a predefined set of emotion classes. |
| Approach: | They propose that a classification setup for emotion analysis should be performed in an integrated manner, including the different semantic roles that participate in an emotion episode. |
| Outcome: | The proposed method reveals patterns in the co-occurrence of people’s emotions in interaction. |
SpanEmo: Casting Multi-label Emotion Classification as Span-prediction (2021.eacl-main)
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| Challenge: | Current approaches to ER ignore potential ambiguities, in which multiple emotions overlap. |
| Approach: | They propose a model "SpanEmo" which casts multi-label emotion classification as span-prediction and introduces a loss function focused on modelling multiple co-existing emotions in a sentence. |
| Outcome: | The proposed model can predict multiple co-existing emotions in a sentence and improve model performance and learning meaningful associations between labels and words in the sentence. |
Multi-modal Multi-label Emotion Detection with Modality and Label Dependence (2020.emnlp-main)
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| Challenge: | Existing studies on multi-label emotion detection focus on one modality . current studies focus on label dependence, but there is no consensus on the model . |
| Approach: | They propose a multi-modal sequence-to-set approach to model label dependence and modality dependence in a multiple-modal scenario. |
| Outcome: | The proposed approach is able to model the label dependence and the modality dependence in a multi-modal scenario. |
Seq2Emo: A Sequence to Multi-Label Emotion Classification Model (2021.naacl-main)
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| Challenge: | Existing methods for multi-label emotion classification are based on binary relevance and classifier chain (CC) |
| Approach: | They propose a sequence-to-emotion approach which implicitly models emotion correlations in a bi-directional decoder. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on a SemEval’18 and GoEmotions dataset. |
Modelling Context Emotions using Multi-task Learning for Emotion Controlled Dialog Generation (2021.eacl-main)
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| Challenge: | Recent research has tackled this task using neural generative methods by augmenting emotion classes with the input sequences. |
| Approach: | They propose to use a self-attention based encoder and a decoder with dot product attention mechanism to generate a viable response with a specified emotion. |
| Outcome: | The proposed model outperforms baselines on automatic evaluation measures such as F1 and BLEU scores, thus resulting in more fluent and adequate responses. |
An Emotional Mess! Deciding on a Framework for Building a Dutch Emotion-Annotated Corpus (2020.lrec-1)
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| Challenge: | Existing frameworks for emotion recognition are limited and do not allow for categorical versus dimensional oppositions. |
| Approach: | They propose to use the emotions joy, love, anger, sadness and fear as well as dimensional models to annotate texts from different domains and topics. |
| Outcome: | The proposed frameworks are well-suited to annotate texts from different domains and topics, but the connotation of the labels strongly depends on the origin of the texts. |