Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations

Zhong, Peixiang; Wang, Di; Chen, Miao

doi:10.48550/arxiv.1909.10681

Cited by 14 publications

(29 citation statements)

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“…RNN-based methods: CNN+cLSTM (Poria et al 2017), DialogueRNN (Majumder et al 2019) KET (Zhong, Wang, and Miao 2019): A transformer structure with hierarchical self-attention and external commonsense knowledge.…”

Section: Baseline Methodsmentioning

confidence: 99%

“…With the rise of Transformer and graph neural networks in NLP tasks, many works have also introduce them into the ERC task. Zhong, Wang, and Miao (2019) propose KET, which is a structure of hierarchical Transformers assisted by external commonsense knowledge. DialogueXL (Shen et al 2020) applies dialogue-aware self-attention to deal with the multiparty structures.…”

Section: Emotion Recognition In Conversationmentioning

confidence: 99%

“…For the evaluation metrics, we choose micro-averaged F1 for DailyDialog and weighted-average F1 for the other datasets, following previous works (Ishiwatari et al 2020;Shen et al 2021;Zhong, Wang, and Miao 2019).…”

Section: Datasetsmentioning

confidence: 99%

See 2 more Smart Citations

S+PAGE: A Speaker and Position-Aware Graph Neural Network Model for Emotion Recognition in Conversation

Chen¹,

Chong²,

Xu³

et al. 2021

Preprint

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Emotion recognition in conversation (ERC) has attracted much attention in recent years for its necessity in widespread applications. Existing ERC methods mostly model the self and inter-speaker context separately, posing a major issue for lacking enough interaction between them. In this paper, we propose a novel Speaker and Position-Aware Graph neural network model for ERC (S+PAGE), which contains three stages to combine the benefits of both Transformer and relational graph convolution network (R-GCN) for better contextual modeling. Firstly, a two-stream conversational Transformer is presented to extract the coarse self and inter-speaker contextual features for each utterance. Then, a speaker and position-aware conversation graph is constructed, and we propose an enhanced R-GCN model, called PAG, to refine the coarse features guided by a relative positional encoding. Finally, both of the features from the former two stages are input into a conditional random field layer to model the emotion transfer. Extensive experiments demonstrate that our model achieves state-of-the-art performance on three ERC datasets.

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Section: Baseline Methodsmentioning

confidence: 99%

Section: Emotion Recognition In Conversationmentioning

confidence: 99%

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S+PAGE: A Speaker and Position-Aware Graph Neural Network Model for Emotion Recognition in Conversation

Chen¹,

Chong²,

Xu³

et al. 2021

Preprint

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show abstract

“…Studies on ERC applied to text followed, mainly built on an artificial conversation dataset named DailyDialog (Li et al, 2017). (Zhong et al, 2019) incorporated a knowledge base into the network using context-aware attention and hierarchical self-attention using Transformers (Vaswani et al, 2017). Ghosal et al (2019) uses graph neural networks to deal with context propagation limitations.…”

Section: Related Workmentioning

confidence: 99%

“…We choose DailyDialog for comparison and reproducibility purposes, as it is often used for ERC. In this work, we use the train/val/test splits provided by (Zhong et al, 2019).…”

Section: Dailydialogmentioning

confidence: 99%

Few-Shot Emotion Recognition in Conversation with Sequential Prototypical Networks

Guibon,

Labeau,

Flamein

et al. 2021

Preprint

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Several recent studies on dyadic humanhuman interactions have been done on conversations without specific business objectives. However, many companies might benefit from studies dedicated to more precise environments such as after sales services or customer satisfaction surveys. In this work, we place ourselves in the scope of a live chat customer service in which we want to detect emotions and their evolution in the conversation flow. This context leads to multiple challenges that range from exploiting restricted, small and mostly unlabeled datasets to finding and adapting methods for such context. We tackle these challenges by using Few-Shot Learning while making the hypothesis it can serve conversational emotion classification for different languages and sparse labels. We contribute by proposing a variation of Prototypical Networks for sequence labeling in conversation that we name ProtoSeq. We test this method on two datasets with different languages: daily conversations in English and customer service chat conversations in French. When applied to emotion classification in conversations, our method proved to be competitive even when compared to other ones. The code for Proto-Seq is available at https://github.com/ gguibon/ProtoSeq.

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Directed Acyclic Graphs With Prototypical Networks for Few-Shot Emotion Recognition in Conversation

Kang,

Cho

2023

IEEE Access

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Emotion recognition in conversation is the task of recognizing the emotion in each utterance of a conversation, and it is an active field of study with various applications. Many studies have been conducted in well-designed settings in which all of the emotion labels in the training set are available. However, a rich dataset with emotion labels for all utterances is rare. In this study, we address this problem by resorting to few-shot learning. Specifically, we propose Directed Acyclic Graph (DAG)-based prototypical networks, namely ProtoDAG, to better capture contextual information in conversations, which in turn leads to accurate emotion predictions. In our model, the DAG layers are tailored into prototypical networks, which is learned end-to-end. Our experiments with popular benchmark datasets demonstrate that our model achieves stateof-the-art results outperforming the existing few-shot learning model by a significant margin and is even competitive with fully supervised baseline models for emotion recognition in conversation.

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Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations

Cited by 14 publications

References 50 publications

S+PAGE: A Speaker and Position-Aware Graph Neural Network Model for Emotion Recognition in Conversation

S+PAGE: A Speaker and Position-Aware Graph Neural Network Model for Emotion Recognition in Conversation

Few-Shot Emotion Recognition in Conversation with Sequential Prototypical Networks

Directed Acyclic Graphs With Prototypical Networks for Few-Shot Emotion Recognition in Conversation

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