Past, Present, and Future: Conversational Emotion Recognition through Structural Modeling of Psychological Knowledge

Li, Jiangnan; Lin, Zheng; Fu, Peng; Wang, Weiping

doi:10.18653/v1/2021.findings-emnlp.104

Cited by 44 publications

(22 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[9] uses GCN to solve the problem of context propagation in existing GRU-based methods. Commonsense knowledge [8], psychological knowledge [18], and cognitive theory of emotion [14] are also used to enhance dialogue emotion recognition. Emotional Dialogue.…”

Section: Related Workmentioning

confidence: 99%

Think Twice: A Human-like Two-stage Conversational Agent for Emotional Response Generation

Qian¹,

Wang²,

Ma³

et al. 2023

Preprint

View full text Add to dashboard Cite

Towards human-like dialogue systems, current emotional dialogue approaches jointly model emotion and semantics with a unified neural network. This strategy tends to generate safe responses due to the mutual restriction between emotion and semantics, and requires the rare large-scale emotion-annotated dialogue corpus. Inspired by the "think twice" behavior in human intelligent dialogue, we propose a two-stage conversational agent for the generation of emotional dialogue. Firstly, a dialogue model trained without the emotion-annotated dialogue corpus generates a prototype response that meets the contextual semantics. Secondly, the first-stage prototype is modified by a controllable emotion refiner with the empathy hypothesis. Experimental results on the DailyDialog and Empathet-icDialogues datasets demonstrate that the proposed conversational agent outperforms the compared models in the emotion generation and maintains the semantic performance in the automatic and human evaluations.

show abstract

Section: Related Workmentioning

confidence: 99%

Think Twice: A Human-like Two-stage Conversational Agent for Emotional Response Generation

Qian¹,

Wang²,

Ma³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…NRC_VAD Lexicon has human ratings of valence, arousal, and dominance for more than 20,000 English words. COSMIC (Ghosal et al, 2020) and Psychological (Li et al, 2021) improve the performance of emotion recognition by extracting commonsense knowledge of the previous utterances. Commonsense knowledge feature is extracted and leveraged with COMET (Bosselut et al, 2019) trained with ATOMIC (The Atlas of Machine Commonsense) .…”

Section: Related Workmentioning

confidence: 99%

“…Psychological (Li et al, 2021) uses commonsense knowledge as enrich edges and processes it with graph transformer. Psychological performs emotion recognition by utilizing intention of utterances from not only past contexts but also future context.…”

Section: Ermc-disgcnmentioning

confidence: 99%

CoMPM: Context Modeling with Speaker’s Pre-trained Memory Tracking for Emotion Recognition in Conversation

Lee¹,

Lee²

2022

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

View full text Add to dashboard Cite

As the use of interactive machines grow, the task of Emotion Recognition in Conversation (ERC) became more important. If the machine-generated sentences reflect emotion, more human-like sympathetic conversations are possible. Since emotion recognition in conversation is inaccurate if the previous utterances are not taken into account, many studies reflect the dialogue context to improve the performances. Many recent approaches show performance improvement by combining knowledge into modules learned from external structured data. However, structured data is difficult to access in non-English languages, making it difficult to extend to other languages. Therefore, we extract the pre-trained memory using the pre-trained language model as an extractor of external knowledge. We introduce CoMPM, which combines the speaker's pre-trained memory with the context model, and find that the pre-trained memory significantly improves the performance of the context model. CoMPM achieves the first or second performance on all data and is state-of-the-art among systems that do not leverage structured data. In addition, our method shows that it can be extended to other languages because structured knowledge is not required, unlike previous methods. Our code is available on github 1 .

show abstract

“…Regarding neural models, one promising approach, beginning to be found in the literature, is to integrate social science-based rules as features in the encoding stage. Similarly pre-trained representations using a knowledge base from the social science literature can be integrated into graph neural models ( Li et al, 2021 ). In addition, these hybrid approaches have the benefit of allowing greater explainability of the output.…”

Section: Challenge 2: Mixing Neural and Social-science Derived Modelsmentioning

confidence: 99%

Socio-conversational systems: Three challenges at the crossroads of fields

2022

View full text Add to dashboard Cite

Socio-conversational systems are dialogue systems, including what are sometimes referred to as chatbots, vocal assistants, social robots, and embodied conversational agents, that are capable of interacting with humans in a way that treats both the specifically social nature of the interaction and the content of a task. The aim of this paper is twofold: 1) to uncover some places where the compartmentalized nature of research conducted around socio-conversational systems creates problems for the field as a whole, and 2) to propose a way to overcome this compartmentalization and thus strengthen the capabilities of socio-conversational systems by defining common challenges. Specifically, we examine research carried out by the signal processing, natural language processing and dialogue, machine/deep learning, social/affective computing and social sciences communities. We focus on three major challenges for the development of effective socio-conversational systems, and describe ways to tackle them.

show abstract

Past, Present, and Future: Conversational Emotion Recognition through Structural Modeling of Psychological Knowledge

Cited by 44 publications

References 24 publications

Think Twice: A Human-like Two-stage Conversational Agent for Emotional Response Generation

Think Twice: A Human-like Two-stage Conversational Agent for Emotional Response Generation

CoMPM: Context Modeling with Speaker’s Pre-trained Memory Tracking for Emotion Recognition in Conversation

Socio-conversational systems: Three challenges at the crossroads of fields

Contact Info

Product

Resources

About