Context-Dependent Embedding Utterance Representations for Emotion Recognition in Conversations

• URL: http://arxiv.org/abs/2304.08216v2
• Date: Sat, 3 Jun 2023 07:43:12 GMT
• Title: Context-Dependent Embedding Utterance Representations for Emotion Recognition in Conversations
• Authors: Patr\'icia Pereira, Helena Moniz, Isabel Dias and Joao Paulo Carvalho
• Abstract summary: We approach Emotion Recognition in Conversations leveraging the conversational context. We propose context-dependent embedding representations of each utterance. The effectiveness of our approach is validated on the open-domain DailyDialog dataset and on the task-oriented EmoWOZ dataset.
• Score: 1.8126187844654875
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Emotion Recognition in Conversations (ERC) has been gaining increasing importance as conversational agents become more and more common. Recognizing emotions is key for effective communication, being a crucial component in the development of effective and empathetic conversational agents. Knowledge and understanding of the conversational context are extremely valuable for identifying the emotions of the interlocutor. We thus approach Emotion Recognition in Conversations leveraging the conversational context, i.e., taking into attention previous conversational turns. The usual approach to model the conversational context has been to produce context-independent representations of each utterance and subsequently perform contextual modeling of these. Here we propose context-dependent embedding representations of each utterance by leveraging the contextual representational power of pre-trained transformer language models. In our approach, we feed the conversational context appended to the utterance to be classified as input to the RoBERTa encoder, to which we append a simple classification module, thus discarding the need to deal with context after obtaining the embeddings since these constitute already an efficient representation of such context. We also investigate how the number of introduced conversational turns influences our model performance. The effectiveness of our approach is validated on the open-domain DailyDialog dataset and on the task-oriented EmoWOZ dataset.

Related papers

• Context Does Matter: Implications for Crowdsourced Evaluation Labels in Task-Oriented Dialogue Systems [57.16442740983528]
  Crowdsourced labels play a crucial role in evaluating task-oriented dialogue systems. Previous studies suggest using only a portion of the dialogue context in the annotation process. This study investigates the influence of dialogue context on annotation quality.
  arXiv  Detail & Related papers  (2024-04-15T17:56:39Z)
• Token Trails: Navigating Contextual Depths in Conversational AI with ChatLLM [0.5743699972363359]
  Token Trails is a novel approach that leverages token-type embeddings to navigate the contextual nuances within conversations. Our framework utilizes token-type embeddings to distinguish between user utterances and bot responses, facilitating the generation of context-aware replies.
  arXiv  Detail & Related papers  (2024-04-03T02:11:39Z)
• Paralinguistics-Enhanced Large Language Modeling of Spoken Dialogue [71.15186328127409]
  Paralinguistics-enhanced Generative Pretrained Transformer (ParalinGPT) Model takes the conversational context of text, speech embeddings, and paralinguistic attributes as input prompts within a serialized multitasking framework. We utilize the Switchboard-1 corpus, including its sentiment labels as the paralinguistic attribute, as our spoken dialogue dataset.
  arXiv  Detail & Related papers  (2023-12-23T18:14:56Z)
• Multiscale Contextual Learning for Speech Emotion Recognition in Emergency Call Center Conversations [4.297070083645049]
  This paper presents a multi-scale conversational context learning approach for speech emotion recognition. We investigated this approach on both speech transcriptions and acoustic segments. According to our tests, the context derived from previous tokens has a more significant influence on accurate prediction than the following tokens.
  arXiv  Detail & Related papers  (2023-08-28T20:31:45Z)
• deep learning of segment-level feature representation for speech emotion recognition in conversations [9.432208348863336]
  We propose a conversational speech emotion recognition method to deal with capturing attentive contextual dependency and speaker-sensitive interactions. First, we use a pretrained VGGish model to extract segment-based audio representation in individual utterances. Second, an attentive bi-directional recurrent unit (GRU) models contextual-sensitive information and explores intra- and inter-speaker dependencies jointly.
  arXiv  Detail & Related papers  (2023-02-05T16:15:46Z)
• Channel-aware Decoupling Network for Multi-turn Dialogue Comprehension [81.47133615169203]
  We propose compositional learning for holistic interaction across utterances beyond the sequential contextualization from PrLMs. We employ domain-adaptive training strategies to help the model adapt to the dialogue domains. Experimental results show that our method substantially boosts the strong PrLM baselines in four public benchmark datasets.
  arXiv  Detail & Related papers  (2023-01-10T13:18:25Z)
• FCTalker: Fine and Coarse Grained Context Modeling for Expressive Conversational Speech Synthesis [75.74906149219817]
  Conversational Text-to-Speech (TTS) aims to synthesis an utterance with the right linguistic and affective prosody in a conversational context. We propose a novel expressive conversational TTS model, as termed FCTalker, that learn the fine and coarse grained context dependency at the same time during speech generation.
  arXiv  Detail & Related papers  (2022-10-27T12:20:20Z)
• Emotion Dynamics Modeling via BERT [7.3785751096660555]
  We develop a series of BERT-based models to capture the inter-interlocutor and intra-interlocutor dependencies of the conversational emotion dynamics. Our proposed models can attain around 5% and 10% improvement over the state-of-the-art baselines, respectively.
  arXiv  Detail & Related papers  (2021-04-15T05:58:48Z)
• AdCOFE: Advanced Contextual Feature Extraction in Conversations for emotion classification [0.29360071145551075]
  The proposed model of Advanced Contextual Feature Extraction (AdCOFE) addresses these issues. Experiments on the Emotion recognition in conversations dataset show that AdCOFE is beneficial in capturing emotions in conversations.
  arXiv  Detail & Related papers  (2021-04-09T17:58:19Z)
• Dialogue History Matters! Personalized Response Selectionin Multi-turn Retrieval-based Chatbots [62.295373408415365]
  We propose a personalized hybrid matching network (PHMN) for context-response matching. Our contributions are two-fold: 1) our model extracts personalized wording behaviors from user-specific dialogue history as extra matching information. We evaluate our model on two large datasets with user identification, i.e., personalized dialogue Corpus Ubuntu (P- Ubuntu) and personalized Weibo dataset (P-Weibo)
  arXiv  Detail & Related papers  (2021-03-17T09:42:11Z)
• Filling the Gap of Utterance-aware and Speaker-aware Representation for Multi-turn Dialogue [76.88174667929665]
  A multi-turn dialogue is composed of multiple utterances from two or more different speaker roles. In the existing retrieval-based multi-turn dialogue modeling, the pre-trained language models (PrLMs) as encoder represent the dialogues coarsely. We propose a novel model to fill such a gap by modeling the effective utterance-aware and speaker-aware representations entailed in a dialogue history.
  arXiv  Detail & Related papers  (2020-09-14T15:07:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.