title: Dialog2Flow: Pre-training Soft-Contrastive Action-Driven Sentence
    Embeddings for Automatic Dialog Flow Extraction
summary: Efficiently deriving structured workflows from unannotated dialogs remains an
  underexplored and formidable challenge in computational linguistics. Automating
  this process could significantly accelerate the manual design of workflows in
  new domains and enable the grounding of large language models in
  domain-specific flowcharts, enhancing transparency and controllability. In this
  paper, we introduce Dialog2Flow (D2F) embeddings, which differ from
  conventional sentence embeddings by mapping utterances to a latent space where
  they are grouped according to their communicative and informative functions
  (i.e., the actions they represent). D2F allows for modeling dialogs as
  continuous trajectories in a latent space with distinct action-related regions.
  By clustering D2F embeddings, the latent space is quantized, and dialogs can be
  converted into sequences of region/action IDs, facilitating the extraction of
  the underlying workflow. To pre-train D2F, we build a comprehensive dataset by
  unifying twenty task-oriented dialog datasets with normalized per-turn action
  annotations. We also introduce a novel soft contrastive loss that leverages the
  semantic information of these actions to guide the representation learning
  process, showing superior performance compared to standard supervised
  contrastive loss. Evaluation against various sentence embeddings, including
  dialog-specific ones, demonstrates that D2F yields superior qualitative and
  quantitative results across diverse domains.
paper_authors._id: 671b671a25260fa066e1a4da
paper_authors.name: Sergio Burdisso
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-10-25T09:38:42.902Z
paper_authors._id: 61d626d9e3a234cfcb617dca
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/61d626d9e3a234cfcb617dca/kJD3oxckDgiaEnYwGth6p.jpeg
paper_authors.fullname: Sergio Burdisso
paper_authors.type: user
paper_authors.user: sergioburdisso
paper_authors._id: 671b671a25260fa066e1a4db
paper_authors.name: Srikanth Madikeri
paper_authors._id: 671b671a25260fa066e1a4dc
paper_authors.name: Petr Motlicek
paper_title: Dialog2Flow: Pre-training Soft-Contrastive Action-Driven Sentence
    Embeddings for Automatic Dialog Flow Extraction
paper_summary: Efficiently deriving structured workflows from unannotated dialogs remains an
  underexplored and formidable challenge in computational linguistics. Automating
  this process could significantly accelerate the manual design of workflows in
  new domains and enable the grounding of large language models in
  domain-specific flowcharts, enhancing transparency and controllability. In this
  paper, we introduce Dialog2Flow (D2F) embeddings, which differ from
  conventional sentence embeddings by mapping utterances to a latent space where
  they are grouped according to their communicative and informative functions
  (i.e., the actions they represent). D2F allows for modeling dialogs as
  continuous trajectories in a latent space with distinct action-related regions.
  By clustering D2F embeddings, the latent space is quantized, and dialogs can be
  converted into sequences of region/action IDs, facilitating the extraction of
  the underlying workflow. To pre-train D2F, we build a comprehensive dataset by
  unifying twenty task-oriented dialog datasets with normalized per-turn action
  annotations. We also introduce a novel soft contrastive loss that leverages the
  semantic information of these actions to guide the representation learning
  process, showing superior performance compared to standard supervised
  contrastive loss. Evaluation against various sentence embeddings, including
  dialog-specific ones, demonstrates that D2F yields superior qualitative and
  quantitative results across diverse domains.
paper_ai_keywords: Dialog2Flow (D2F) embeddings latent space communicative and informative functions continuous trajectories action-related regions soft contrastive loss semantic information representation learning process sentence embeddings dialog-specific embeddings