Dialogue Understanding
29 papers with code • 0 benchmarks • 9 datasets
Benchmarks
These leaderboards are used to track progress in Dialogue Understanding
Datasets
Most implemented papers
Adding Chit-Chat to Enhance Task-Oriented Dialogues
Existing dialogue corpora and models are typically designed under two disjoint motives: while task-oriented systems focus on achieving functional goals (e. g., booking hotels), open-domain chatbots aim at making socially engaging conversations.
CREAD: Combined Resolution of Ellipses and Anaphora in Dialogues
In this work, we propose a novel joint learning framework of modeling coreference resolution and query rewriting for complex, multi-turn dialogue understanding.
Semantic Representation for Dialogue Modeling
Although neural models have achieved competitive results in dialogue systems, they have shown limited ability in representing core semantics, such as ignoring important entities.
A Structure Self-Aware Model for Discourse Parsing on Multi-Party Dialogues
Conversational discourse structures aim to describe how a dialogue is organised, thus they are helpful for dialogue understanding and response generation.
M2H2: A Multimodal Multiparty Hindi Dataset For Humor Recognition in Conversations
We propose several strong multimodal baselines and show the importance of contextual and multimodal information for humor recognition in conversations.
DialogLM: Pre-trained Model for Long Dialogue Understanding and Summarization
For a dialogue, it corrupts a window of text with dialogue-inspired noise, and guides the model to reconstruct this window based on the content of the remaining conversation.
CSAGN: Conversational Structure Aware Graph Network for Conversational Semantic Role Labeling
Conversational semantic role labeling (CSRL) is believed to be a crucial step towards dialogue understanding.
A Benchmark for Automatic Medical Consultation System: Frameworks, Tasks and Datasets
In recent years, interest has arisen in using machine learning to improve the efficiency of automatic medical consultation and enhance patient experience.
User-Centric Conversational Recommendation with Multi-Aspect User Modeling
In this work, we highlight that the user's historical dialogue sessions and look-alike users are essential sources of user preferences besides the current dialogue session in CRS.
FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue
Task transfer, transferring knowledge contained in related tasks, holds the promise of reducing the quantity of labeled data required to fine-tune language models.