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Found 9 papers, 0 papers with code
Improving Out-of-Scope Detection in Intent Classification by Using Embeddings of the Word Graph Space of the Classes
This paper explores how intent classification can be improved by representing the class labels not as a discrete set of symbols but as a space where the word graphs associated to each class are mapped using typical graph embedding techniques.
Exploring the Advantages of Dense-Vector to One-Hot Encoding of Intent Classes in Out-of-Scope Detection Tasks
This work explores the intrinsic limitations of the popular one-hot encoding method in classification of intents when detection of out-of-scope (OOS) inputs is required.
Using Meta-Knowledge Mined from Identifiers to Improve Intent Recognition in Conversational Systems
In this paper we explore the improvement of intent recognition in conversational systems by the use of meta-knowledge embedded in intent identifiers.
Using Meta-Knowledge Mined from Identifiers to Improve Intent Recognition in Neuro-Symbolic Algorithms
By using neuro-symbolic algorithms able to incorporate such proto-taxonomies to expand intent representation, we show that such mined meta-knowledge can improve accuracy in intent recognition.
From Disjoint Sets to Parallel Data to Train Seq2Seq Models for Sentiment Transfer
We present a method for creating parallel data to train Seq2Seq neural networks for sentiment transfer.
A Hybrid Solution to Learn Turn-Taking in Multi-Party Service-based Chat Groups
To predict the next most likely participant to interact in a multi-party conversation is a difficult problem.
Learning Multi-Party Turn-Taking Models from Dialogue Logs
This paper investigates the application of machine learning (ML) techniques to enable intelligent systems to learn multi-party turn-taking models from dialogue logs.
Different but Equal: Comparing User Collaboration with Digital Personal Assistants vs. Teams of Expert Agents
This work compares user collaboration with conversational personal assistants vs. teams of expert chatbots.
A Hybrid Architecture for Multi-Party Conversational Systems
Multi-party Conversational Systems are systems with natural language interaction between one or more people or systems.
