Search Results for author:
Found 11 papers, 3 papers with code
FLAP: Flow-Adhering Planning with Constrained Decoding in LLMs
To study this, we propose the problem of faithful planning in TODs that needs to resolve user intents by following predefined flows and preserving API dependencies.
MAGID: An Automated Pipeline for Generating Synthetic Multi-modal Datasets
Development of multimodal interactive systems is hindered by the lack of rich, multimodal (text, images) conversational data, which is needed in large quantities for LLMs.
DeAL: Decoding-time Alignment for Large Language Models
Current work focuses on alignment at model training time, through techniques such as Reinforcement Learning with Human Feedback (RLHF).
User Simulation with Large Language Models for Evaluating Task-Oriented Dialogue
One of the major impediments to the development of new task-oriented dialogue (TOD) systems is the need for human evaluation at multiple stages and iterations of the development process.
NatCS: Eliciting Natural Customer Support Dialogues
Existing task-oriented dialogue datasets, which were collected to benchmark dialogue systems mainly in written human-to-bot settings, are not representative of real customer support conversations and do not provide realistic benchmarks for systems that are applied to natural data.
Intent Induction from Conversations for Task-Oriented Dialogue Track at DSTC 11
With increasing demand for and adoption of virtual assistants, recent work has investigated ways to accelerate bot schema design through the automatic induction of intents or the induction of slots and dialogue states.
Dialog2API: Task-Oriented Dialogue with API Description and Example Programs
The conversational model interacts with the environment by generating and executing programs triggering a set of pre-defined APIs.
Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System
Pre-trained language models have been recently shown to benefit task-oriented dialogue (TOD) systems.
Goal-Embedded Dual Hierarchical Model for Task-Oriented Dialogue Generation
Hierarchical neural networks are often used to model inherent structures within dialogues.
CASA-NLU: Context-Aware Self-Attentive Natural Language Understanding for Task-Oriented Chatbots
In this work, we propose a context-aware self-attentive NLU (CASA-NLU) model that uses multiple signals, such as previous intents, slots, dialog acts and utterances over a variable context window, in addition to the current user utterance.
Simple, Fast, Accurate Intent Classification and Slot Labeling for Goal-Oriented Dialogue Systems
With the advent of conversational assistants, like Amazon Alexa, Google Now, etc., dialogue systems are gaining a lot of traction, especially in industrial setting.
