Search Results for author:
Found 20 papers, 6 papers with code
LD-SDS: Towards an Expressive Spoken Dialogue System based on Linked-Data
In this work we discuss the related challenges and describe an approach towards the fusion of state-of-the-art technologies from the Spoken Dialogue Systems (SDS) and the Semantic Web and Information Retrieval domains.
Collaborative Multi-Agent Dialogue Model Training Via Reinforcement Learning
and their own objectives, and can only interact via natural language they generate.
Plato Dialogue System: A Flexible Conversational AI Research Platform
Plato has been designed to be easy to understand and debug and is agnostic to the underlying learning frameworks that train each component.
Exploration Based Language Learning for Text-Based Games
In this work, we propose to use the exploration approach of Go-Explore for solving text-based games.
Joint Contextual Modeling for ASR Correction and Language Understanding
As a baseline approach, we trained task-specific Statistical Language Models (SLM) and fine-tuned state-of-the-art Generalized Pre-training (GPT) Language Model to re-rank the n-best ASR hypotheses, followed by a model to identify the dialog act and slots.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Controllable Text Generation with Focused Variation
This work introduces Focused-Variation Network (FVN), a novel model to control language generation.
Language Model is All You Need: Natural Language Understanding as Question Answering
Different flavors of transfer learning have shown tremendous impact in advancing research and applications of machine learning.
Can You be More Social? Injecting Politeness and Positivity into Task-Oriented Conversational Agents
The second component of the research is the construction of a conversational agent model capable of injecting social language into an agent's responses while still preserving content.
Generative Conversational Networks
We show an average improvement of 35% in intent detection and 21% in slot tagging over a baseline model trained from the seed data.
"How Robust r u?": Evaluating Task-Oriented Dialogue Systems on Spoken Conversations
Most prior work in dialogue modeling has been on written conversations mostly because of existing data sets.
Training Conversational Agents with Generative Conversational Networks
Rich, open-domain textual data available on the web resulted in great advancements for language processing.
User Response and Sentiment Prediction for Automatic Dialogue Evaluation
Automatic evaluation is beneficial for open-domain dialog system development.
What is wrong with you?: Leveraging User Sentiment for Automatic Dialog Evaluation
Existing model-based metrics for system response evaluation are trained on human annotated data, which is cumbersome to collect.
Understanding How People Rate Their Conversations
In this work, we conduct a study to better understand how people rate their interactions with conversational agents.
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code
This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims.
Knowledge-Grounded Conversational Data Augmentation with Generative Conversational Networks
Specifically, we show that for open-domain conversations with 10\% of seed data, our approach performs close to the baseline that uses 100% of the data, while for knowledge-grounded conversations, it achieves the same using only 1% of the data, on human ratings of engagingness, fluency, and relevance.
Weakly Supervised Data Augmentation Through Prompting for Dialogue Understanding
Dialogue understanding tasks often necessitate abundant annotated data to achieve good performance and that presents challenges in low-resource settings.
PLACES: Prompting Language Models for Social Conversation Synthesis
Collecting high quality conversational data can be very expensive for most applications and infeasible for others due to privacy, ethical, or similar concerns.
Selective In-Context Data Augmentation for Intent Detection using Pointwise V-Information
This work focuses on in-context data augmentation for intent detection.
Ranked #1 on
Intent Detection
on BANKING77 10-shot
"What do others think?": Task-Oriented Conversational Modeling with Subjective Knowledge
We hope this task and dataset can promote further research on TOD and subjective content understanding.
