Search Results for author:
Found 23 papers, 11 papers with code
DialogStitch: Synthetic Deeper and Multi-Context Task-Oriented Dialogs
Real-world conversational agents must effectively handle long conversations that span multiple contexts.
Continual Dialogue State Tracking via Example-Guided Question Answering
Dialogue systems are frequently updated to accommodate new services, but naively updating them by continually training with data for new services in diminishing performance on previously learnt services.
AUTODIAL: Efficient Asynchronous Task-Oriented Dialogue Model
As large dialogue models become commonplace in practice, the problems surrounding high compute requirements for training, inference and larger memory footprint still persists.
Data-Efficiency with a Single GPU: An Exploration of Transfer Methods for Small Language Models
Multi-task learning (MTL), instruction tuning, and prompting have recently been shown to improve the generalizability of large language models to new tasks.
KETOD: Knowledge-Enriched Task-Oriented Dialogue
We also propose two new models, SimpleToDPlus and Combiner, for the proposed task.
Know Thy Strengths: Comprehensive Dialogue State Tracking Diagnostics
Recent works that revealed the vulnerability of dialogue state tracking (DST) models to distributional shifts have made holistic comparisons on robustness and qualitative analyses increasingly important for understanding their relative performance.
Ranked #4 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.1
(using extra training data)
Dialogue State Tracking
Multi-domain Dialogue State Tracking
+1
Database Search Results Disambiguation for Task-Oriented Dialog Systems
We find that training on our augmented dialog data improves the model's ability to deal with ambiguous scenarios, without sacrificing performance on unmodified turns.
Robustness through Data Augmentation Loss Consistency
Our experiments show that DAIR consistently outperforms ERM and DA-ERM with little marginal computational cost and sets new state-of-the-art results in several benchmarks involving covariant data augmentation.
Ranked #1 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.2
Multi-domain Dialogue State Tracking
Visual Question Answering
Annotation Inconsistency and Entity Bias in MultiWOZ
In this work, we identify an overlooked issue with dialog state annotation inconsistencies in the dataset, where a slot type is tagged inconsistently across similar dialogs leading to confusion for DST modeling.
DVD: A Diagnostic Dataset for Multi-step Reasoning in Video Grounded Dialogue
A video-grounded dialogue system is required to understand both dialogue, which contains semantic dependencies from turn to turn, and video, which contains visual cues of spatial and temporal scene variations.
ProFormer: Towards On-Device LSH Projection Based Transformers
At the heart of text based neural models lay word representations, which are powerful but occupy a lot of memory making it challenging to deploy to devices with memory constraints such as mobile phones, watches and IoT.
Neural Assistant: Joint Action Prediction, Response Generation, and Latent Knowledge Reasoning
In this paper, we develop Neural Assistant: a single neural network model that takes conversation history and an external knowledge source as input and jointly produces both text response and action to be taken by the system as output.
Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset
A significant barrier to progress in data-driven approaches to building dialog systems is the lack of high quality, goal-oriented conversational data.
On-Device Text Representations Robust To Misspellings via Projections
Recently, there has been a strong interest in developing natural language applications that live on personal devices such as mobile phones, watches and IoT with the objective to preserve user privacy and have low memory.
Deep Reinforcement Learning For Modeling Chit-Chat Dialog With Discrete Attributes
Open domain dialog systems face the challenge of being repetitive and producing generic responses.
Do Neural Dialog Systems Use the Conversation History Effectively? An Empirical Study
Neural generative models have been become increasingly popular when building conversational agents.
Transferable Neural Projection Representations
Neural word representations are at the core of many state-of-the-art natural language processing models.
Towards Non-saturating Recurrent Units for Modelling Long-term Dependencies
Modelling long-term dependencies is a challenge for recurrent neural networks.
The Bottleneck Simulator: A Model-based Deep Reinforcement Learning Approach
Deep reinforcement learning has recently shown many impressive successes.
Model-based Reinforcement Learning
reinforcement-learning
+1
A Deep Reinforcement Learning Chatbot (Short Version)
We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition.
A Deep Reinforcement Learning Chatbot
By applying reinforcement learning to crowdsourced data and real-world user interactions, the system has been trained to select an appropriate response from the models in its ensemble.
Variance Reduction in SGD by Distributed Importance Sampling
This leads the model to update using an unbiased estimate of the gradient which also has minimum variance when the sampling proposal is proportional to the L2-norm of the gradient.
