research
∙
08/16/2023
Enhancing Performance on Seen and Unseen Dialogue Scenarios using Retrieval-Augmented End-to-End Task-Oriented System
End-to-end task-oriented dialogue (TOD) systems have achieved promising ...
share
research
∙
04/19/2023
A Theory on Adam Instability in Large-Scale Machine Learning
We present a theory for the previously unexplained divergent behavior no...
research
∙
01/10/2023
Scaling Laws for Generative Mixed-Modal Language Models
Generative language models define distributions over sequences of tokens...
research
∙
08/05/2022
BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage
We present BlenderBot 3, a 175B parameter dialogue model capable of open...
research
∙
05/02/2022
OPT: Open Pre-trained Transformer Language Models
Large language models, which are often trained for hundreds of thousands...
research
∙
03/24/2022
Language Models that Seek for Knowledge: Modular Search Generation for Dialogue and Prompt Completion
Language models (LMs) have recently been shown to generate more factual ...
research
∙
01/12/2022
Human Evaluation of Conversations is an Open Problem: comparing the sensitivity of various methods for evaluating dialogue agents
At the heart of improving conversational AI is the open problem of how t...
research
∙
10/13/2021
Teaching Models new APIs: Domain-Agnostic Simulators for Task Oriented Dialogue
We demonstrate that large language models are able to simulate Task Orie...
research
∙
06/08/2021
Hash Layers For Large Sparse Models
We investigate the training of sparse layers that use different paramete...
research
∙
06/08/2021
Staircase Attention for Recurrent Processing of Sequences
Attention mechanisms have become a standard tool for sequence modeling t...
research
∙
05/13/2021
Not All Memories are Created Equal: Learning to Forget by Expiring
Attention mechanisms have shown promising results in sequence modeling t...
research
∙
10/24/2020
Adding Chit-Chats to Enhance Task-Oriented Dialogues
The existing dialogue corpora and models are typically designed under tw...
research
∙
06/22/2020
Open-Domain Conversational Agents: Current Progress, Open Problems, and Future Directions
We present our view of what is necessary to build an engaging open-domai...
research
∙
04/28/2020
Recipes for building an open-domain chatbot
Building open-domain chatbots is a challenging area for machine learning...
research
∙
11/10/2019
Don't Say That! Making Inconsistent Dialogue Unlikely with Unlikelihood Training
Generative dialogue models currently suffer from a number of problems wh...
research
∙
11/09/2019
The Dialogue Dodecathlon: Open-Domain Knowledge and Image Grounded Conversational Agents
We introduce dodecaDialogue: a set of 12 tasks that measures if a conver...
research
∙
09/06/2019
ACUTE-EVAL: Improved Dialogue Evaluation with Optimized Questions and Multi-turn Comparisons
While dialogue remains an important end-goal of natural language researc...
research
∙
08/12/2019
Neural Text Generation with Unlikelihood Training
Neural text generation is a key tool in natural language applications, b...
research
∙
02/22/2019
What makes a good conversation? How controllable attributes affect human judgments
A good conversation requires balance -- between simplicity and detail; s...
research
∙
02/03/2019
Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings
We consider the task of inferring is-a relationships from large text cor...
research
∙
11/03/2018
Wizard of Wikipedia: Knowledge-Powered Conversational agents
In open-domain dialogue intelligent agents should exhibit the use of kno...
research
∙
06/08/2018
Hearst Patterns Revisited: Automatic Hypernym Detection from Large Text Corpora
Methods for unsupervised hypernym detection may broadly be categorized a...
research
∙
04/14/2017
Distributional Modeling on a Diet: One-shot Word Learning from Text Only
We test whether distributional models can do one-shot learning of defini...
research
∙
05/18/2016
Relations such as Hypernymy: Identifying and Exploiting Hearst Patterns in Distributional Vectors for Lexical Entailment
We consider the task of predicting lexical entailment using distribution...
research
∙
03/03/2016