Search Results for author:
Found 13 papers, 1 papers with code
Intent Discovery for Enterprise Virtual Assistants: Applications of Utterance Embedding and Clustering to Intent Mining
A key challenge in the creation and refinement of virtual assistants is the ability to mine unlabeled utterance data to discover common intents.
Lightweight Transformers for Conversational AI
To understand how training on conversational language impacts performance of pre-trained models on downstream dialogue tasks, we build compact Transformer-based Language Models from scratch on several large corpora of conversational data.
"Don't forget to put the milk back!" Dataset for Enabling Embodied Agents to Detect Anomalous Situations
Our work assists in this goal by enabling robots to inform their users of dangerous or unsanitary anomalies in their home.
Dialogue with Robots: Proposals for Broadening Participation and Research in the SLIVAR Community
The ability to interact with machines using natural human language is becoming not just commonplace, but expected.
Mastering Robot Manipulation with Multimodal Prompts through Pretraining and Multi-task Fine-tuning
Inspired by their success in language tasks, existing research has leveraged LLMs in embodied instruction following and task planning.
Alexa, play with robot: Introducing the First Alexa Prize SimBot Challenge on Embodied AI
The Alexa Prize program has empowered numerous university students to explore, experiment, and showcase their talents in building conversational agents through challenges like the SocialBot Grand Challenge and the TaskBot Challenge.
Alexa Arena: A User-Centric Interactive Platform for Embodied AI
We introduce Alexa Arena, a user-centric simulation platform for Embodied AI (EAI) research.
Improving Open-Domain Dialogue Evaluation with a Causal Inference Model
We project these features to the dialogue level and train a dialogue-level MLP regression model, a dialogue-level LSTM, and a novel causal inference model called counterfactual-LSTM (CF-LSTM) to predict ratings.
GIVL: Improving Geographical Inclusivity of Vision-Language Models with Pre-Training Methods
A key goal for the advancement of AI is to develop technologies that serve the needs not just of one group but of all communities regardless of their geographical region.
Alexa, Let's Work Together: Introducing the First Alexa Prize TaskBot Challenge on Conversational Task Assistance
Since its inception in 2016, the Alexa Prize program has enabled hundreds of university students to explore and compete to develop conversational agents through the SocialBot Grand Challenge.
Deep Clustering with Measure Propagation
For example, deep embedded clustering (DEC) has greatly improved the unsupervised clustering performance, by using stacked autoencoders for representation learning.
Ranked #1 on
Deep Clustering
on Stackoverflow
