Search Results for author:
Found 14 papers, 4 papers with code
SEINE: SEgment-based Indexing for NEural information retrieval
However, these methods focus on finding the representation that best represents a text (aka metric learning) and the actual retrieval function that is responsible for similarity matching between query and document is kept at a minimum by using dot product.
Sequencing Matters: A Generate-Retrieve-Generate Model for Building Conversational Agents
This paper contains what the Georgetown InfoSense group has done in regard to solving the challenges presented by TREC iKAT 2023.
GazBy: Gaze-Based BERT Model to Incorporate Human Attention in Neural Information Retrieval
This paper is interested in investigating whether human gaze signals can be leveraged to improve state-of-the-art search engine performance and how to incorporate this new input signal marked by human attention into existing neural retrieval models.
Incorporating Voice Instructions in Model-Based Reinforcement Learning for Self-Driving Cars
This paper presents a novel approach that supports natural language voice instructions to guide deep reinforcement learning (DRL) algorithms when training self-driving cars.
Model-based Reinforcement Learning
reinforcement-learning
+2
Feature Modulation to Improve Struggle Detection in Web Search: A Psychological Approach
Existing Web search struggle detection methods rely on effort-based features to identify the struggling moments.
High-Quality Diversification for Task-Oriented Dialogue Systems
Many task-oriented dialogue systems use deep reinforcement learning (DRL) to learn policies that respond to the user appropriately and complete the tasks successfully.
More Diverse Dialogue Datasets via Diversity-Informed Data Collection
Automated generation of conversational dialogue using modern neural architectures has made notable advances.
Balancing Reinforcement Learning Training Experiences in Interactive Information Retrieval
Interactive Information Retrieval (IIR) and Reinforcement Learning (RL) share many commonalities, including an agent who learns while interacts, a long-term and complex goal, and an algorithm that explores and adapts.
Information Retrieval and Its Sister Disciplines
This article presents a summary graph to show the relationships between Information Retrieval (IR) and other related disciplines.
Corpus-Level End-to-End Exploration for Interactive Systems
A core interest in building Artificial Intelligence (AI) agents is to let them interact with and assist humans.
A Re-classification of Information Seeking Tasks and Their Computational Solutions
This article presents a re-classification of information seeking (IS) tasks, concepts, and algorithms.
Modeling Long-Range Context for Concurrent Dialogue Acts Recognition
Previous work on DA recognition either assumes one DA per utterance or fails to realize the sequential nature of dialogues.
DeepTileBars: Visualizing Term Distribution for Neural Information Retrieval
Most neural Information Retrieval (Neu-IR) models derive query-to-document ranking scores based on term-level matching.
