160 citations · 275 across the 7 of their papers we have counts for
11 papers
Improving Top-K Decoding for Non-Autoregressive Semantic Parsing via Intent Conditioning
Geunseob Oh, Rahul Goel, Chris Hidey +4
Semantic parsing (SP) is a core component of modern virtual assistants like Google Assistant and Amazon Alexa. While sequence-to-sequence-based auto-regressive (AR) approaches are…
Resource Constrained Dialog Policy Learning via Differentiable Inductive Logic Programming
Zhenpeng Zhou, Ahmad Beirami, Paul Crook +3
Motivated by the needs of resource constrained dialog policy learning, we introduce dialog policy via differentiable inductive logic (DILOG). We explore the tasks of one-shot learn…
User Memory Reasoning for Conversational Recommendation
Hu Xu, Seungwhan Moon, Honglei Liu +3
We study a conversational recommendation model which dynamically manages users' past (offline) preferences and current (online) requests through a structured and cumulative user me…
Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialogue
Dongyeop Kang, Anusha Balakrishnan, Pararth Shah +3
Traditional recommendation systems produce static rather than interactive recommendations invariant to a user's specific requests, clarifications, or current mood, and can suffer f…
User Modeling for Task Oriented Dialogues
Izzeddin Gur, Dilek Hakkani-Tur, Gokhan Tur +1
We introduce end-to-end neural network based models for simulating users of task-oriented dialogue systems. User simulation in dialogue systems is crucial from two different perspe…
FollowNet: Robot Navigation by Following Natural Language Directions with Deep Reinforcement Learning
Pararth Shah, Marek Fiser, Aleksandra Faust +2
Understanding and following directions provided by humans can enable robots to navigate effectively in unknown situations. We present FollowNet, an end-to-end differentiable neural…