37 citations · 89 across the 16 of their papers we have counts for
15 papers · 1 filter
Large Language Models as Zero-shot Dialogue State Tracker through Function Calling
Zekun Li, Zhiyu Zoey Chen, Mike Ross +7
Large language models (LLMs) are increasingly prevalent in conversational systems due to their advanced understanding and generative capabilities in general contexts. However, thei…
KETOD: Knowledge-Enriched Task-Oriented Dialogue
Zhiyu Chen, Bing Liu, Seungwhan Moon +3
Existing studies in dialogue system research mostly treat task-oriented dialogue and chit-chat as separate domains. Towards building a human-like assistant that can converse natura…
Database Search Results Disambiguation for Task-Oriented Dialog Systems
Kun Qian, Ahmad Beirami, Satwik Kottur +5
As task-oriented dialog systems are becoming increasingly popular in our lives, more realistic tasks have been proposed and explored. However, new practical challenges arise. For i…
Teaching Models new APIs: Domain-Agnostic Simulators for Task Oriented Dialogue
Moya Chen, Paul A. Crook, Stephen Roller
We demonstrate that large language models are able to simulate Task Oriented Dialogues in novel domains, provided only with an API implementation and a list of goals. We show these…
Zero-Shot Dialogue State Tracking via Cross-Task Transfer
Zhaojiang Lin, Bing Liu, Andrea Madotto +8
Zero-shot transfer learning for dialogue state tracking (DST) enables us to handle a variety of task-oriented dialogue domains without the expense of collecting in-domain data. In…
Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue State Tracking
Zhaojiang Lin, Bing Liu, Seungwhan Moon +7
Zero-shot cross-domain dialogue state tracking (DST) enables us to handle task-oriented dialogue in unseen domains without the expense of collecting in-domain data. In this paper,…
Continual Learning in Task-Oriented Dialogue Systems
Andrea Madotto, Zhaojiang Lin, Zhenpeng Zhou +6
Continual learning in task-oriented dialogue systems can allow us to add new domains and functionalities through time without incurring the high cost of a whole system retraining.…
Overview of the Ninth Dialog System Technology Challenge: DSTC9
Chulaka Gunasekara, Seokhwan Kim, Luis Fernando D'Haro +36
This paper introduces the Ninth Dialog System Technology Challenge (DSTC-9). This edition of the DSTC focuses on applying end-to-end dialog technologies for four distinct tasks in…
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…
Adding Chit-Chat to Enhance Task-Oriented Dialogues
Kai Sun, Seungwhan Moon, Paul Crook +7
Existing dialogue corpora and models are typically designed under two disjoint motives: while task-oriented systems focus on achieving functional goals (e.g., booking hotels), open…
Situated and Interactive Multimodal Conversations
Seungwhan Moon, Satwik Kottur, Paul A. Crook +9
Next generation virtual assistants are envisioned to handle multimodal inputs (e.g., vision, memories of previous interactions, in addition to the user's utterances), and perform m…
Information Seeking in the Spirit of Learning: a Dataset for Conversational Curiosity
Pedro Rodriguez, Paul Crook, Seungwhan Moon +1
Open-ended human learning and information-seeking are increasingly mediated by digital assistants. However, such systems often ignore the user's pre-existing knowledge. Assuming a…
SIMMC: Situated Interactive Multi-Modal Conversational Data Collection And Evaluation Platform
Paul A. Crook, Shivani Poddar, Ankita De +4
As digital virtual assistants become ubiquitous, it becomes increasingly important to understand the situated behaviour of users as they interact with these assistants. To this end…
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…
End-to-End Joint Learning of Natural Language Understanding and Dialogue Manager
Xuesong Yang, Yun-Nung Chen, Dilek Hakkani-Tur +4
Natural language understanding and dialogue policy learning are both essential in conversational systems that predict the next system actions in response to a current user utteranc…