Search Results for author:
Found 19 papers, 11 papers with code
Topic-Aware Response Generation in Task-Oriented Dialogue with Unstructured Knowledge Access
To alleviate the problem of structured databases' limited coverage, recent task-oriented dialogue systems incorporate external unstructured knowledge to guide the generation of system responses.
A Sequence-to-Sequence Approach to Dialogue State Tracking
This paper is concerned with dialogue state tracking (DST) in a task-oriented dialogue system.
Ranked #1 on
Multi-domain Dialogue State Tracking
on SGD
Dynamic Mesh-Aware Radiance Fields
Embedding polygonal mesh assets within photorealistic Neural Radience Fields (NeRF) volumes, such that they can be rendered and their dynamics simulated in a physically consistent manner with the NeRF, is under-explored from the system perspective of integrating NeRF into the traditional graphics pipeline.
Learning to Execute Actions or Ask Clarification Questions
In this paper, we extend the Minecraft Corpus Dataset by annotating all builder utterances into eight types, including clarification questions, and propose a new builder agent model capable of determining when to ask or execute instructions.
ASSIST: Towards Label Noise-Robust Dialogue State Tracking
In this paper, instead of improving the annotation quality further, we propose a general framework, named ASSIST (lAbel noiSe-robuSt dIalogue State Tracking), to train DST models robustly from noisy labels.
Schema-Guided User Satisfaction Modeling for Task-Oriented Dialogues
Further, it employs a fulfillment representation layer for learning how many task attributes have been fulfilled in the dialogue, an importance predictor component for calculating the importance of task attributes.
Towards Asking Clarification Questions for Information Seeking on Task-Oriented Dialogues
Task-oriented dialogue systems aim at providing users with task-specific services.
A Survey on Asking Clarification Questions Datasets in Conversational Systems
The ability to understand a user's underlying needs is critical for conversational systems, especially with limited input from users in a conversation.
Towards Understanding In-Context Learning with Contrastive Demonstrations and Saliency Maps
We investigate the role of various demonstration components in the in-context learning (ICL) performance of large language models (LLMs).
Logician: A Unified End-to-End Neural Approach for Open-Domain Information Extraction
In this paper, we consider the problem of open information extraction (OIE) for extracting entity and relation level intermediate structures from sentences in open-domain.
Learning Interpretable Relationships between Entities, Relations and Concepts via Bayesian Structure Learning on Open Domain Facts
Compared to the state-of-the-art methods, the learned network structures help improving the identification of concepts for entities based on the relations of entities on both datasets.
Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking
Existing approaches that have considered such relations generally fall short in: (1) fusing prior slot-domain membership relations and dialogue-aware dynamic slot relations explicitly, and (2) generalizing to unseen domains.
Dialogue State Tracking
Multi-domain Dialogue State Tracking
+1
Multi-Hop Open-Domain Question Answering over Structured and Unstructured Knowledge
DEHG employs a graph constructor to integrate structured and unstructured information, a context encoder to represent nodes and question, a heterogeneous information reasoning layer to conduct multi-hop reasoning on both information sources, and an answer decoder to generate answers for the question.
A Graph-Guided Reasoning Approach for Open-ended Commonsense Question Answering
Hence, a new benchmark challenge set for open-ended commonsense reasoning (OpenCSR) has been recently released, which contains natural science questions without any predefined choices.
Unlocking the Potential of User Feedback: Leveraging Large Language Model as User Simulator to Enhance Dialogue System
This approach uses LLM as annotation-free user simulator to assess dialogue responses, combining them with smaller fine-tuned end-to-end TOD models.
ChatGPT vs. Google: A Comparative Study of Search Performance and User Experience
In this study, we investigate the differences in user behavior when employing search engines and chatbot tools for information-seeking tasks.
A Large Language Model Enhanced Conversational Recommender System
For response generation, we utilize the generation ability of LLM as a language interface to better interact with users.
Enhancing Large Language Model Induced Task-Oriented Dialogue Systems Through Look-Forward Motivated Goals
Recently, the development of large language models (LLMs) has been significantly enhanced the question answering and dialogue generation, and makes them become increasingly popular in current practical scenarios.
