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Found 9 papers, 4 papers with code
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.
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.
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.
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
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.
A Sequence-to-Sequence Approach to Dialogue State Tracking
This paper is concerned with dialogue state tracking (DST) in a task-oriented dialogue system.
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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.
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.
