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Found 9 papers, 4 papers with code
GRAVL-BERT: Graphical Visual-Linguistic Representations for Multimodal Coreference Resolution
Learning from multimodal data has become a popular research topic in recent years.
Building Goal-Oriented Dialogue Systems with Situated Visual Context
Most popular goal-oriented dialogue agents are capable of understanding the conversational context.
Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systems
Finally, we evaluate our system using a typical movie ticket booking task and show that the dialogue simulator is an essential component of the system that leads to over $50\%$ improvement in turn-level action signature prediction accuracy.
Few Shot Dialogue State Tracking using Meta-learning
Dialogue State Tracking (DST) forms a core component of automated chatbot based systems designed for specific goals like hotel, taxi reservation, tourist information, etc.
From Machine Reading Comprehension to Dialogue State Tracking: Bridging the Gap
In this paper, we propose using machine reading comprehension (RC) in state tracking from two perspectives: model architectures and datasets.
Dialog State Tracking: A Neural Reading Comprehension Approach
In contrast to traditional state tracking methods where the dialog state is often predicted as a distribution over a closed set of all the possible slot values within an ontology, our method uses a simple attention-based neural network to point to the slot values within the conversation.
Ranked #19 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.0
MultiWOZ 2.1: A Consolidated Multi-Domain Dialogue Dataset with State Corrections and State Tracking Baselines
To fix the noisy state annotations, we use crowdsourced workers to re-annotate state and utterances based on the original utterances in the dataset.
Ranked #16 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.0
Dialogue State Tracking
Multi-domain Dialogue State Tracking
Single-Document Summarization Using Sentence Embeddings and K-Means Clustering
This paper proposes a novel method for extractive single document summarization using K-Means clustering and Sentence Embeddings.
Parsing Coordination for Spoken Language Understanding
Typical spoken language understanding systems provide narrow semantic parses using a domain-specific ontology.
