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Found 15 papers, 3 papers with code
Multi-User MultiWOZ: Task-Oriented Dialogues among Multiple Users
While most task-oriented dialogues assume conversations between the agent and one user at a time, dialogue systems are increasingly expected to communicate with multiple users simultaneously who make decisions collaboratively.
Toward More Accurate and Generalizable Evaluation Metrics for Task-Oriented Dialogs
Measurement of interaction quality is a critical task for the improvement of spoken dialog systems.
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.
Dialog Simulation with Realistic Variations for Training Goal-Oriented Conversational Systems
Our approach includes a novel goal-sampling technique for sampling plausible user goals and a dialog simulation technique that uses heuristic interplay between the user and the system (Alexa), where the user tries to achieve the sampled goal.
MA-DST: Multi-Attention Based Scalable Dialog State Tracking
Task oriented dialog agents provide a natural language interface for users to complete their goal.
Controlled Text Generation for Data Augmentation in Intelligent Artificial Agents
Data availability is a bottleneck during early stages of development of new capabilities for intelligent artificial agents.
Simple Question Answering with Subgraph Ranking and Joint-Scoring
Using this framework as a starting point, we focus on two aspects: improving subgraph selection through a novel ranking method and leveraging the subject--relation dependency by proposing a joint scoring CNN model with a novel loss function that enforces the well-order of scores.
Unsupervised Transfer Learning for Spoken Language Understanding in Intelligent Agents
Our findings suggest unsupervised pre-training on a large corpora of unlabeled utterances leads to significantly better SLU performance compared to training from scratch and it can even outperform conventional supervised transfer.
Online Embedding Compression for Text Classification using Low Rank Matrix Factorization
Empirically, we show that the proposed method can achieve 90% compression with minimal impact in accuracy for sentence classification tasks, and outperforms alternative methods like fixed-point quantization or offline word embedding compression.
Contextual Language Model Adaptation for Conversational Agents
Statistical language models (LM) play a key role in Automatic Speech Recognition (ASR) systems used by conversational agents.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Fast and Scalable Expansion of Natural Language Understanding Functionality for Intelligent Agents
Fast expansion of natural language functionality of intelligent virtual agents is critical for achieving engaging and informative interactions.
Topic-based Evaluation for Conversational Bots
Dialog evaluation is a challenging problem, especially for non task-oriented dialogs where conversational success is not well-defined.
On Evaluating and Comparing Open Domain Dialog Systems
In this paper, we propose a comprehensive evaluation strategy with multiple metrics designed to reduce subjectivity by selecting metrics which correlate well with human judgement.
