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Found 50 papers, 8 papers with code
Explaining Dialogue Evaluation Metrics using Adversarial Behavioral Analysis
There is an increasing trend in using neural methods for dialogue model evaluation.
Dialogue Response Generation via Contrastive Latent Representation Learning
In this work, we aim to construct a robust sentence representation learning model, that is specifically designed for dialogue response generation, with Transformer-based encoder-decoder structure.
Open World Classification with Adaptive Negative Samples
Open world classification is a task in natural language processing with key practical relevance and impact.
Cluster-Guided Label Generation in Extreme Multi-Label Classification
For extreme multi-label classification (XMC), existing classification-based models poorly perform for tail labels and often ignore the semantic relations among labels, like treating "Wikipedia" and "Wiki" as independent and separate labels.
Selective In-Context Data Augmentation for Intent Detection using Pointwise V-Information
This work focuses on in-context data augmentation for intent detection.
Ranked #1 on
Intent Detection
on HWU64 5-shot
PENTATRON: PErsonalized coNText-Aware Transformer for Retrieval-based cOnversational uNderstanding
Conversational understanding is an integral part of modern intelligent devices.
Constrained Policy Optimization for Controlled Self-Learning in Conversational AI Systems
Based on the experimental results, we demonstrate that the proposed approach is capable of achieving the best balance between the policy value and constraint satisfaction rate.
Debiasing Neighbor Aggregation for Graph Neural Network in Recommender Systems
Graph neural networks (GNNs) have achieved remarkable success in recommender systems by representing users and items based on their historical interactions.
Learning Personalized Representations using Graph Convolutional Network
Then we propose a graph convolutional network(GCN) based model, namely Personalized Dynamic Routing Feature Encoder(PDRFE), that generates personalized customer representations learned from the built graph.
Scalable and Robust Self-Learning for Skill Routing in Large-Scale Conversational AI Systems
Skill routing is an important component in large-scale conversational systems.
Domain-Aware Contrastive Knowledge Transfer for Multi-domain Imbalanced Data
In many real-world machine learning applications, samples belong to a set of domains e. g., for product reviews each review belongs to a product category.
DeepSketch: A New Machine Learning-Based Reference Search Technique for Post-Deduplication Delta Compression
Data reduction in storage systems is becoming increasingly important as an effective solution to minimize the management cost of a data center.
Deciding Whether to Ask Clarifying Questions in Large-Scale Spoken Language Understanding
A large-scale conversational agent can suffer from understanding user utterances with various ambiguities such as ASR ambiguity, intent ambiguity, and hypothesis ambiguity.
AUGNLG: Few-shot Natural Language Generation using Self-trained Data Augmentation
Natural Language Generation (NLG) is a key component in a task-oriented dialogue system, which converts the structured meaning representation (MR) to the natural language.
Learning Slice-Aware Representations with Mixture of Attentions
Real-world machine learning systems are achieving remarkable performance in terms of coarse-grained metrics like overall accuracy and F-1 score.
Handling Long-Tail Queries with Slice-Aware Conversational Systems
We have been witnessing the usefulness of conversational AI systems such as Siri and Alexa, directly impacting our daily lives.
Neural model robustness for skill routing in large-scale conversational AI systems: A design choice exploration
Ensuring model robustness or resilience in the skill routing component is an important problem since skills may dynamically change their subscription in the ontology after the skill routing model has been deployed to production.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
DEUS: A Data-driven Approach to Estimate User Satisfaction in Multi-turn Dialogues
Digital assistants are experiencing rapid growth due to their ability to assist users with day-to-day tasks where most dialogues are happening multi-turn.
Guided Dialogue Policy Learning without Adversarial Learning in the Loop
Reinforcement learning methods have emerged as a popular choice for training an efficient and effective dialogue policy.
A scalable framework for learning from implicit user feedback to improve natural language understanding in large-scale conversational AI systems
Natural Language Understanding (NLU) is an established component within a conversational AI or digital assistant system, and it is responsible for producing semantic understanding of a user request.
Self-Supervised Contrastive Learning for Efficient User Satisfaction Prediction in Conversational Agents
Moreover, a powerful satisfaction model can be used as an objective function that a conversational agent continuously optimizes for.
Large-scale Hybrid Approach for Predicting User Satisfaction with Conversational Agents
An widely used approach to tackle this is to collect human annotation data and use them for evaluation or modeling.
Guided Dialog Policy Learning without Adversarial Learning in the Loop
Reinforcement Learning (RL) methods have emerged as a popular choice for training an efficient and effective dialogue policy.
The Eighth Dialog System Technology Challenge
This paper introduces the Eighth Dialog System Technology Challenge.
Generating a Common Question from Multiple Documents using Multi-source Encoder-Decoder Models
A preliminary step towards this goal is to generate a question that captures common concepts of multiple documents.
Data-Efficient Goal-Oriented Conversation with Dialogue Knowledge Transfer Networks
Our main dataset is the Stanford Multi-Domain dialogue corpus.
Structuring Latent Spaces for Stylized Response Generation
This structure allows the system to generate stylized relevant responses by sampling in the neighborhood of the conversation model prediction, and continuously control the style level.
Unsupervised Dialogue Spectrum Generation for Log Dialogue Ranking
Although the data-driven approaches of some recent bot building platforms make it possible for a wide range of users to easily create dialogue systems, those platforms don{'}t offer tools for quickly identifying which log dialogues contain problems.
Few-Shot Dialogue Generation Without Annotated Data: A Transfer Learning Approach
Learning with minimal data is one of the key challenges in the development of practical, production-ready goal-oriented dialogue systems.
Contextual Out-of-Domain Utterance Handling With Counterfeit Data Augmentation
Neural dialog models often lack robustness to anomalous user input and produce inappropriate responses which leads to frustrating user experience.
ConvLab: Multi-Domain End-to-End Dialog System Platform
We present ConvLab, an open-source multi-domain end-to-end dialog system platform, that enables researchers to quickly set up experiments with reusable components and compare a large set of different approaches, ranging from conventional pipeline systems to end-to-end neural models, in common environments.
Consistent Dialogue Generation with Self-supervised Feature Learning
Generating responses that are consistent with the dialogue context is one of the central challenges in building engaging conversational agents.
Jointly Optimizing Diversity and Relevance in Neural Response Generation
In this paper, we propose a SpaceFusion model to jointly optimize diversity and relevance that essentially fuses the latent space of a sequence-to-sequence model and that of an autoencoder model by leveraging novel regularization terms.
Ranked #1 on
Dialogue Generation
on Reddit (multi-ref)
Improving Robustness of Neural Dialog Systems in a Data-Efficient Way with Turn Dropout
We present a new dataset for studying the robustness of dialog systems to OOD input, which is bAbI Dialog Task 6 augmented with OOD content in a controlled way.
Nudging Neural Conversational Model with Domain Knowledge
Neural conversation models are attractive because one can train a model directly on dialog examples with minimal labeling.
Zero-Shot Adaptive Transfer for Conversational Language Understanding
Conversational agents such as Alexa and Google Assistant constantly need to increase their language understanding capabilities by adding new domains.
OneNet: Joint Domain, Intent, Slot Prediction for Spoken Language Understanding
In practice, most spoken language understanding systems process user input in a pipelined manner; first domain is predicted, then intent and semantic slots are inferred according to the semantic frames of the predicted domain.
Toward Continual Learning for Conversational Agents
While end-to-end neural conversation models have led to promising advances in reducing hand-crafted features and errors induced by the traditional complex system architecture, they typically require an enormous amount of data due to the lack of modularity.
Speaker-Sensitive Dual Memory Networks for Multi-Turn Slot Tagging
In multi-turn dialogs, natural language understanding models can introduce obvious errors by being blind to contextual information.
Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning
Building a dialogue agent to fulfill complex tasks, such as travel planning, is challenging because the agent has to learn to collectively complete multiple subtasks.
