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Found 29 papers, 0 papers with code
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)
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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.
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.
Feedback-Based Self-Learning in Large-Scale Conversational AI Agents
Typically, the accuracy of the ML models in these components are improved by manually transcribing and annotating data.
Continuous Learning for Large-scale Personalized Domain Classification
This is a major component in mainstream IPDAs in industry.
Locale-agnostic Universal Domain Classification Model in Spoken Language Understanding
In this paper, we introduce an approach for leveraging available data across multiple locales sharing the same language to 1) improve domain classification model accuracy in Spoken Language Understanding and user experience even if new locales do not have sufficient data and 2) reduce the cost of scaling the domain classifier to a large number of locales.
Coupled Representation Learning for Domains, Intents and Slots in Spoken Language Understanding
Our proposed model learns the vector representation of intents based on the slots tied to these intents by aggregating the representations of the slots.
Differentiable Greedy Networks
Optimal selection of a subset of items from a given set is a hard problem that requires combinatorial optimization.
Efficient Large-Scale Neural Domain Classification with Personalized Attention
In this paper, we explore the task of mapping spoken language utterances to one of thousands of natural language understanding domains in intelligent personal digital assistants (IPDAs).
Contextual Slot Carryover for Disparate Schemas
In the slot-filling paradigm, where a user can refer back to slots in the context during a conversation, the goal of the contextual understanding system is to resolve the referring expressions to the appropriate slots in the context.
A Scalable Neural Shortlisting-Reranking Approach for Large-Scale Domain Classification in Natural Language Understanding
Intelligent personal digital assistants (IPDAs), a popular real-life application with spoken language understanding capabilities, can cover potentially thousands of overlapping domains for natural language understanding, and the task of finding the best domain to handle an utterance becomes a challenging problem on a large scale.
Efficient Large-Scale Domain Classification with Personalized Attention
In this paper, we explore the task of mapping spoken language utterances to one of thousands of natural language understanding domains in intelligent personal digital assistants (IPDAs).
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.
Cross-Lingual Transfer Learning for POS Tagging without Cross-Lingual Resources
Evaluating on POS datasets from 14 languages in the Universal Dependencies corpus, we show that the proposed transfer learning model improves the POS tagging performance of the target languages without exploiting any linguistic knowledge between the source language and the target language.
Domainless Adaptation by Constrained Decoding on a Schema Lattice
In many applications such as personal digital assistants, there is a constant need for new domains to increase the system{'}s coverage of user queries.
Frustratingly Easy Neural Domain Adaptation
Popular techniques for domain adaptation such as the feature augmentation method of Daum{\'e} III (2009) have mostly been considered for sparse binary-valued features, but not for dense real-valued features such as those used in neural networks.
