Search Results for author:
Found 10 papers, 4 papers with code
Intent Discovery for Enterprise Virtual Assistants: Applications of Utterance Embedding and Clustering to Intent Mining
A key challenge in the creation and refinement of virtual assistants is the ability to mine unlabeled utterance data to discover common intents.
Lightweight Transformers for Conversational AI
To understand how training on conversational language impacts performance of pre-trained models on downstream dialogue tasks, we build compact Transformer-based Language Models from scratch on several large corpora of conversational data.
Alexa, play with robot: Introducing the First Alexa Prize SimBot Challenge on Embodied AI
The Alexa Prize program has empowered numerous university students to explore, experiment, and showcase their talents in building conversational agents through challenges like the SocialBot Grand Challenge and the TaskBot Challenge.
Cross-stitched Multi-modal Encoders
In this paper, we propose a novel architecture for multi-modal speech and text input.
Seq-2-Seq based Refinement of ASR Output for Spoken Name Capture
Person name capture from human speech is a difficult task in human-machine conversations.
Constrained Decoding for Computationally Efficient Named Entity Recognition Taggers
Current state-of-the-art models for named entity recognition (NER) are neural models with a conditional random field (CRF) as the final layer.
Multiple Word Embeddings for Increased Diversity of Representation
Most state-of-the-art models in natural language processing (NLP) are neural models built on top of large, pre-trained, contextual language models that generate representations of words in context and are fine-tuned for the task at hand.
Computationally Efficient NER Taggers with Combined Embeddings and Constrained Decoding
The CRF layer is used to facilitate global coherence between labels, and the contextual embeddings provide a better representation of words in context.
An Effective Label Noise Model for DNN Text Classification
In this paper, we propose an approach to training deep networks that is robust to label noise.
Baseline: A Library for Rapid Modeling, Experimentation and Development of Deep Learning Algorithms targeting NLP
We introduce Baseline: a library for reproducible deep learning research and fast model development for NLP.
