Search Results for author:
Found 17 papers, 6 papers with code
Small But Funny: A Feedback-Driven Approach to Humor Distillation
The emergence of Large Language Models (LLMs) has brought to light promising language generation capabilities, particularly in performing tasks like complex reasoning and creative writing.
Retrieve-and-Fill for Scenario-based Task-Oriented Semantic Parsing
We tackle this problem by introducing scenario-based semantic parsing: a variant of the original task which first requires disambiguating an utterance's "scenario" (an intent-slot template with variable leaf spans) before generating its frame, complete with ontology and utterance tokens.
AutoNLU: Detecting, root-causing, and fixing NLU model errors
Improving the quality of Natural Language Understanding (NLU) models, and more specifically, task-oriented semantic parsing models, in production is a cumbersome task.
Assessing Data Efficiency in Task-Oriented Semantic Parsing
Data efficiency, despite being an attractive characteristic, is often challenging to measure and optimize for in task-oriented semantic parsing; unlike exact match, it can require both model- and domain-specific setups, which have, historically, varied widely across experiments.
Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimization
When tuning the architecture and hyperparameters of large machine learning models for on-device deployment, it is desirable to understand the optimal trade-offs between on-device latency and model accuracy.
Diagnosing Transformers in Task-Oriented Semantic Parsing
Modern task-oriented semantic parsing approaches typically use seq2seq transformers to map textual utterances to semantic frames comprised of intents and slots.
Span Pointer Networks for Non-Autoregressive Task-Oriented Semantic Parsing
An effective recipe for building seq2seq, non-autoregressive, task-oriented parsers to map utterances to semantic frames proceeds in three steps: encoding an utterance $x$, predicting a frame's length |y|, and decoding a |y|-sized frame with utterance and ontology tokens.
Low-Resource Task-Oriented Semantic Parsing via Intrinsic Modeling
Task-oriented semantic parsing models typically have high resource requirements: to support new ontologies (i. e., intents and slots), practitioners crowdsource thousands of samples for supervised fine-tuning.
Non-Autoregressive Semantic Parsing for Compositional Task-Oriented Dialog
Semantic parsing using sequence-to-sequence models allows parsing of deeper representations compared to traditional word tagging based models.
Lightweight Convolutional Representations for On-Device Natural Language Processing
The increasing computational and memory complexities of deep neural networks have made it difficult to deploy them on low-resource electronic devices (e. g., mobile phones, tablets, wearables).
Examining Hyperparameters of Neural Networks Trained Using Local Search
Deep neural networks (DNNs) have been found useful for many applications.
Evaluating Lottery Tickets Under Distributional Shifts
The Lottery Ticket Hypothesis suggests large, over-parameterized neural networks consist of small, sparse subnetworks that can be trained in isolation to reach a similar (or better) test accuracy.
Derivative-Free Optimization of Neural Networks using Local Search
LS is an algorithm where constrained noise is iteratively applied to subsets of the search space.
Optimizing Deep Neural Networks with Multiple Search Neuroevolution
This paper presents an evolutionary metaheuristic called Multiple Search Neuroevolution (MSN) to optimize deep neural networks.
PyText: A Seamless Path from NLP research to production
We introduce PyText - a deep learning based NLP modeling framework built on PyTorch.
Efficient Single-Shot Multibox Detector for Construction Site Monitoring
Asset monitoring in construction sites is an intricate, manually intensive task, that can highly benefit from automated solutions engineered using deep neural networks.
Experiential Robot Learning with Accelerated Neuroevolution
We test our algorithm first on a simulated task of playing the game Flappy Bird, then on a physical NAO robot in a static Object Centering task.
