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Found 19 papers, 9 papers with code
Pre-training Intent-Aware Encoders for Zero- and Few-Shot Intent Classification
Intent classification (IC) plays an important role in task-oriented dialogue systems as it identifies user intents from given utterances.
Conversation Style Transfer using Few-Shot Learning
Conventional text style transfer approaches for natural language focus on sentence-level style transfer without considering contextual information, and the style is described with attributes (e. g., formality).
Improving Prediction Backward-Compatiblility in NLP Model Upgrade with Gated Fusion
When upgrading neural models to a newer version, new errors that were not encountered in the legacy version can be introduced, known as regression errors.
Backward Compatibility During Data Updates by Weight Interpolation
This method interpolates between the weights of the old and new model and we show in extensive experiments that it reduces negative flips without sacrificing the improved accuracy of the new model.
Dialog2API: Task-Oriented Dialogue with API Description and Example Programs
The conversational model interacts with the environment by generating and executing programs triggering a set of pre-defined APIs.
Label Semantic Aware Pre-training for Few-shot Text Classification
Label semantic aware systems have leveraged this information for improved text classification performance during fine-tuning and prediction.
Measuring and Reducing Model Update Regression in Structured Prediction for NLP
First, we measure and analyze model update regression in different model update settings.
Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System
Pre-trained language models have been recently shown to benefit task-oriented dialogue (TOD) systems.
Semantic Parsing in Task-Oriented Dialog with Recursive Insertion-based Encoder
At the generation time, the model constructs the semantic parse tree by recursively inserting the predicted non-terminal labels at the predicted positions until termination.
Towards End-to-End In-Image Neural Machine Translation
In this paper, we offer a preliminary investigation into the task of in-image machine translation: transforming an image containing text in one language into an image containing the same text in another language.
Capturing document context inside sentence-level neural machine translation models with self-training
Neural machine translation (NMT) has arguably achieved human level parity when trained and evaluated at the sentence-level.
A Generalized Framework of Sequence Generation with Application to Undirected Sequence Models
We investigate this problem by proposing a generalized model of sequence generation that unifies decoding in directed and undirected models.
Molecular geometry prediction using a deep generative graph neural network
Conventional conformation generation methods minimize hand-designed molecular force field energy functions that are often not well correlated with the true energy function of a molecule observed in nature.
Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement
We propose a conditional non-autoregressive neural sequence model based on iterative refinement.
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Machine Translation
on IWSLT2015 German-English
Simple Nearest Neighbor Policy Method for Continuous Control Tasks
As this policy does not require any optimization, it allows us to investigate the underlying difficulty of a task without being distracted by optimization difficulty of a learning algorithm.
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation
In this work, we propose to apply trust region optimization to deep reinforcement learning using a recently proposed Kronecker-factored approximation to the curvature.
Generating Images from Captions with Attention
Motivated by the recent progress in generative models, we introduce a model that generates images from natural language descriptions.
Initialization Strategies of Spatio-Temporal Convolutional Neural Networks
We propose a new way of incorporating temporal information present in videos into Spatial Convolutional Neural Networks (ConvNets) trained on images, that avoids training Spatio-Temporal ConvNets from scratch.
Unsupervised Learning of Video Representations using LSTMs
We further evaluate the representations by finetuning them for a supervised learning problem - human action recognition on the UCF-101 and HMDB-51 datasets.
