Search Results for author:
Found 11 papers, 2 papers with code
Temporal Generalization for Spoken Language Understanding
Spoken Language Understanding (SLU) models in industry applications are usually trained offline on historic data, but have to perform well on incoming user requests after deployment.
The Emotional Impact of Game Duration: A Framework for Understanding Player Emotions in Extended Gameplay Sessions
However, at that time, players were unaware of the significant impact that playtime could have on their feelings.
Unveiling the Impact of Macroeconomic Policies: A Double Machine Learning Approach to Analyzing Interest Rate Effects on Financial Markets
Results indicate that gradient boosting is a useful tool for predicting fund returns; for example, a 1% increase in interest rates causes an actively managed fund's return to decrease by -11. 97%.
Prompt Perturbation Consistency Learning for Robust Language Models
However, their performance on sequence labeling tasks such as intent classification and slot filling (IC-SF), which is a central component in personal assistant systems, lags significantly behind discriminative models.
Neural Architecture Search for Parameter-Efficient Fine-tuning of Large Pre-trained Language Models
Parameter-efficient tuning (PET) methods fit pre-trained language models (PLMs) to downstream tasks by either computing a small compressed update for a subset of model parameters, or appending and fine-tuning a small number of new model parameters to the pre-trained network.
ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation
Paraphrase generation is a long-standing task in natural language processing (NLP).
Measuring and Mitigating Local Instability in Deep Neural Networks
We formulate the model stability problem by studying how the predictions of a model change, even when it is retrained on the same data, as a consequence of stochasticity in the training process.
Unsupervised Syntactically Controlled Paraphrase Generation with Abstract Meaning Representations
In this paper, we demonstrate that leveraging Abstract Meaning Representations (AMR) can greatly improve the performance of unsupervised syntactically controlled paraphrase generation.
Evaluating the Effectiveness of Efficient Neural Architecture Search for Sentence-Pair Tasks
Neural Architecture Search (NAS) methods, which automatically learn entire neural model or individual neural cell architectures, have recently achieved competitive or state-of-the-art (SOTA) performance on variety of natural language processing and computer vision tasks, including language modeling, natural language inference, and image classification.
FlagIt: A System for Minimally Supervised Human Trafficking Indicator Mining
In this paper, we describe and study the indicator mining problem in the online sex advertising domain.
Temporal Learning and Sequence Modeling for a Job Recommender System
We present our solution to the job recommendation task for RecSys Challenge 2016.
