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Found 10 papers, 0 papers with code
Search Beyond Queries: Training Smaller Language Models for Web Interactions via Reinforcement Learning
This work focuses on training smaller language models as agents across various scenarios, systematically evaluating the impact of human demonstrations on the training process.
Zero-Shot Generalizable End-to-End Task-Oriented Dialog System using Context Summarization and Domain Schema
To overcome this challenge, we introduce a novel Zero-Shot generalizable end-to-end Task-oriented Dialog system, ZS-ToD, that leverages domain schemas to allow for robust generalization to unseen domains and exploits effective summarization of the dialog history.
Toward Open-domain Slot Filling via Self-supervised Co-training
Phase one acquires two sets of complementary pseudo labels automatically.
Personalizing Task-oriented Dialog Systems via Zero-shot Generalizable Reward Function
In this work, we propose a novel framework, P-ToD, to personalize task-oriented dialog systems capable of adapting to a wide range of user profiles in an unsupervised fashion using a zero-shot generalizable reward function.
MobileRec: A Large-Scale Dataset for Mobile Apps Recommendation
To facilitate research for app recommendation systems, we introduce a large-scale dataset, called MobileRec.
Effect of Balancing Data Using Synthetic Data on the Performance of Machine Learning Classifiers for Intrusion Detection in Computer Networks
Many of these researchers used datasets collected by various organizations to train ML models for predicting intrusions.
Generalized Zero-shot Intent Detection via Commonsense Knowledge
Thus, these models should seamlessly adapt and classify utterances with both seen and unseen intents -- unseen intents emerge after deployment and they do not have training data.
Linguistically-Enriched and Context-Aware Zero-shot Slot Filling
Thus, it is imperative that these models seamlessly adapt and fill slots from both seen and unseen domains -- unseen domains contain unseen slot types with no training data, and even seen slots in unseen domains are typically presented in different contexts.
App-Aware Response Synthesis for User Reviews
Solving the challenge by simply building a model per app (i. e., training with review-response pairs of a single app) may be insufficient because individual apps have limited review-response pairs, and such pairs typically lack the relevant information needed to respond to a new review.
Unsupervised Paraphrasing via Deep Reinforcement Learning
Our evaluation also shows that PUP achieves a great trade-off between semantic similarity and diversity of expression.
