Nikolaos Pappas

Plug and Play Autoencoders for Conditional Text Generation

2020

Text autoencoders are commonly used for conditional generation tasks such as style transfer.We propose methods which are plug and play, where any pretrained autoencoder can be used, and only require learning a mapping within the …

Deep Encoder, Shallow Decoder: Reevaluating Non-autoregressive Machine Translation

2020 · arXiv (Cornell University)

Much recent effort has been invested in non-autoregressive neural machine translation, which appears to be an efficient alternative to state-of-the-art autoregressive machine translation on modern GPUs. In contrast to the latter, where generation is sequential, …

Sense-Aware Statistical Machine Translation using Adaptive Context-Dependent Clustering

2017 · Zenodo (CERN European Organization for Nuclear Research)

Statistical machine translation (SMT) systems use local cues from n-gram translation and language models to select the translation of each source word. Such systems do not explicitly perform word sense disambiguation (WSD), although this would …

Battery-aware Cyclic Scheduling in Energy-harvesting Federated Learning

2025 · arXiv (Cornell University)

Federated Learning (FL) has emerged as a promising framework for distributed learning, but its growing complexity has led to significant energy consumption, particularly from computations on the client side. This challenge is especially critical in …

Towards Long Context Hallucination Detection

2025 · arXiv (Cornell University)

Large Language Models (LLMs) have demonstrated remarkable performance across various tasks. However, they are prone to contextual hallucination, generating information that is either unsubstantiated or contradictory to the given context. Although many studies have investigated …

Document-Level Neural Machine Translation with Hierarchical Attention Networks

2018

Neural Machine Translation (NMT) can be improved by including document-level contextual information. For this purpose, we propose a hierarchical attention model to capture the context in a structured and dynamic manner. The model is integrated …

GILE: A Generalized Input-Label Embedding for Text Classification

2019 · Transactions of the Association for Computational Linguistics

Neural text classification models typically treat output labels as categorical variables that lack description and semantics. This forces their parametrization to be dependent on the label set size, and, hence, they are unable to scale …