Aram Galstyan

Capturing Edge Attributes via Network Embedding

2018 · IEEE Transactions on Computational Social Systems

Network embedding, which aims to learn low-dimensional representations of nodes, has been used for various graph related tasks including visualization, link prediction, and node classification. Most existing embedding methods rely solely on network structure. However, …

Domain Agnostic Prototypical Distribution for Unsupervised Model Adaptation

2020 · arXiv (Cornell University)

We develop an algorithm for adaptation of a classifier from a labeled source domain to an unlabeled target domain in a sequential learning setting. This problem has been studied extensively in unsupervised domain adaptation (UDA) …

Lawyers are Dishonest? Quantifying Representational Harms in Commonsense Knowledge Resources

2021 · arXiv (Cornell University)

Warning: this paper contains content that may be offensive or upsetting. Numerous natural language processing models have tried injecting commonsense by using the ConceptNet knowledge base to improve performance on different tasks. ConceptNet, however, is …

Think Clearly: Improving Reasoning via Redundant Token Pruning

2025 · arXiv (Cornell University)

Recent large language models have shown promising capabilities in long-form reasoning, following structured chains of thought before arriving at a final answer. However, we observe that these reasoning paths tend to include substantial redundancy; analyzing …

Better Automatic Evaluation of Open-Domain Dialogue Systems with Contextualized Embeddings

2019

Despite advances in open-domain dialogue systems, automatic evaluation of such systems is still a challenging problem. Traditional reference-based metrics such as BLEU are ineffective because there could be many valid responses for a given context …