2 citations · 4 across the 6 of their papers we have counts for
6 papers
Peek Across: Improving Multi-Document Modeling via Cross-Document Question-Answering
Avi Caciularu, Matthew E. Peters, Jacob Goldberger +2
The integration of multi-document pre-training objectives into language models has resulted in remarkable improvements in multi-document downstream tasks. In this work, we propose…
Conformal Nucleus Sampling
Shauli Ravfogel, Yoav Goldberg, Jacob Goldberger
Language models generate text based on successively sampling the next word. A decoding procedure based on nucleus (top-) sampling chooses from the smallest possible set of words…
Long Context Question Answering via Supervised Contrastive Learning
Avi Caciularu, Ido Dagan, Jacob Goldberger +1
Long-context question answering (QA) tasks require reasoning over a long document or multiple documents. Addressing these tasks often benefits from identifying a set of evidence sp…
Proposition-Level Clustering for Multi-Document Summarization
Ori Ernst, Avi Caciularu, Ori Shapira +4
Text clustering methods were traditionally incorporated into multi-document summarization (MDS) as a means for coping with considerable information repetition. Particularly, cluste…
Domain Adaptation For Formant Estimation Using Deep Learning
Yehoshua Dissen, Joseph Keshet, Jacob Goldberger +1
In this paper we present a domain adaptation technique for formant estimation using a deep network. We first train a deep learning network on a small read speech dataset. We then f…
PMI Matrix Approximations with Applications to Neural Language Modeling
Oren Melamud, Ido Dagan, Jacob Goldberger
The negative sampling (NEG) objective function, used in word2vec, is a simplification of the Noise Contrastive Estimation (NCE) method. NEG was found to be highly effective in lear…