2k citations · 2.1k across the 28 of their papers we have counts for
35 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…
Denoising Word Embeddings by Averaging in a Shared Space
Avi Caciularu, Ido Dagan, Jacob Goldberger
We introduce a new approach for smoothing and improving the quality of word embeddings. We consider a method of fusing word embeddings that were trained on the same corpus but with…
Speech enhancement with mixture-of-deep-experts with clean clustering pre-training
Shlomo E. Chazan, Jacob Goldberger, Sharon Gannot
In this study we present a mixture of deep experts (MoDE) neural-network architecture for single microphone speech enhancement. Our architecture comprises a set of deep neural netw…