10 citations · 17 across the 5 of their papers we have counts for
8 papers · 1 filter
Efficient Unsupervised Sentence Compression by Fine-tuning Transformers with Reinforcement Learning
Demian Gholipour Ghalandari, Chris Hokamp, Georgiana Ifrim
Sentence compression reduces the length of text by removing non-essential content while preserving important facts and grammaticality. Unsupervised objective driven methods for sen…
DynE: Dynamic Ensemble Decoding for Multi-Document Summarization
Chris Hokamp, Demian Gholipour Ghalandari, Nghia The Pham +1
Sequence-to-sequence (s2s) models are the basis for extensive work in natural language processing. However, some applications, such as multi-document summarization, multi-modal mac…
A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portal
Demian Gholipour Ghalandari, Chris Hokamp, Nghia The Pham +2
Multi-document summarization (MDS) aims to compress the content in large document collections into short summaries and has important applications in story clustering for newsfeeds,…
Evaluating the Supervised and Zero-shot Performance of Multi-lingual Translation Models
Chris Hokamp, John Glover, Demian Gholipour
We study several methods for full or partial sharing of the decoder parameters of multilingual NMT models. We evaluate both fully supervised and zero-shot translation performance i…
Off-the-Shelf Unsupervised NMT
Chris Hokamp, Sebastian Ruder, John Glover
We frame unsupervised machine translation (MT) in the context of multi-task learning (MTL), combining insights from both directions. We leverage off-the-shelf neural MT architectur…
Generating High-Quality Surface Realizations Using Data Augmentation and Factored Sequence Models
Henry Elder, Chris Hokamp
This work presents a new state of the art in reconstruction of surface realizations from obfuscated text. We identify the lack of sufficient training data as the major obstacle to…