Search Results for author:
Found 9 papers, 2 papers with code
Revisiting text decomposition methods for NLI-based factuality scoring of summaries
In this paper we systematically compare different granularities of decomposition -- from document to sub-sentence level, and we show that the answer is no.
DynE: Dynamic Ensemble Decoding for Multi-Document Summarization
Sequence-to-sequence (s2s) models are the basis for extensive work in natural language processing.
A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portal
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, presentation of search results, and timeline generation.
Task Selection Policies for Multitask Learning
One of the questions that arises when designing models that learn to solve multiple tasks simultaneously is how much of the available training budget should be devoted to each individual task.
Evaluating the Supervised and Zero-shot Performance of Multi-lingual Translation Models
To our knowledge, this is the largest evaluation of multi-lingual translation yet conducted in terms of the total size of the training data we use, and in terms of the diversity of zero-shot translation pairs we evaluate.
Off-the-Shelf Unsupervised NMT
We frame unsupervised machine translation (MT) in the context of multi-task learning (MTL), combining insights from both directions.
360\mbox$^\circ$ Stance Detection
To ameliorate this, we propose 360{\mbox{$^\circ$}} Stance Detection, a tool that aggregates news with multiple perspectives on a topic.
360° Stance Detection
To ameliorate this, we propose 360{\deg} Stance Detection, a tool that aggregates news with multiple perspectives on a topic.
Modeling documents with Generative Adversarial Networks
This paper describes a method for using Generative Adversarial Networks to learn distributed representations of natural language documents.
