259 citations · 972 across the 36 of their papers we have counts for
11 papers · 1 filter
Learning to Summarize Long Texts with Memory Compression and Transfer
Jaehong Park, Jonathan Pilault, Christopher Pal
We introduce Mem2Mem, a memory-to-memory mechanism for hierarchical recurrent neural network based encoder decoder architectures and we explore its use for abstractive document sum…
DuoRAT: Towards Simpler Text-to-SQL Models
Torsten Scholak, Raymond Li, Dzmitry Bahdanau +2
Recent neural text-to-SQL models can effectively translate natural language questions to corresponding SQL queries on unseen databases. Working mostly on the Spider dataset, resear…
On the impressive performance of randomly weighted encoders in summarization tasks
Jonathan Pilault, Jaehong Park, Christopher Pal
In this work, we investigate the performance of untrained randomly initialized encoders in a general class of sequence to sequence models and compare their performance with that of…
On Extractive and Abstractive Neural Document Summarization with Transformer Language Models
Sandeep Subramanian, Raymond Li, Jonathan Pilault +1
We present a method to produce abstractive summaries of long documents that exceed several thousand words via neural abstractive summarization. We perform a simple extractive step…
Interactive Language Learning by Question Answering
Xingdi Yuan, Marc-Alexandre Cote, Jie Fu +4
Humans observe and interact with the world to acquire knowledge. However, most existing machine reading comprehension (MRC) tasks miss the interactive, information-seeking componen…
Interactive Machine Comprehension with Information Seeking Agents
Xingdi Yuan, Jie Fu, Marc-Alexandre Cote +3
Existing machine reading comprehension (MRC) models do not scale effectively to real-world applications like web-level information retrieval and question answering (QA). We argue t…