6 citations · 9 across the 9 of their papers we have counts for
13 papers
How May I Help You? Using Neural Text Simplification to Improve Downstream NLP Tasks
Hoang Van, Zheng Tang, Mihai Surdeanu
The general goal of text simplification (TS) is to reduce text complexity for human consumption. This paper investigates another potential use of neural TS: assisting machines perf…
Cheap and Good? Simple and Effective Data Augmentation for Low Resource Machine Reading
Hoang Van, Vikas Yadav, Mihai Surdeanu
We propose a simple and effective strategy for data augmentation for low-resource machine reading comprehension (MRC). Our approach first pretrains the answer extraction components…
The Language of Food during the Pandemic: Hints about the Dietary Effects of Covid-19
Hoang Van, Ahmad Musa, Mihai Surdeanu +1
We study the language of food on Twitter during the pandemic lockdown in the United States, focusing on the two month period of March 15 to May 15, 2020. Specifically, we analyze o…
Using the Hammer Only on Nails: A Hybrid Method for Evidence Retrieval for Question Answering
Zhengzhong Liang, Yiyun Zhao, Mihai Surdeanu
Evidence retrieval is a key component of explainable question answering (QA). We argue that, despite recent progress, transformer network-based approaches such as universal sentenc…
Unsupervised Alignment-based Iterative Evidence Retrieval for Multi-hop Question Answering
Vikas Yadav, Steven Bethard, Mihai Surdeanu
Evidence retrieval is a critical stage of question answering (QA), necessary not only to improve performance, but also to explain the decisions of the corresponding QA method. We i…
Quick and (not so) Dirty: Unsupervised Selection of Justification Sentences for Multi-hop Question Answering
Vikas Yadav, Steven Bethard, Mihai Surdeanu
We propose an unsupervised strategy for the selection of justification sentences for multi-hop question answering (QA) that (a) maximizes the relevance of the selected sentences, (…