no code implementations • IJCNLP 2019 • Sandeep Suntwal, Mithun Paul, Rebecca Sharp, Mihai Surdeanu
As expected, even though this method achieves high accuracy when evaluated in the same domain, the performance in the target domain is poor, marginally above chance. To mitigate this dependence on lexicalized information, we experiment with several strategies for masking out names by replacing them with their semantic category, coupled with a unique identifier to mark that the same or new entities are referenced between claim and evidence.
1 code implementation • NAACL 2019 • Rebecca Sharp, Adarsh Pyarelal, Benjamin Gyori, Keith Alcock, Egoitz Laparra, Marco A. Valenzuela-Esc{\'a}rcega, Ajay Nagesh, Vikas Yadav, John Bachman, Zheng Tang, Heather Lent, Fan Luo, Mithun Paul, Steven Bethard, Kobus Barnard, Clayton Morrison, Mihai Surdeanu
Building causal models of complicated phenomena such as food insecurity is currently a slow and labor-intensive manual process.
no code implementations • WS 2018 • Mithun Paul, Rebecca Sharp, Mihai Surdeanu
For example, such a system trained in the news domain may learn that a sentence like {``}Palestinians recognize Texas as part of Mexico{''} tends to be unsupported, but this fact (and its corresponding lexicalized cues) have no value in, say, a scientific domain.