64 - Neural Network Models for Sentence Pair Tasks, with Wuwei Lan and Wei Xu

Best reproduction paper at COLING 2018, by Wuwei Lan and Wei Xu.
This paper takes a bunch of models for sentence pair classification
(including paraphrase identification, semantic textual similarity,
natural language inference / entailment, and answer sentence
selection for QA) and compares all of them on all tasks. There's a
very nice table in the paper showing the cross product of models
and datasets, and how by looking at the original papers this table
is almost empty; Wuwei and Wei fill in all of the missing values in
that table with their own experiments. This is a very nice piece of
work that helps us gain a broader understanding of how these models
perform in diverse settings, and it's awesome that COLING
explicitly asked for and rewarded this kind of paper, as it's not
your typical "come look at my shiny new model!" paper. Our
discussion with Wuwei and Wei covers what models and datasets the
paper looked at, why the datasets can be treated similarly (and
some reasons for why maybe they should be treated differently), the
differences between the models that were tested, and the
difficulties of reproducing someone else's model.
https://www.semanticscholar.org/paper/Neural-Network-Models-for-Paraphrase-Semantic-and-Lan-Xu/6c990c162816bff2133a8e0ed9719bd0f87ae9d9