From db406f87123e035d8c02965f87a8b58c51258fe9 Mon Sep 17 00:00:00 2001
From: Jason Riesa <jason.riesa@gmail.com>
Date: Sat, 11 Jun 2016 11:11:41 -0700
Subject: [PATCH] Adds best papers justification from committee

---
 best_papers.html | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/best_papers.html b/best_papers.html
index 6c8d715..a53c69a 100644
--- a/best_papers.html
+++ b/best_papers.html
@@ -45,6 +45,7 @@ <h4>
                 <b>Improving sentence compression by learning to predict gaze</b><br>
                 Sigrid Klerke, Yoav Goldberg and Anders S&oslash;gaard
               </p>
+              <p>This paper shows how to use gaze measurements obtained via eye-tracking to improve models for deletion-based sentence compression. It uses information from different data sources, eliminating the need to reply on data that is doubly annotated with sentence-compression and eye-tracking information, and points to data utilization methodology that could lead to larger improvements in the future.</p>
               <br>
               <p><h4><b>Short Paper, Runners Up</b></h4></p>
               <p>
@@ -61,10 +62,12 @@ <h4>
                 <b>Feuding Families and Former Friends; Unsupervised Learning for Dynamic Fictional Relationships</b><br>
                 Mohit Iyyer, Anupam Guha, Snigdha Chaturvedi, Jordan Boyd-Graber and Hal Daum&eacute; III
               </p>
+              <p>This paper addresses a novel problem of modeling interpersonal states between characters and how they evolve overtime. It brings new insights into unsupervised relationship modeling which involves joint learning a set of relationship descriptors and their trajectories. It was selected to receive a best paper award for its originality.</p>
               <p>
                 <b>Learning to Compose Neural Networks for Question Answering</b><br>
                 Jacob Andreas, Marcus Rohrbach, Trevor Darrell and Dan Klein
               </p>
+              <p>This paper is noteworthy for the innovative algorithmic ideas which bring together the strengths of neural network models and compositional logical semantics, and it demonstrates this approach in two different experiments, text-based question answering as well as image-based question answering, showing impact in multiple communities.  The paper is pointing in a good direction for people to continue to expand, challenge and enhance this work.</p>
               <br>
               <p><h4><b>Long Paper, Runners Up</b></h4></p>
               <p>