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106										<h1>SWAGex for Commonsense Validation and Explanation</h1>
107										
108										<p><strong>01/27/2020</strong></p>
109									</header>
110									<h2>SWAGex for Commonsense Validation and Explanation</h2>
111
112<p><img class="image fit" src="https://user-images.githubusercontent.com/13065761/151680380-8d367cf0-8487-442b-a674-7ed3e9c1f445.jpeg" alt="" /></p>
113
114<h2 id="project-motivation">Project Motivation</h2>
115<p>Commonsense reasoning has long been a challenge in the field of Natural Language Processing (NLP). Recently, the emergence of large pre-trained language models achieving state-of-the-art results in several
116NLP tasks, such as BERT and GPT has also influenced commonsense tasks to regain popularity.</p>
117
118<p>Natural Language Inference (NLI) is one of the most popular tasks in commonsense reasoning research. It consists of determining if a hypothesis is correct, given a premise. Situations With Adversarial Generation (SWAG) task is closely related to NLI. It consists of determining the most appropriate ending given a start phase as a context. The start phrases and endings in SWAG dataset are video captions. The task is to find the ending that describes a possible (future) world that can follow the given sentence even when it is not strictly entailed. Making such inference necessitates a rich understanding of everyday physical
119situations.</p>
120
121<h2 id="summary">Summary</h2>
122<ul>
123  <li>Used multiple methods on the pre-trained language model BERT to recognize sentences against commonsense and justify the reasoning behind this decision.</li>
124  <li>
125    <p>Investigated the ability to transfer commonsense knowledge from SWAG to ComVE task by training a model for the Explanation task with Next Event Prediction data.</p>
126
127    <p><img class="image fit" src="https://user-images.githubusercontent.com/13065761/151681108-1bac2faf-030e-44d2-b38a-b701b1eb0bd1.png" alt="" />
128  <img class="image fit" src="https://user-images.githubusercontent.com/13065761/151681110-03301049-6762-482e-9b1d-f4fd3f27939d.png" alt="" /></p>
129  </li>
130</ul>
131
132<h2 id="project-results">Project Results</h2>
133<ul>
134  <li>Paper “SWAGex at SemEval-2020: Commonsense Explanation as Next Event Prediction”, appeared in Proceedings of the 14th International Workshop on Semantic Evaluation.</li>
135  <li>Poster presentation at COLING, in Dec 2020 (online)</li>
136</ul>
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