Papers by Siddharth Karamcheti
Finding Generalizable Evidence by Learning to Convince Q&A Models (D19-1)
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| Challenge: | a system that finds the strongest supporting evidence for a given answer is proposed . a study using passage-based question-answering (QA) shows that agents select evidence that generalizes . |
| Approach: | They propose a system that finds the strongest supporting evidence for a given answer . they use passage-based question-answering (QA) as a testbed to train evidence agents . |
| Outcome: | The proposed system improves QA in a robust manner by using agent-selected evidence. |
Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering (2021.acl-long)
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| Challenge: | Currently, language-equipped vision systems such as VizWiz, TapTapSee, BeMyEyes, and CamFind are actively being deployed across a broad spectrum of users. |
| Approach: | They propose to identify collective outliers in active learning methods that are hard and often impossible for models to learn . they also propose to use visual inputs to identify these outlier examples as examples assigned low model confidence and prediction variability during training. |
| Outcome: | The proposed methods outperform random selection on visual question answering tasks. |
Learning to Speak and Act in a Fantasy Text Adventure Game (D19-1)
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Jack Urbanek, Angela Fan, Siddharth Karamcheti, Saachi Jain, Samuel Humeau, Emily Dinan, Tim Rocktäschel, Douwe Kiela, Arthur Szlam, Jason Weston
| Challenge: | Existing studies on grounded dialogue use only statistical regularities of text data, without explicit understanding of the world that the text describes. |
| Approach: | They propose a large-scale crowdsourced text adventure game as a research platform for studying grounded dialogue. |
| Outcome: | The proposed game allows agents to perceive, emote, and act whilst conducting dialogue with other agents. |