Papers by Arjun Chandrasekaran
How much coffee was consumed during EMNLP 2019? Fermi Problems: A New Reasoning Challenge for AI (2021.emnlp-main)
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| Challenge: | a new reasoning challenge is proposed to help AI systems to solve real-world problems . Fermi Problems are questions whose answers can only be approximated because their computation is either impossible or impossible. |
| Approach: | They propose a new reasoning challenge, Fermi Problems, which asks questions whose answers can only be approximated because their computation is either impractical or impossible. |
| Outcome: | The proposed datasets show that even fine-tuned large-scale language models perform poorly on these datasets. |
Punny Captions: Witty Wordplay in Image Descriptions (N18-2)
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| Challenge: | Developing computational models that can produce contextually witty image descriptions is challenging because of the large corpus of sentences that are not available for large scale corpora. |
| Approach: | They propose to use linguistic wordplay, specifically puns, to generate witty image descriptions from large corpus of sentences or encode them via an encoder-decoder neural network architecture. |
| Outcome: | The proposed models perform better than baseline models using human data and show that they are slightly wittier than human-written witty descriptions. |
Do explanations make VQA models more predictable to a human? (D18-1)
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| Challenge: | Existing explanations of a model's behavior are not used in interactive tasks like Visual Question Answering (VQA). |
| Approach: | They analyze existing explanations and their role in making a VQA model more predictable to a human by using human-in-the-loop approaches that treat the model as a black-box. |
| Outcome: | The proposed explanations make a model more predictable to humans, whereas human-in-the-loop approaches treat it as a black-box do. |