Arjun Chandrasekaran


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How much coffee was consumed during EMNLP 2019? Fermi Problems: A New Reasoning Challenge for AI
Ashwin Kalyan | Abhinav Kumar | Arjun Chandrasekaran | Ashish Sabharwal | Peter Clark
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Many real-world problems require the combined application of multiple reasoning abilities—employing suitable abstractions, commonsense knowledge, and creative synthesis of problem-solving strategies. To help advance AI systems towards such capabilities, we propose a new reasoning challenge, namely Fermi Problems (FPs), which are questions whose answers can only be approximately estimated because their precise computation is either impractical or impossible. For example, “How much would the sea level rise if all ice in the world melted?” FPs are commonly used in quizzes and interviews to bring out and evaluate the creative reasoning abilities of humans. To do the same for AI systems, we present two datasets: 1) A collection of 1k real-world FPs sourced from quizzes and olympiads; and 2) a bank of 10k synthetic FPs of intermediate complexity to serve as a sandbox for the harder real-world challenge. In addition to question-answer pairs, the datasets contain detailed solutions in the form of an executable program and supporting facts, helping in supervision and evaluation of intermediate steps. We demonstrate that even extensively fine-tuned large-scale language models perform poorly on these datasets, on average making estimates that are off by two orders of magnitude. Our contribution is thus the crystallization of several unsolved AI problems into a single, new challenge that we hope will spur further advances in building systems that can reason.


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Punny Captions: Witty Wordplay in Image Descriptions
Arjun Chandrasekaran | Devi Parikh | Mohit Bansal
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

Wit is a form of rich interaction that is often grounded in a specific situation (e.g., a comment in response to an event). In this work, we attempt to build computational models that can produce witty descriptions for a given image. Inspired by a cognitive account of humor appreciation, we employ linguistic wordplay, specifically puns, in image descriptions. We develop two approaches which involve retrieving witty descriptions for a given image from a large corpus of sentences, or generating them via an encoder-decoder neural network architecture. We compare our approach against meaningful baseline approaches via human studies and show substantial improvements. Moreover, in a Turing test style evaluation, people find the image descriptions generated by our model to be slightly wittier than human-written witty descriptions when the human is subject to similar constraints as the model regarding word usage and style.

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Do explanations make VQA models more predictable to a human?
Arjun Chandrasekaran | Viraj Prabhu | Deshraj Yadav | Prithvijit Chattopadhyay | Devi Parikh
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

A rich line of research attempts to make deep neural networks more transparent by generating human-interpretable ‘explanations’ of their decision process, especially for interactive tasks like Visual Question Answering (VQA). In this work, we analyze if existing explanations indeed make a VQA model — its responses as well as failures — more predictable to a human. Surprisingly, we find that they do not. On the other hand, we find that human-in-the-loop approaches that treat the model as a black-box do.


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Sort Story: Sorting Jumbled Images and Captions into Stories
Harsh Agrawal | Arjun Chandrasekaran | Dhruv Batra | Devi Parikh | Mohit Bansal
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing