Dhruv Batra


2020

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Where Are You? Localization from Embodied Dialog
Meera Hahn | Jacob Krantz | Dhruv Batra | Devi Parikh | James Rehg | Stefan Lee | Peter Anderson
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We present WHERE ARE YOU? (WAY), a dataset of ~6k dialogs in which two humans – an Observer and a Locator – complete a cooperative localization task. The Observer is spawned at random in a 3D environment and can navigate from first-person views while answering questions from the Locator. The Locator must localize the Observer in a detailed top-down map by asking questions and giving instructions. Based on this dataset, we define three challenging tasks: Localization from Embodied Dialog or LED (localizing the Observer from dialog history), Embodied Visual Dialog (modeling the Observer), and Cooperative Localization (modeling both agents). In this paper, we focus on the LED task – providing a strong baseline model with detailed ablations characterizing both dataset biases and the importance of various modeling choices. Our best model achieves 32.7% success at identifying the Observer’s location within 3m in unseen buildings, vs. 70.4% for human Locators.

2019

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Improving Generative Visual Dialog by Answering Diverse Questions
Vishvak Murahari | Prithvijit Chattopadhyay | Dhruv Batra | Devi Parikh | Abhishek Das
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Prior work on training generative Visual Dialog models with reinforcement learning ((Das et al., ICCV 2017) has explored a Q-Bot-A-Bot image-guessing game and shown that this ‘self-talk’ approach can lead to improved performance at the downstream dialog-conditioned image-guessing task. However, this improvement saturates and starts degrading after a few rounds of interaction, and does not lead to a better Visual Dialog model. We find that this is due in part to repeated interactions between Q-Bot and A-BOT during self-talk, which are not informative with respect to the image. To improve this, we devise a simple auxiliary objective that incentivizes Q-Bot to ask diverse questions, thus reducing repetitions and in turn enabling A-Bot to explore a larger state space during RL i.e. be exposed to more visual concepts to talk about, and varied questions to answer. We evaluate our approach via a host of automatic metrics and human studies, and demonstrate that it leads to better dialog, i.e. dialog that is more diverse (i.e. less repetitive), consistent (i.e. has fewer conflicting exchanges), fluent (i.e., more human-like), and detailed, while still being comparably image-relevant as prior work and ablations.

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CoDraw: Collaborative Drawing as a Testbed for Grounded Goal-driven Communication
Jin-Hwa Kim | Nikita Kitaev | Xinlei Chen | Marcus Rohrbach | Byoung-Tak Zhang | Yuandong Tian | Dhruv Batra | Devi Parikh
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

In this work, we propose a goal-driven collaborative task that combines language, perception, and action. Specifically, we develop a Collaborative image-Drawing game between two agents, called CoDraw. Our game is grounded in a virtual world that contains movable clip art objects. The game involves two players: a Teller and a Drawer. The Teller sees an abstract scene containing multiple clip art pieces in a semantically meaningful configuration, while the Drawer tries to reconstruct the scene on an empty canvas using available clip art pieces. The two players communicate with each other using natural language. We collect the CoDraw dataset of ~10K dialogs consisting of ~138K messages exchanged between human players. We define protocols and metrics to evaluate learned agents in this testbed, highlighting the need for a novel “crosstalk” evaluation condition which pairs agents trained independently on disjoint subsets of the training data. We present models for our task and benchmark them using both fully automated evaluation and by having them play the game live with humans.

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CLEVR-Dialog: A Diagnostic Dataset for Multi-Round Reasoning in Visual Dialog
Satwik Kottur | José M. F. Moura | Devi Parikh | Dhruv Batra | Marcus Rohrbach
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Visual Dialog is a multimodal task of answering a sequence of questions grounded in an image (using the conversation history as context). It entails challenges in vision, language, reasoning, and grounding. However, studying these subtasks in isolation on large, real datasets is infeasible as it requires prohibitively-expensive complete annotation of the ‘state’ of all images and dialogs. We develop CLEVR-Dialog, a large diagnostic dataset for studying multi-round reasoning in visual dialog. Specifically, we construct a dialog grammar that is grounded in the scene graphs of the images from the CLEVR dataset. This combination results in a dataset where all aspects of the visual dialog are fully annotated. In total, CLEVR-Dialog contains 5 instances of 10-round dialogs for about 85k CLEVR images, totaling to 4.25M question-answer pairs. We use CLEVR-Dialog to benchmark performance of standard visual dialog models; in particular, on visual coreference resolution (as a function of the coreference distance). This is the first analysis of its kind for visual dialog models that was not possible without this dataset. We hope the findings from CLEVR-Dialog will help inform the development of future models for visual dialog. Our code and dataset are publicly available.

2017

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The Promise of Premise: Harnessing Question Premises in Visual Question Answering
Aroma Mahendru | Viraj Prabhu | Akrit Mohapatra | Dhruv Batra | Stefan Lee
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

In this paper, we make a simple observation that questions about images often contain premises – objects and relationships implied by the question – and that reasoning about premises can help Visual Question Answering (VQA) models respond more intelligently to irrelevant or previously unseen questions. When presented with a question that is irrelevant to an image, state-of-the-art VQA models will still answer purely based on learned language biases, resulting in non-sensical or even misleading answers. We note that a visual question is irrelevant to an image if at least one of its premises is false (i.e. not depicted in the image). We leverage this observation to construct a dataset for Question Relevance Prediction and Explanation (QRPE) by searching for false premises. We train novel question relevance detection models and show that models that reason about premises consistently outperform models that do not. We also find that forcing standard VQA models to reason about premises during training can lead to improvements on tasks requiring compositional reasoning.

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Deal or No Deal? End-to-End Learning of Negotiation Dialogues
Mike Lewis | Denis Yarats | Yann Dauphin | Devi Parikh | Dhruv Batra
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Much of human dialogue occurs in semi-cooperative settings, where agents with different goals attempt to agree on common decisions. Negotiations require complex communication and reasoning skills, but success is easy to measure, making this an interesting task for AI. We gather a large dataset of human-human negotiations on a multi-issue bargaining task, where agents who cannot observe each other’s reward functions must reach an agreement (or a deal) via natural language dialogue. For the first time, we show it is possible to train end-to-end models for negotiation, which must learn both linguistic and reasoning skills with no annotated dialogue states. We also introduce dialogue rollouts, in which the model plans ahead by simulating possible complete continuations of the conversation, and find that this technique dramatically improves performance. Our code and dataset are publicly available.

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Natural Language Does Not Emerge ‘Naturally’ in Multi-Agent Dialog
Satwik Kottur | José Moura | Stefan Lee | Dhruv Batra
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

A number of recent works have proposed techniques for end-to-end learning of communication protocols among cooperative multi-agent populations, and have simultaneously found the emergence of grounded human-interpretable language in the protocols developed by the agents, learned without any human supervision! In this paper, using a Task & Talk reference game between two agents as a testbed, we present a sequence of ‘negative’ results culminating in a ‘positive’ one – showing that while most agent-invented languages are effective (i.e. achieve near-perfect task rewards), they are decidedly not interpretable or compositional. In essence, we find that natural language does not emerge ‘naturally’,despite the semblance of ease of natural-language-emergence that one may gather from recent literature. We discuss how it is possible to coax the invented languages to become more and more human-like and compositional by increasing restrictions on how two agents may communicate.

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ParlAI: A Dialog Research Software Platform
Alexander Miller | Will Feng | Dhruv Batra | Antoine Bordes | Adam Fisch | Jiasen Lu | Devi Parikh | Jason Weston
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

We introduce ParlAI (pronounced “par-lay”), an open-source software platform for dialog research implemented in Python, available at http://parl.ai. Its goal is to provide a unified framework for sharing, training and testing dialog models; integration of Amazon Mechanical Turk for data collection, human evaluation, and online/reinforcement learning; and a repository of machine learning models for comparing with others’ models, and improving upon existing architectures. Over 20 tasks are supported in the first release, including popular datasets such as SQuAD, bAbI tasks, MCTest, WikiQA, QACNN, QADailyMail, CBT, bAbI Dialog, Ubuntu, OpenSubtitles and VQA. Several models are integrated, including neural models such as memory networks, seq2seq and attentive LSTMs.

2016

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Question Relevance in VQA: Identifying Non-Visual And False-Premise Questions
Arijit Ray | Gordon Christie | Mohit Bansal | Dhruv Batra | Devi Parikh
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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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

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Human Attention in Visual Question Answering: Do Humans and Deep Networks look at the same regions?
Abhishek Das | Harsh Agrawal | Larry Zitnick | Devi Parikh | Dhruv Batra
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Resolving Language and Vision Ambiguities Together: Joint Segmentation & Prepositional Attachment Resolution in Captioned Scenes
Gordon Christie | Ankit Laddha | Aishwarya Agrawal | Stanislaw Antol | Yash Goyal | Kevin Kochersberger | Dhruv Batra
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Analyzing the Behavior of Visual Question Answering Models
Aishwarya Agrawal | Dhruv Batra | Devi Parikh
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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A Corpus and Cloze Evaluation for Deeper Understanding of Commonsense Stories
Nasrin Mostafazadeh | Nathanael Chambers | Xiaodong He | Devi Parikh | Dhruv Batra | Lucy Vanderwende | Pushmeet Kohli | James Allen
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Visual Storytelling
Ting-Hao Kenneth Huang | Francis Ferraro | Nasrin Mostafazadeh | Ishan Misra | Aishwarya Agrawal | Jacob Devlin | Ross Girshick | Xiaodong He | Pushmeet Kohli | Dhruv Batra | C. Lawrence Zitnick | Devi Parikh | Lucy Vanderwende | Michel Galley | Margaret Mitchell
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2013

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A Systematic Exploration of Diversity in Machine Translation
Kevin Gimpel | Dhruv Batra | Chris Dyer | Gregory Shakhnarovich
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing