RESEARCH

NLP

Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning

October 22, 2017

Abstract

We introduce the first goal-driven training for visual question answering and dialog agents. Specifically, we pose a cooperative ‘image guessing’ game between two agents – Q-BOT and A-BOT– who communicate in natural language dialog so that Q-BOT can select an unseen image from a lineup of images. We use deep reinforcement learning (RL) to learn the policies of these agents end-to-end – from pixels to multi-agent multi-round dialog to game reward.

We demonstrate two experimental results.

First, as a ‘sanity check’ demonstration of pure RL (from scratch), we show results on a synthetic world, where the agents communicate in ungrounded vocabularies, i.e., symbols with no pre-specified meanings (X, Y, Z). We find that two bots invent their own communication protocol and start using certain symbols to ask/answer about certain visual attributes (shape/color/style). Thus, we demonstrate the emergence of grounded language and communication among ‘visual’ dialog agents with no human supervision.

Second, we conduct large-scale real-image experiments on the VisDial dataset [5], where we perform supervised pretraining with human-dialog data and show that the RL fine-tuned agents significantly outperform their supervised counterparts. Interestingly, the RL Q-BOT learns to ask questions that A-BOT is good at, ultimately resulting in more informative dialog and a better team. Further, pretraining with human-dialog data (in English) ensures human-interpretability and scope for pairing these agents with humans.

Download the Paper

Related Publications

September 10, 2019

NLP

Bridging the Gap Between Relevance Matching and Semantic Matching for Short Text Similarity Modeling | Facebook AI Research

A core problem of information retrieval (IR) is relevance matching, which is to rank documents by relevance to a user’s query. On the other hand, many NLP problems, such as question answering and paraphrase identification, can be considered…

Jinfeng Rao, Linqing Liu, Yi Tay, Wei Yang, Peng Shi, Jimmy Lin

September 10, 2019

June 11, 2019

NLP

COMPUTER VISION

Adversarial Inference for Multi-Sentence Video Description | Facebook AI Research

While significant progress has been made in the image captioning task, video description is still in its infancy due to the complex nature of video data. Generating multi-sentence descriptions for long videos is even more challenging. Among the…

Jae Sung Park, Marcus Rohrbach, Trevor Darrell, Anna Rohrbach

June 11, 2019

May 17, 2019

NLP

Unsupervised Hyper-alignment for Multilingual Word Embeddings | Facebook AI Research

We consider the problem of aligning continuous word representations, learned in multiple languages, to a common space. It was recently shown that, in the case of two languages, it is possible to learn such a mapping without supervision. This…

Jean Alaux, Edouard Grave, Marco Cuturi, Armand Joulin

May 17, 2019

July 27, 2019

NLP

Unsupervised Question Answering by Cloze Translation | Facebook AI Research

Obtaining training data for Question Answering (QA) is time-consuming and resource-intensive, and existing QA datasets are only available for limited domains and languages. In this work, we explore to what extent high quality training data is…

Patrick Lewis, Ludovic Denoyer, Sebastian Riedel

July 27, 2019

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.