Ming Zhao, Peter Anderson, Vihan Jain, Su Wang, Alexander Ku, Jason Baldridge, Eugene Ie. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. 2021.

Learning to follow instructions is of fundamental importance to autonomous agents for vision-and-language navigation (VLN).In this paper, we study how an agent can navigate long paths when learning from a corpus that consists of shorter ones.We show that existing state-of-the-art agents do not generalize well.To this end, we propose BabyWalk, a new VLN agent that is learned to navigate by decomposing long instructions into shorter ones (BabySteps) and completing them sequentially.A special design memory buffer is used by the agent to turn its past experiences into contexts for future steps.The learning process is composed of two phases.In the first phase, the agent uses imitation learning from demonstration to accomplish BabySteps.In the second phase, the agent uses curriculum-based reinforcement learning to maximize rewards on navigation tasks with increasingly longer instructions.We create two new benchmark datasets (of long navigation tasks) and use them in conjunction with existing ones to examine BabyWalk's generalization ability.Empirical results show that BabyWalk achieves state-of-the-art results on several metrics, in particular, is able to follow long instructions better.The codes and the datasets are released on our project page https://github.com/Sha-Lab/babywalk.

Learning to fuse vision and language information and representing them is an important research problem with many applications.Recent progresses have leveraged the ideas of pretraining (from language modeling) and attention layers in Transformers to learn representation from datasets containing images aligned with linguistic expressions that describe the images.In this paper, we propose learning representations from a set of implied, visually grounded expressions between image and text, automatically mined from those datasets.In particular, we use denotation graphs to represent how specific concepts (such as sentences describing images) can be linked to abstract and generic concepts (such as short phrases) that are also visually grounded.This type of generic-to-specific relations can be discovered using linguistic analysis tools.We propose methods to incorporate such relations into learning representation.We show that state-of-the-art multimodal learning models can be further improved by leveraging automatically harvested structural relations.The representations lead to stronger empirical results on downstream tasks of cross-modal image retrieval, referring expression, and compositional attribute-object recognition.Both our codes and the extracted denotation graphs on the Flickr30K and the COCO datasets are publically available on

We develop RecSim NG, a probabilistic platform that supports natural, concise specification and learning of models for multi-agent recommender systems simulation. RecSim NG is a scalable, modular, differentiable simulator implemented in Edward2 and TensorFlow.

Advances in learning and representations have reinvigorated work that connects language to other modalities.A particularly exciting direction is Vision-and-Language Navigation (VLN), in which agents interpret natural language instructions and visual scenes to move through environments and reach goals.Despite recent progress, current research leaves unclear how much of a role language understanding plays in this task, especially because dominant evaluation metrics have focused on goal completion rather than the sequence of actions corresponding to the instructions.Here, we highlight shortcomings of current metrics for the Room-to-Room dataset (Anderson et al., 2018b) and propose a new metric, Coverage weighted by Length Score (CLS).We also show that the existing paths in the dataset are not ideal for evaluating instruction following because they are direct-to-goal shortest paths.We join existing short paths to form more challenging extended paths to create a new data set, Room-for-Room (R4R).Using R4R and CLS, we show that agents that receive rewards for instruction fidelity outperform agents that focus on goal completion.

Vision-and-Language Navigation (VLN) tasks such as Room-to-Room (R2R) require machine agents to interpret natural language instructions and learn to act in visually realistic environments to achieve navigation goals. The overall task requires competence in several perception problems: successful agents combine spatio-temporal, vision and language understanding to produce appropriate action sequences. Our approach adapts pre-trained vision and language representations to relevant in-domain tasks making them more effective for VLN. Specifically, the representations are adapted to solve both a cross-modal sequence alignment and sequence coherence task. In the sequence alignment task, the model determines whether an instruction corresponds to a sequence of visual frames. In the sequence coherence task, the model determines whether the perceptual sequences are predictive sequentially in the instruction-conditioned latent space. By transferring the domain-adapted representations, we improve competitive agents in R2R as measured by the success rate weighted by path length (SPL) metric.

Reinforcement learning methods for recommender systems optimize recommendations for long-term user engagement. However, since users are often presented with slates of multiple items---which may have interacting effects on user choice---methods are required to deal with the combinatorics of the RL action space. We develop SlateQ, a decomposition of value-based temporal-difference and Q-learning that renders RL tractable with slates. Under mild assumptions on user choice behavior, we show that the long-term value (LTV) of a slate can be decomposed into a tractable function of its component item-wise LTVs. We demonstrate our methods in simulation, and validate the scalability and effectiveness of decomposed TD-learning on YouTube.

Creating and maintaining a platform for reliably producing and deploying machine learning models requires careful orchestration of many components---a learner for generating models based on training data, modules for analyzing and validating both data as well as models, and finally infrastructure for serving models in production. This becomes particularly challenging when data changes over time and fresh models need to be produced continuously. Unfortunately, such orchestration is often done ad hoc using glue code and custom scripts developed by individual teams for specific use cases, leading to duplicated effort and fragile systems with high technical debt.