This paper is the arXiv version of the paper that appears in the proceedings of EMNLP 2019. The content of the main paper is the exactly same as in the proceedings (modulo citation updates). However, the evaluation method used to obtain the results in the main paper unfortunately induces non-deterministic agent behavior, which makes comparisons difficult. We provide additional results herein obtained via a deterministic evaluation scheme in Appendix G. All conclusions and qualitative claims made in the main paper are unaffected by this change of evaluation scheme, and still hold on the new results. We strongly recommend future work reference results in Appendix G when comparing with our methods. AbstractMobile agents that can leverage help from humans can potentially accomplish more complex tasks than they could entirely on their own. We develop "Help, Anna!" (HANNA), an interactive photo-realistic simulator in which an agent fulfills object-finding tasks by requesting and interpreting natural languageand-vision assistance. An agent solving tasks in a HANNA environment can leverage simulated human assistants, called ANNA (Automatic Natural Navigation Assistants), which, upon request, provide natural language and visual instructions to direct the agent towards the goals. To address the HANNA problem, we develop a memory-augmented neural agent that hierarchically models multiple levels of decision-making, and an imitation learning algorithm that teaches the agent to avoid repeating past mistakes while simultaneously predicting its own chances of making future progress. Empirically, our approach is able to ask for help more effectively than competitive baselines and, thus, attains higher task success rate on both previously seen and previously unseen environments. We publicly release code and data at https://github. com/khanhptnk/hanna . , et al. 2018a. On evaluation of embodied navigation agents. arXiv preprint arXiv:1807.06757. van den Hengel. 2018b. Visionand-language navigation: Interpreting visuallygrounded navigation instructions in real environments. In She. 2016. Collaborative language grounding toward situated human-robot dialogue. AI Magazine, 37(4):32-45.

Many models in natural language processing define probabilistic distributions over linguistic structures. We argue that (1) the quality of a model's posterior distribution can and should be directly evaluated, as to whether probabilities correspond to empirical frequencies; and (2) NLP uncertainty can be projected not only to pipeline components, but also to exploratory data analysis, telling a user when to trust and not trust the NLP analysis. We present a method to analyze calibration, and apply it to compare the miscalibration of several commonly used models. We also contribute a coreference sampling algorithm that can create confidence intervals for a political event extraction task. 1

Machine translation is a natural candidate problem for reinforcement learning from human feedback: users provide quick, dirty ratings on candidate translations to guide a system to improve. Yet, current neural machine translation training focuses on expensive human-generated reference translations. We describe a reinforcement learning algorithm that improves neural machine translation systems from simulated human feedback. Our algorithm combines the advantage actor-critic algorithm (Mnih et al., 2016) with the attention-based neural encoderdecoder architecture (Luong et al., 2015). This algorithm (a) is well-designed for problems with a large action space and delayed rewards, (b) effectively optimizes traditional corpus-level machine translation metrics, and (c) is robust to skewed, high-variance, granular feedback modeled after actual human behaviors.

We present Vision-based Navigation with Languagebased Assistance (VNLA), a grounded vision-language task where an agent with visual perception is guided via language to find objects in photorealistic indoor environments. The task emulates a real-world scenario in that (a) the requester may not know how to navigate to the target objects and thus makes requests by only specifying high-level endgoals, and (b) the agent is capable of sensing when it is lost and querying an advisor, who is more qualified at the task, to obtain language subgoals to make progress. To model language-based assistance, we develop a general framework termed Imitation Learning with Indirect Intervention (I3L), and propose a solution that is effective on the VNLA task. Empirical results show that this approach significantly improves the success rate of the learning agent over other baselines in both seen and unseen environments.Our code and data are publicly available at https: /

Intra-articular fluid is advantageous in the evaluation of patients with a suspected meniscal retear. MR arthrography with gadolinium-based contrast material is the most accurate imaging method for the diagnosis of meniscal retears.