Representation Learning for Conversational Data using Discourse Mutual Information Maximization

Although many pretrained models exist for text or images, there have been relatively fewer attempts to train representations specifically for dialog understanding. Prior works usually relied on finetuned representations based on generic text representation models like BERT or GPT-2. But such language modeling pretraining objectives do not take the structural information of conversational text into consideration. Although generative dialog models can learn structural features too, we argue that the structure-unaware word-by-word generation is not suitable for effective conversation modeling. We empirically demonstrate that such representations do not perform consistently across various dialog understanding tasks. Hence, we propose a structure-aware Mutual Information based loss-function DMI (Discourse Mutual Information) for training dialog-representation models, that additionally captures the inherent uncertainty in response prediction. Extensive evaluation on nine diverse dialog modeling tasks shows that our proposed DMI-based models outperform strong baselines by significant margins.Comment: Preprint, 15 pages, To appear in NAACL 2022 (Main

Last Mile Delivery and Route Planning for Freight

This report analyzes anticipated list mile challenges in Indiana by using a scenario-based approach to develop forecasts of GDP growth and thus freight growth across industry clusters in Indiana counties; potential congestion implied by this growth; and a proactive plan to add capacity to alleviate the congestion. We use a quantitative approach to aggregate ramp level flows, industry cluster locations, county layout, and economic activity to develop our recommendations. We develop forecasts through the year 2050 based on long-term planning approaches used by other states (California, Ohio, and Utah). We use data from global databases that consider different possible geo-political scenarios and regulatory choices to scale it down to county-level impact. At the same time, we track industry cluster locations within each county, ramps from interstates, and distances to travel within the counties to reach freight destinations. The result is a report that combines macro trends with micro detail to develop potential capacity bottlenecks

tardis-sn/tardis: TARDIS v2023.10.20

<p>This release has been created automatically by the TARDIS continuous delivery pipeline.</p> <p>A complete list of changes for this release is available at <a href="https://github.com/tardis-sn/tardis/blob/master/CHANGELOG.md">CHANGELOG.md</a>.</p&gt

tardis-sn/tardis: TARDIS v2023.11.05

<p>This release has been created automatically by the TARDIS continuous delivery pipeline.</p> <p>A complete list of changes for this release is available at <a href="https://github.com/tardis-sn/tardis/blob/master/CHANGELOG.md">CHANGELOG.md</a>.</p&gt