Pointwise Mutual Information Based Metric and Decoding Strategy for Faithful Generation in Document Grounded Dialogs

A major concern in using deep learning based generative models for document-grounded dialogs is the potential generation of responses that are not \textit{faithful} to the underlying document. Existing automated metrics used for evaluating the faithfulness of response with respect to the grounding document measure the degree of similarity between the generated response and the document's content. However, these automated metrics are far from being well aligned with human judgments. Therefore, to improve the measurement of faithfulness, we propose a new metric that utilizes (Conditional) Point-wise Mutual Information (PMI) between the generated response and the source document, conditioned on the dialogue. PMI quantifies the extent to which the document influences the generated response -- with a higher PMI indicating a more faithful response. We build upon this idea to create a new decoding technique that incorporates PMI into the response generation process to predict more faithful responses. Our experiments on the BEGIN benchmark demonstrate an improved correlation of our metric with human evaluation. We also show that our decoding technique is effective in generating more faithful responses when compared to standard decoding techniques on a set of publicly available document-grounded dialog datasets.Comment: EMNLP 202

The Role of Canyons in Strata Formation

This paper provides a spatial and temporal multi-scale approach of European submarine canyons. We fi rst present the long-term geologic view of European margins as related to controls on submarine canyon development. Then we discuss the extent to which submarine canyon systems resemble river systems because both essentially form drainage networks. Finally, we deal with the hortest-term, highestresolution scale to get a fl avor of the current functioning and health of modern submarine canyons in the northwestern Mediterranean Sea. Submarine canyons are unique features of the seafl oor whose existence was known by European fi shermen centuries ago, especially for those canyons that have their heads at short distance from shoreline. Popular names given to specifi c canyons in the different languages spoken in European coastal communities refer to the concepts of a"deep" or"trench." In the old times it was also common thinking that submarine canyons where so deep that nobody could measure their depth or even that they had no bottom. Submarine canyons are just one of the seven different types of seafl oor valleys identifi ed by Shepard (1973) in his pioneering morphogenetic classifi cation. Shepard (1973) defined submarine canyons as"steep-walled, sinuous valleys, with V-shaped cross sections, and relief comparable even to the largest of land canyons; tributaries are found in most of the canyons and rock outcrops abound on their walls." Canyons are features typical of continental slopes with their upper reaches and heads cut into the continental shelf

The Making of the NEAM Tsunami Hazard Model 2018 (NEAMTHM18)

The NEAM Tsunami Hazard Model 2018 (NEAMTHM18) is a probabilistic hazard model for tsunamis generated by earthquakes. It covers the coastlines of the North-eastern Atlantic, the Mediterranean, and connected seas (NEAM). NEAMTHM18 was designed as a three-phase project. The first two phases were dedicated to the model development and hazard calculations, following a formalized decision-making process based on a multiple-expert protocol. The third phase was dedicated to documentation and dissemination. The hazard assessment workflow was structured in Steps and Levels. There are four Steps: Step-1) probabilistic earthquake model; Step-2) tsunami generation and modeling in deep water; Step-3) shoaling and inundation; Step-4) hazard aggregation and uncertainty quantification. Each Step includes a different number of Levels. Level-0 always describes the input data; the other Levels describe the intermediate results needed to proceed from one Step to another. Alternative datasets and models were considered in the implementation. The epistemic hazard uncertainty was quantified through an ensemble modeling technique accounting for alternative models’ weights and yielding a distribution of hazard curves represented by the mean and various percentiles. Hazard curves were calculated at 2,343 Points of Interest (POI) distributed at an average spacing of ∼20 km. Precalculated probability maps for five maximum inundation heights (MIH) and hazard intensity maps for five average return periods (ARP) were produced from hazard curves. In the entire NEAM Region, MIHs of several meters are rare but not impossible. Considering a 2% probability of exceedance in 50 years (ARP≈2,475 years), the POIs with MIH >5 m are fewer than 1% and are all in the Mediterranean on Libya, Egypt, Cyprus, and Greece coasts. In the North-East Atlantic, POIs with MIH >3 m are on the coasts of Mauritania and Gulf of Cadiz. Overall, 30% of the POIs have MIH >1 m. NEAMTHM18 results and documentation are available through the TSUMAPS-NEAM project website (http://www.tsumaps-neam.eu/), featuring an interactive web mapper. Although the NEAMTHM18 cannot substitute in-depth analyses at local scales, it represents the first action to start local and more detailed hazard and risk assessments and contributes to designing evacuation maps for tsunami early warning.publishedVersio

MAi: An Intelligent Model Acquisition Interface for Interactive Specification of Dialogue Agents

The state of the art in automated conversational agents for enterprise (e.g. for customer support) require a lengthy design process with experts in the loop who have to figure out and specify complex conversation patterns. This demonstration looks at a prototype interface that aims to bring down the expertise required to design such agents as well as the time taken to do so. Specifically, we will focus on how a metawriter can assist the domain-writer during the design process and how complex conversation patterns can be derived from simplifying abstractions at the interface level

Improving read performance of Phase Change Memories via Write Cancellation and Write Pausing

Phase Change Memory (PCM) is emerging as a promising technology to build large-scale main memory systems in a cost-effective manner. A characteristic of PCM is that it has write latency much higher than read latency. A higher write latency can typically be tolerated using buffers. However, once a write request is scheduled for service to a bank, it can still cause increased latency for later arriving read requests to the same bank. We show that for the baseline PCM system with read-priority scheduling, the write requests increase the effective read latency to 2.3x (on average), causing significant performance degradation. To reduce the read latency of PCM devices under such scenarios, we propose adaptive Write Cancellation policies. Such policies can abort the processing of a scheduled write requests if a read request arrives to the same bank within a predetermined period. We also propose Write Pausing, which exploits the iterative write algorithms used in PCM to pause at the end of each write iteration to service any pending reads. For the baseline system, the proposed technique removes 75 % of the latency increase incurred by read requests and improves overall system performance by 46% (on average), while requiring negligible hardware and simple extensions to PCM controller.