Implicitly Supervised Language Model Adaptation for Meeting Transcription

We describe the use of meeting metadata, acquired using a computerized meeting organization and note-taking system, to improve automatic transcription of meetings. By applying a two-step language model adaptation process based on notes and agenda items, we were able to reduce perplexity by 9 % and word error rate by 4 % relative on a set of ten meetings recorded in-house. This approach can be used to leverage other types of metadata.

Expanding the use of natural and nature-based infrastructure to enhance coastal resiliency: Forecast and hind-cast load reductions from Living shoreline BMPs : Project Report (Year 2 of 3)

The vulnerability of coastal communities and the growing risks to coastal infrastructure continue largely due to past and ongoing patterns of development in high risk areas. This project is focused on increasing the use of natural and nature-based features (NNBFs) to increase resilience of coastal communities to flooding caused by extreme weather events. This project has proposed two efforts to increase understanding of NNBFS; 1) describe the current status, and 2) quantify role of NNBF creation/ restoration for water quality benefits in support of coastal resilience. The products of the 3-year project are intended to support informed coastal management decision-making regarding two concerns associated with NNBFs: The natural capital of coastal communities is generally declining, and is projected to decline at an accelerating rate due to sea level rise and current land use practices. The use of NNBFs to sustain or increase resilience in coastal communities is restricted by the many competing needs for limited local resources

Learning to Ask Questions for Zero-shot Dialogue State Tracking

CMU Portugal project iFetch (LISBOA-01-0247-FEDER-045920). Publisher Copyright: © 2023 Copyright held by the owner/author(s).We present a method for performing zero-shot Dialogue State Tracking (DST) by casting the task as a learning-to-ask-questions framework. The framework learns to pair the best question generation (QG) strategy with in-domain question answering (QA) methods to extract slot values from a dialogue without any human intervention. A novel self-supervised QA pretraining step using in-domain data is essential to learn the structure without requiring any slot-filling annotations. Moreover, we show that QG methods need to be aligned with the same grammatical person used in the dialogue. Empirical evaluation on the MultiWOZ 2.1 dataset demonstrates that our approach, when used alongside robust QA models, outperforms existing zero-shot methods in the challenging task of zero-shot cross domain adaptation-given a comparable amount of domain knowledge during data creation. Finally, we analyze the impact of the types of questions used, and demonstrate that the algorithmic approach outperforms template-based question generation.publishersversionpublishe

Natural Language, Mixed-Initiative Personal Assistant Agents

The increasing popularity and use of personal voice assistant technologies, such as Siri and Google Now, is driving and expanding progress toward the long-term and lofty goal of using artificial intelligence to build human-computer dialog systems capable of understanding natural language. While dialog-based systems such as Siri support utterances communicated through natural language, they are limited in the flexibility they afford to the user in interacting with the system and, thus, support primarily action-requesting and information-seeking tasks. Mixed-initiative interaction, on the other hand, is a flexible interaction technique where the user and the system act as equal participants in an activity, and is often exhibited in human-human conversations. In this paper, we study user support for mixed-initiative interaction with dialog-based systems through natural language using a bag-of-words model and k-nearest-neighbor classifier. We study this problem in the context of a toolkit we developed for automated, mixed-initiative dialog system construction, involving a dialog authoring notation and management engine based on lambda calculus, for specifying and implementing task-based, mixed-initiative dialogs. We use ordering at Subway through natural language, human-computer dialogs as a case study. Our results demonstrate that the dialogs authored with our toolkit support the end user\u27s completion of a natural language, human-computer dialog in a mixed-initiative fashion. The use of natural language in the resulting mixed-initiative dialogs afford the user the ability to experience multiple self-directed paths through the dialog and makes the flexibility in communicating user utterances commensurate with that in dialog completion paths---an aspect missing from commercial assistants like Siri

Piankatank River Shoreline Situation Report

This shoreline inventory has been developed as a tool for assessing conditions along the tidal shoreline of the river, and tributaries in the Piankatank River Watershed. Recent conditions are reported for three zones within the immediate riparian river area: riparian land use, bank and buffers, and the shoreline. A series of maps and tabular data are published to illustrate and quantify results of an extensive survey in the watershed. This survey extends from the mouth of the Dragon Run to the mouth of the Piankatank River, at the confluence with the Chesapeake Bay. Coverage extends slightly south and east, including regions surrounding Gwynn Island in Mathews County (Figure I)

Expanding The Use Of Natural And Nature-Based Infrastructure To Enhance Coastal Resiliency

The vulnerability of coastal communities and the growing risks of coastal infrastructure continue largely due to past and ongoing patterns of development in high risk areas. This project is focused on increasing the use of natural and nature-based features (NNBFs) to increase resilience of coastal communities to flooding caused by extreme weather events