This paper describes the use of Multi-Task Neural Networks (NNs) for system dialogue act selection. These models leverage the representations learned by the Natural Language Understanding (NLU) unit to enable robust initialization/bootstrapping of dialogue policies from medium sized initial data sets. We evaluate the models on two goal-oriented dialogue corpora in the travel booking domain. Results show the proposed models improve over models trained without knowledge of NLU tasks.

This paper describes different aspects of an open competition to evaluate multicultural name matching software, including the contest design, development of the test data, different phases of the competition, behavior of the participating teams, results of the competition, and lessons learned throughout. The competition, known as The MITRE Challenge™, was informally announced at LREC 2010 and was recently concluded. Contest participants used the competition website (http://mitrechallenge.mitre.org) to download the competition data set and guidelines, upload results, and to view accuracy metrics for each result set submitted. Participants were allowed to submit unlimited result sets, with their top-scoring set determining their overall ranking. The competition website featured a leader board that displayed the top score for each participant, ranked according to the principal contest metric - mean average precision (MAP). MAP and other metrics were calculated in near-real time on a remote server, based on ground truth developed for the competition data set. Additional measures were taken to guard against gaming the competition metric or overfitting to the competition data set. Lessons learned during running this first MITRE Challenge will be valuable to others considering running similar evaluation campaigns.

This paper describes the development and evaluation of enhancements to the specialized information retrieval capabilities of a multimodal reporting system. The system enables collection and dissemination of information through a distributed data architecture by allowing users to input free text documents, which are indexed for subsequent search and retrieval by other users. This unstructured data entry method is essential for users of this system, but it requires an intelligent support system for processing queries against the data. The system, known as TIGR (Tactical Ground Reporting), allows keyword searching and geospatial filtering of results, but lacked the ability to efficiently index and search person names and perform approximate name matching. To improve TIGR’s ability to provide accurate, comprehensive results for queries on person names we iteratively updated existing entity extraction and name matching technologies to better align with the TIGR use case. We evaluated each version of the entity extraction and name matching components to find the optimal configuration for the TIGR context, and combined those pieces into a named entity extraction, indexing, and search module that integrates with the current TIGR system. By comparing system-level evaluations of the original and updated TIGR search processes, we show that our enhancements to personal name search significantly improved the performance of the overall information retrieval capabilities of the TIGR system.