| tech,18-1-H01-1017,ak | beyond current capabilities in <term> dialogue | systems | </term> , the DARPA Communicator program | #225 To support engaging human users in robust, mixed-initiative speech dialogue interactions which reach beyond current capabilities in dialogue systems, the DARPA Communicator program [1] is funding the development of a distributed message-passing infrastructure for dialogue systems which all Communicator participants are using. | |
| tech,38-1-H01-1017,ak | message-passing infrastructure </term> for <term> dialogue | systems | </term> which all Communicator participants | #245 To support engaging human users in robust, mixed-initiative speech dialogue interactions which reach beyond current capabilities in dialogue systems, the DARPA Communicator program [1] is funding the development of a distributed message-passing infrastructure for dialogue systems which all Communicator participants are using. | |
| tech,10-1-H01-1040,ak | from <term> information extraction ( IE ) | systems | </term> - <term> named entity annotations </term> | #289 In this paper we show how two standard outputs from information extraction (IE) systems - named entity annotations and scenario templates - can be used to enhance access to text collections via a standard text browser. | |
| tech,9-2-H01-1040,ak | information is used in a <term> prototype | system | </term> designed to support <term> information | #323 We describe how this information is used in a prototype system designed to support information workers' access to a pharmaceutical news archive as part of their industry watch function. | |
| tech,13-3-H01-1040,ak | qualitative user evaluation </term> of the <term> | system | </term> , which while broadly positive indicates | #357 We also report results of a preliminary, qualitative user evaluation of thesystem, which while broadly positive indicates further work needs to be done on the interface to make users aware of the increased potential of IE-enhanced text browsers. | |
| tech,10-1-H01-1041,ak | <term> Korean-to-English machine translation | system | CCLINC ( Common Coalition Language System | #399 At MIT Lincoln Laboratory, we have been developing a Korean-to-English machine translation system CCLINC (Common Coalition Language System at Lincoln Laboratory). | |
| tech,1-2-H01-1041,ak | <term> CCLINC Korean-to-English translation | system | </term> consists of two <term> core modules | #415 The CCLINC Korean-to-English translation system consists of two core modules, language understanding and generation modules mediated by a language neutral meaning representation called a semantic frame. | |
| tech,5-3-H01-1041,ak | frame </term> . The key features of the <term> | system | </term> include : ( i ) Robust efficient <term> | #444 The key features of thesystem include: (i) Robust efficient parsing of Korean (a verb final language with overt case markers, relatively free word order, and frequent omissions of arguments). | |
| tech,4-5-H01-1041,ak | target language </term> . ( iii ) Rapid <term> | system | development </term> and <term> porting to new | #501 (iii) Rapidsystem development and porting to new domains via knowledge-based automated acquisition of grammars. | |
| tech,15-6-H01-1041,ak | chemical biological warfare , the <term> | system | </term> produces the <term> translation output | #530 Having been trained on Korean newspaper articles on missiles and chemical biological warfare, thesystem produces the translation output sufficient for content understanding of the original document. | |
| tech,30-1-H01-1042,ak | </term> of <term> machine translation ( MT ) | systems | </term> . We believe that these <term> evaluation | #579 The purpose of this research is to test the efficacy of applying automated evaluation techniques, originally devised for the evaluation of human language learners, to the output of machine translation (MT) systems. | |
| tech,24-2-H01-1042,ak | development of <term> machine translation | systems | </term> . This , the first experiment in | #607 We believe that these evaluation techniques will provide information about both the human language learning process, the translation process and the development of machine translation systems. | |
| tech,3-2-H01-1049,ak | integrate a <term> spoken language understanding | system | </term> with <term> intelligent mobile agents | #801 We integrate a spoken language understanding system with intelligent mobile agents that mediate between users and information sources. | |
| personnel can converse with their logistics | system | to place a <term> supply or information request | #838 Using LCS-Marine, tactical personnel can converse with their logistics system to place a supply or information request. | ||
| tech,5-6-H01-1049,ak | Requestors </term> can also instruct the <term> | system | </term> to notify them when the status of | #869 Requestors can also instruct thesystem to notify them when the status of a request changes or when a request is complete. | |
| tech,14-1-H01-1055,ak | the goal of naturally sounding <term> dialog | systems | </term> within reach . However , the improved | #926 Recent advances in Automatic Speech Recognition technology have put the goal of naturally sounding dialog systems within reach. | |
| tech,15-2-H01-1055,ak | to light a new problem : as <term> dialog | systems | </term> understand more of what the <term> | #946 However, the improved speech recognition has brought to light a new problem: as dialog systems understand more of what the user tells them, they need to be more sophisticated at responding to the user. | |
| other,3-3-H01-1055,ak | the <term> user </term> . The issue of <term> | system | response </term> to <term> users </term> has | #971 The issue ofsystem response to users has been extensively studied by the natural language generation community, though rarely in the context of dialog systems. | |
| tech,24-3-H01-1055,ak | though rarely in the context of <term> dialog | systems | </term> . We show how research in <term> generation | #993 The issue of system response to users has been extensively studied by the natural language generation community, though rarely in the context of dialog systems. | |
| tech,10-4-H01-1055,ak | generation </term> can be adapted to <term> dialog | systems | </term> , and how the high cost of hand-crafting | #1006 We show how research in generation can be adapted to dialog systems, and how the high cost of hand-crafting knowledge-based generation systems can be overcome by employing machine learning techniques. |
