| 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,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,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. | |
| 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. | |
| tech,20-4-H01-1055,ak | hand-crafting <term> knowledge-based generation | systems | </term> can be overcome by employing <term> | #1017 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. | |
| tech,8-1-H01-1068,ak | evaluation </term> of <term> spoken dialogue | systems | </term> . The three tiers measure <term> user | #1205 We describe a three-tiered approach for evaluation of spoken dialogue systems. | |
| generation of natural language </term> , current | systems | use <term> manual or semi-automatic methods | #1768 While paraphrasing is critical both for interpretation and generation of natural language, current systems use manual or semi-automatic methods to collect paraphrases. | ||
| tech,11-4-P01-1056,ak | performs better than the <term> rule-based | systems | </term> and the <term> baselines </term> , and | #2112 We show that the trainable sentence planner performs better than the rule-based systems and the baselines, and as well as the hand-crafted system. | |
| phrases </term> degrades the performance of our | systems | . In this paper , we introduce a <term> generative | #2666 Learning only syntactically motivated phrases degrades the performance of our systems. | ||
| tech,18-2-N03-1018,ak | <term> output </term> of <term> black-box OCR | systems | </term> in order to make it more useful for | #2733 The model is designed for use in error correction, with a focus on post-processing the output of black-box OCR systems in order to make it more useful for NLP tasks. | |
| tech,18-3-N03-1026,ak | quality </term> of <term> sentence condensation | systems | </term> . An <term> experimental evaluation | #2859 Furthermore, we propose the use of standard parser evaluation methods for automatically evaluating the summarization quality of sentence condensation systems. | |
| precision </term> and <term> recall </term> on both | systems | . Motivated by these arguments , we introduce | #4091 In this paper we formulate story link detection and new event detection as information retrieval task and hypothesize on the impact of precision and recall on both systems. | ||
| tech,8-1-P03-1031,ak | understanding process </term> in <term> spoken dialogue | systems | </term> . This process enables the <term> system | #4137 This paper concerns the discourse understanding process in spoken dialogue systems. | |
| tech,15-1-P03-1033,ak | </term> to each user in <term> spoken dialogue | systems | </term> . Unlike previous studies that focus | #4298 We address appropriate user modeling in order to generate cooperative responses to each user in spoken dialogue systems. | |
| tech,6-4-H05-1005,ak | use of multiple <term> machine translation | systems | </term> provides yet more <term> redundancy | #5230 Further, the use of multiple machine translation systems provides yet more redundancy, yielding different ways to realize that information in English. |
