We examine the implementation of clarification dialogues, a mechanism for ensuring that question answering systems take into account user goals by allowing them to ask series of related questions either by refining or expanding on previous questions with follow-up questions, in the context of open domain Question Answering systems. We develop an algorithm for clarification dialogue recognition through the analysis of collected data on clarification dialogues and examine the importance of clarification dialogue recognition for question answering. The algorithm is evaluated and shown to successfully recognize the start and continuation of clarification dialogues in 94% of cases. We then show the usefulness of the algorithm by demonstrating how the recognition of clarification dialogues can simplify the task of answer retrieval.

An analytical method for design and performance analysis of language models (LM) is described, and an example interactive software tool based on the technique is demonstrated. The LM performance analysis does not require on-line simulation or experimentation with the recognition system in which the LM is to employed. By exploiting parallels with signal detection theory, a profile of the LM as a function of the design parameters is given in a set of curves analogous to a receiver-operating-characteristic display.

As part of a larger project to develop an aid for writers that would help to eliminate stylistic inconsistencies within a document, we experimented with neural networks to find the points in a text at which its stylistic character changes. Our best results, well above baseline, were achieved with time-delay networks that used features related to the author's syntactic preferences, whereas low-level and vocabulary-based features were not found to be useful. An alternative approach with character bigrams was not successful.

One of the key issues in spoken-language translation is how to deal with unrestricted expressions in spontaneous utterances. We have developed a paraphraser for use as part of a translation system, and in this paper we describe the implementation of a Chinese paraphraser for a Chinese-Japanese spoken-language translation system. When an input sentence cannot be translated by the transfer engine, the paraphraser automatically transforms the sentence into alternative expressions until one of these alternatives can be translated by the transfer engine. Two primary issues must be dealt with in paraphrasing: how to determine new expressions, and how to retain the meaning of the input sentence. We use a pattern-based approach in which the meaning is retained to the greatest possible extent without deep parsing. The paraphrase patterns are acquired from a paraphrase corpus and human experience. The paraphrase instances are automatically extracted and then generalized into paraphrase patterns. A total of 1719 paraphrase patterns obtained using this method and an implemented paraphraser were used in a paraphrasing experiment. The results showed that the implemented paraphraser generated 1.7 paraphrases on average for each test sentence and achieved an accuracy of 88%.

Suppose you're a corporate vice president at a well-known international software company, and you want to check on the visibility of one of your leading researchers in the outside world. You're sitting at your desk, so the most obvious thing to do is to enter their name into a search engine. If the well-known international software company happened to be Microsoft, and if the leading researcher happened to be Microsoft's Susan Dumais, and if the search engine you decided to use happened to be Google, you might be surprised to find that the sponsored link that comes atop the search results is actually from Google itself, exhorting you to ‘Work on NLP at Google’, and alerting you to the fact that ‘Google is hiring experts in statistical language processing’.