Air Travel Information Service Research Articles

GLR parsing with multiple grammars for natural language queries

This article presents an approach for parsing natural language queries that integrates multiple subparsers and subgrammars, in contrast to the traditional single grammar and parser approach. In using LR(k) parsers for natural language processing, we are faced with the problem of rapid growth in parsing table sizes as the number of grammar rules increases. We propose to partition the grammar into multiple subgrammars, each having its own parsing table and parser. Grammar partitioning helps reduce the overall parsing table size when compared to using a single grammar. We used the GLR parser with an LR(1) parsing table in our framework because GLR parsers can handle ambiguity in natural language. A parser composition technique then combines the parsers' outputs to produce an overall parse that is the same as the output parse of single parser. Two different strategies were used for parser composition: (i) parser composition by cascading; and (ii) parser composition with predictive pruning.Our experiments were conducted with natural language queries from the ATIS (Air Travel Information Service) domain. We have manually translated the ATIS-3 corpora into Chinese, and consequently we could experiment with grammar partitioning on parallel linguistic corpora. For English, the unpartitioned ATIS grammar has 72,869 states in its parsing table, while the partitioned English grammar has 3,350 states in total. For Chinese, grammar partitioning reduced the overall parsing table size from 29,734 states to 3,894 states. Both results show that grammar partitioning greatly economizes on the overall parsing table size. Language understanding performances were also examined. Parser composition imparts a robust parsing capability in our framework, and hence obtains a higher understanding performance when compared to using a single GLR parser.

Speech act modeling in a spoken dialog system using a fuzzy fragment-class Markov model

In a spoken dialog system, it is an important problem for the computer to identify the speech act (SA) from a user's utterance due to the variability of spoken language. In this paper, a corpus-based fuzzy fragment-class Markov model (FFCMM) is proposed to model the syntactic characteristics of a speech act and used to choose the speech act candidates. A speech act verification process, that estimates the conditional probability of a speech act given a sequence of fragments, is used to verify the speech act candidate. Most main design procedures are statistical- and corpus-based to reduce manual work. In order to evaluate the proposed method, a spoken dialog system for air travel information service (ATIS) is investigated. The experiments were carried out using a test database from 25 speakers (15 male and 10 female). There are 480 dialogs, containing 3038 sentences in the test database. The experimental results show that the speech act identification rate can be improved by 10.5% using the FFCMM and speech act verification with a rejection rate of 6% compared to a baseline system.

Semiautomatic acquisition of semantic structures for understanding domain-specific natural language queries

This paper describes a methodology for semiautomatic grammar induction from unannotated corpora of information-seeking queries in a restricted domain. The grammar contains both semantic and syntactic structures, which are conducive to (spoken) natural language understanding. Our work aims to ameliorate the reliance of grammar development on expert handcrafting or on the availability of annotated corpora. To strive for reasonable coverage on real data, as well as portability across domains and languages, we adopt a statistical approach. Agglomerative clustering using the symmetrized divergence criterion groups words spatially. These words have similar left and right contexts and tend to form semantic classes. Agglomerative clustering using mutual information groups words temporally. These words tend to co-occur sequentially to form phrases or multiword entities. Our approach is amenable to the optional injection of prior knowledge to catalyze grammar induction. The resultant grammar is interpretable by humans and is amenable to hand-editing for refinement. Hence, our approach is semiautomatic in nature. Experiments were conducted using the ATIS (Air Travel Information Service) corpus and the semiautomatically-induced grammar G/sub SA/ is compared to an entirely handcrafted grammar G/sub H/. G/sub H/ took two months to develop and gave concept error rates of 7 percent and 11.3 percent, respectively, in language understanding of two test corpora. GSA took only three days to produce and gave concept errors of 14 percent and 12.2 percent on the corresponding test corpora. These results provide a desirable trade-off between language understanding performance and grammar development effort.

Language portability of a speech understanding system

An important problem in automatic speech understanding is the transport of an existing application system to a new language. Design choices are required to keep the cost and implementation time of the porting as low as possible. One of the bottlenecks in spoken language system development is represented by data collection. When moving an application from the original language to a new one, it is very important to exploit, as much as possible, the data collected in the first language. This paper discusses the construction of a speech-based Air Travel Information Service (ATIS) for Italian, starting from (American) English. The aim of the work is to maximize the use of data available in English for the ATIS task in the construction of the Italian system. All components will be examined and proposed strategies will be evaluated by experimental tests. By just using a small Italian corpus and the ATIS data available in English, an understanding error rate of 6·7% was obtained on Italian written transcriptions. By adapting to speakers acoustic models and by training a language model on translations of transcriptions, an understanding error rate of 17·9% was obtained by considering read as well as spontaneous Italian sentences.

Automatic classification of intonational phrase boundaries

The relationship between the intonational characteristics of an utterance and other features inferable from its text represents an important source of information both for speech recognition, to constrain the set of allowable hypotheses, and for speech synthesis, to assign intonational features appropriately from text. This work investigates the usefulness of a number of textual features and additional intonational features in predicting the location of one particular intonational feature—intonational phrase boundaries—in natural speech. The corpus for this investigation is 298 utterances from the 774 in the DARPA-collected Air Travel Information Service (ATIS) database. For statistical modeling, we employ classification and regression tree (CART) techniques. We achieve success rates of just over 90%, representing a major improvement over previous attempts at boundary prediction for spontaneous speech.