We present a novel method to correct automatically generated speech transcripts of talks and lecture videos using text from accompanying presentation slides. The approach finesses the challenges of dealing with technical terms which are often outside the vocabulary of speech recognizers. Further, we align the transcript to the slide word sequence so that we can improve the organization of closed captioning for hearing impaired users, and improve automatic highlighting or magnification for visually impaired users. For each speech segment associated with a slide, we construct a sequential Hidden Markov Model for the observed phonemes that follows slide word order, interspersed with text not on the slide. Incongruence between slide words and mistaken transcript words is accounted for using phoneme confusion probabilities. Hence, transcript words different from aligned high probability slide words can be corrected. Experiments on six talks show improvement in transcript accuracy and alignment with slide words.

This paper examines the inductive inference of a complex grammar with neural networks and specifically, the task considered is that of training a network to classify natural language sentences as grammatical or ungrammatical, thereby exhibiting the same kind of discriminatory power provided by the Principles and Parameters linguistic framework, or Government-and-Binding theory. Neural networks are trained, without the division into learned vs. innate components assumed by Chomsky (1956), in an attempt to produce the same judgments as native speakers on sharply grammatical/ungrammatical data. How a recurrent neural network could possess linguistic capability and the properties of various common recurrent neural network architectures are discussed. The problem exhibits training behavior which is often not present with smaller grammars and training was initially difficult. However, after implementing several techniques aimed at improving the convergence of the gradient descent backpropagation-through-time training algorithm, significant learning was possible. It was found that certain architectures are better able to learn an appropriate grammar. The operation of the networks and their training is analyzed. Finally, the extraction of rules in the form of deterministic finite state automata is investigated.

The principle known as 'free indexation' plays an important role in the determination of the referential properties of noun phrases in the principle-and-parameters language framework. First, by investigating the combinatorics of free indexation, we show that the problem of enumerating all possible indexings requires exponential time. Secondly, we exhibit a provably optimal free indexation algorithm.

Conjunctions are particularly difficult to parse in traditional, phrase-based grammars. This paper shows how a different representation, not based on tree structures, markedly improves the parsing problem for conjunctions. It modifies the union of phrase marker model proposed by Goodall [1984], where conjunction is considered as the linearization of a three-dimensional union of a non-tree based phrase marker representation. A PROLOG grammar for conjunctions using this new approach is given. It is far simpler and more transparent than a recent phrase-based extraposition parser conjunctions by Dahl and McCord [1984]. Unlike the Dahl and McCord or ATN SYSCONJ approach, no special trail machinery is needed for conjunction, beyond that required for analyzing simple sentences. While of comparable efficiency, the new approach unifies under a single analysis a host of related constructions: respectively sentences, right node raising, or gapping. Another advantage is that it is also completely reversible (without cuts), and therefore can be used to generate sentences.