Text Generation from Keywords

Text Generation from Keywords Kiyotaka Uchimoto† Satoshi Sekine‡ Hitoshi Isahara† †Communications Research Laboratory 2-2-2, Hikari-dai, Seika-cho, Soraku-gun, Kyoto, 619-0289, Japan {uchimoto,isahara}@crl.go.jp ‡New York University 715 Broadway, 7th floor New York, NY 10003, USA sekine@cs.nyu.edu Abstract We describe a method for generating sentences from “keywords” or “headwords”. This method consists of two main parts, candidate-text con- struction and evaluation. The construction part generates text sentences in the form of depen- dency trees by using complementary informa- tion to replace information that is missing be- cause of a “knowledge gap” and other missing function words to generate natural text sen- tences based on a particular monolingual cor- pus. The evaluation part consists of a model for generating an appropriate text when given keywords. This model considers not only word n-gram information, but also dependency infor- mation between words. Furthermore, it consid- ers both string information and morphological information. 1 Introduction Text generation is an important technique used for applications like machine translation, sum- marization, and human/computer dialogue. In recent years, many corpora have become avail- able, and have been used to generate natural surface sentences. For example, corpora have been used to generate sentences for language model estimation in statistical machine trans- lation. In such translation, given a source lan- guage text, S, the translated text, T, in the target language that maximizes the probabil- ity P(T|S) is selected as the most appropri- ate translation, Tbest, which is represented as (Brown et al., 1990) Tbest = argmaxT P(T |S) = argmaxT (P(S|T ) × P(T )) . (1) In this equation, P(S|T) represents the model used to replace words or phrases in a source lan- guage with those in the target language. It is called a translation model. P(T) represents a language model that is used to reorder trans- lated words or phrases into a natural order in the target language. The input of the language model is a “bag of words,” and the goal of the model is basically to reorder the words. At this point, there is an assumption that natural sen- tences can be generated by merely reordering the words given by a translation model. To give such a complete set of words, however, a trans- lation model needs a large number of bilingual corpora. If we could automatically complement the words needed to generate natural sentences, we would not have to collect the large number of bilingual corpora required by a translation model. In this paper, we assume that the role of the translation model is not to give a complete set of words that can be used to generate nat- ural sentences, but to give a set of headwords or center words that a speaker might want to express, and describe a model that can provide the complementary information needed to gen- erate natural sentences by using a target lan- guage corpus when given a set of headwords. If we denote a set of headwords in a target language as K, we can express Eq. (1) as …

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