Browsing by Subject "word embeddings"

In news agencies, there is a growing interest towards automated journalism. Majority of the systems applied are template- or rule-based, as they are expected to produce accurate and fluent output transparently. However, this approach often leads to output that lacks variety. To overcome this issue, I propose two approaches. In the lexicalization approach new words are included in the sentences, and in relexicalization approach some existing words are replaced with synonyms. Both of the approaches utilize contextual word embeddings for finding suitable words. Furthermore, the above approaches require linguistic resources, which are only available for high- resource languages. Thus, I present variants of the (re)lexicalization approaches that allow their utilization for low-resource languages. These variants utilize cross-lingual word embeddings to access linguistic resources of a high-resource language. The high-resource variants achieved promising results. However, the sampling of words should be further enhanced to improve reliability. The low-resource variants did show some promising results, but the quality suffered from complex morphology of the example language. This is a clear next issue to address and resolving it is expected to significantly improve the results.