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Search Challenges in Natural Language Generation with Complex Optimization Objectives

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Abstract

Automatic natural language generation (NLG) is a difficult problem already when merely trying to come up with natural-sounding utterances. Ubiquituous applications, in particular companion technologies, pose the additional challenge of flexible adaptation to a user or a situation. This requires optimizing complex objectives such as information density, in combinatorial search spaces described using declarative input languages. We believe that AI search and planning is a natural match for these problems, and could substantially contribute to solving them effectively. We illustrate this using a concrete example NLG framework, give a summary of the relevant optimization objectives, and provide an initial list of research challenges.

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Notes

  1. Surprisal values based on [8] obtained from http://tinyurl.com/pltagdemo.

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Acknowledgments

This work was partially supported by the DFG excellence cluster EXC 284 “Multimodal Computing and Interaction”, the DFG collaborative research center SFB 1102 “Information Density and Linguistic Encoding”, as well as the EU FP7 Programme under Grant Areement No. 295261 (MEALS). We thank Maximilian Schwenger for discussions. We are also grateful to Almaz for great Eritrean food.

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Authors and Affiliations

  1. Saarland University, Saarbrücken, Germany

    Vera Demberg, Jörg Hoffmann, David M. Howcroft, Dietrich Klakow & Álvaro Torralba

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  1. Vera Demberg
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  2. Jörg Hoffmann
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  3. David M. Howcroft
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  4. Dietrich Klakow
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  5. Álvaro Torralba
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Correspondence to Jörg Hoffmann.

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Demberg, V., Hoffmann, J., Howcroft, D.M. et al. Search Challenges in Natural Language Generation with Complex Optimization Objectives. Künstl Intell 30, 63–69 (2016). https://doi.org/10.1007/s13218-015-0409-5

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