A Commercial Perspective on Reference

I briefly describe some of the commercial work which Arria NLG is doing in referring expression algorithms, and highlight differences between what is commercially important (at least to Arria) and the NLG research literature. Arria’s focus is on high-quality algorithms for types of reference which are important in its systems. These algorithms need to be parametrisable for different genres and domains, usable in hybrid systems which include some canned text, and support variation.

[1]  Anja Belz,et al.  The GREC Challenges 2010: Overview and Evaluation Results , 2010, INLG.

[2]  Robert Dale,et al.  Computational Interpretations of the Gricean Maxims in the Generation of Referring Expressions , 1995, Cogn. Sci..

[3]  Kees van Deemter,et al.  Are we Bayesian referring expression generators , 2013 .

[4]  Ehud Reiter,et al.  An Architecture for Data-to-Text Systems , 2007, ENLG.

[5]  Albert Gatt,et al.  Attribute Selection for Referring Expression Generation: New Algorithms and Evaluation Methods , 2008, INLG.

[6]  Thiago Castro Ferreira,et al.  Task demands and individual variation in referring expressions , 2016, INLG.

[7]  Emiel Krahmer,et al.  Learning Preferences for Referring Expression Generation: Effects of Domain, Language and Algorithm , 2012, INLG.

[8]  Kees van Deemter.  Computational Models of Referring , 2019, The Oxford Handbook of Reference.

[9]  Emiel Krahmer,et al.  Squibs and Discussions: Real versus Template-Based Natural Language Generation: A False Opposition? , 2005, CL.

[10]  Kees van Deemter.  Designing Algorithms for Referring with Proper Names , 2016, INLG.

[11]  Ehud Reiter,et al.  Generating Approximate Geographic Descriptions , 2009, ENLG.

[12]  Kees van Deemter,et al.  Generating Expressions that Refer to Visible Objects , 2013, NAACL.

[13]  Rodrigo de Oliveira,et al.  Designing an Algorithm for Generating Named Spatial References , 2015, ENLG.

[14]  Emiel Krahmer,et al.  Computational Generation of Referring Expressions: A Survey , 2012, CL.

[15]  Somayajulu Sripada,et al.  A Case Study: NLG meeting Weather Industry Demand for Quality and Quantity of Textual Weather Forecasts , 2014, INLG.