1. What is NLG? NLG "is the process of deliberately constructing a natural language text in order to meet specified communicative goals". [McDonald 1992] Input: some underlying non-linguistic representation of information. Output: NL text, (speech/gesture/image).

2. Why Use NLG? There is content to be conveyed to user. Content is represented in a way that is incomprehensible when directly presented to end user. Content to be conveyed can be effectively understood in the form of an NL message. Too much variability between initial representation of content and corresponding NL message for "mail-merge" style solution.

3. Key Paper Robert Dale, Sabine Geldof and Jean- Philippe Prost, 2002, Generating more natural route descriptions, ANLP2002 (see pdf available on website). Contrasts route descriptions generated by mapblast (see www.mapblast.com)

4. Mail-Merge Style Route Description

5. Natural Route Description Leave the house and drive towards the Midway shops, at the end of the street turn right and then left at the roundabout. Drive along North road and take the third right turn, just after the 1st hump in the road. Go to the end of that road and then go straight ahead at the roundabout, there's a church on your left.

6. Differences 1.Humans often omit steps that automated systems include. 2.Humans typically include landmarks to identify turning points; automated systems describe points using distances/times from other points. 3.Humans typically produce complex clause structures. Automated systems produce a series of one-shot sentences.

7. Humans often omit steps... Continue over roundabout exiting on 398 Continue on 398 and go 1.1 miles Continue on roundabout exiting on 398 Continue on 398 and go 1.7 miles Continue over roundabout exiting on Strada Cantonale, 398

8. Proposed Architecture GIS Map Query Path Planner Sentence Plan Message Based Plan Path Based Plan Text PlannerSentence Planner Text Realisation

9. Path Planner Input: –query: description of start and end nodes. –map data Output: –path-based plan, i.e. sequence of arcs that join start node to end node. Algorithm: –search for spanning path –arc aggregation: a single output arc may correspond to several steps on the map.

10. Text Planner Input –sequence of paths on map Output –a message-based plan, ie a structure of messages from the underlying data source A message is "a piece of domain-specific semantic content that can be realised linguistically". corresponding to the largest linguistic fragments we need to generate the variety of texts that interest us. Different types of message.

11. Message Types Dale et-al. distinguishes between Points: –buildings, landmarks, objects Directions: –instructions to change direction Paths –continuous movements.

12. Messages are Formal Objects which can be linguistically realised { type: point nodeID: n21330 pointtype: start name: "Liverpool Street" poi: [n100, n102] }

13. Realised Message-Based Plan 1.Start at Liverpool Street. 2.Follow Liverpool Street for 86 meters. 3.You are at George Street. 4.Turn right. 5.Follow George Street for 230 meters. 6.You are at Bathurst Street. 7.Turn left. 8.Follow Bathurst Street for 8 meters. 9.You have arrived at your destination. 1.Point 2.Path 3.Point 4.Direction 5.Path 6.Point 7.Direction 8.Path 9.Point

14. Sentence Planner Input –Message-based plan steps, e.g. Start at Liverpool Street. Follow Liverpool Street for 86 meters. Output –Sentence based plan steps e.g. Start at Liverpool Street and follow it for 86 meters. Strategies –aggregation –referring expression generation

15. Aggregation Strategies in Sentence Planning Aggregation: building clauses which communicate several pieces of information at once. Path+Point: fold description of a point into the description of a path in order to identify end- point of path. carry on straight until you reach the square Point+Direction: Combine turn direction with specification of location where instruction is to be executed. when you reach the church turn left

16. Referring Expression Generation Develop strategies for different types of object, e.g. When referring to junction points: –use a landmark that is near the junction –use the type of the intersection –use the name of the intersecting street

17. Linguistic Realisation All strategic decisions mentioned so far are carried out with respect to data objects, not actual pieces of text. Last phase will be "realising" data object as an actual series of sentences.