Collection of J apanese Route Information Reference Expressions Using Maps as Stimuli (2024)

Abstract: We constructed a database of Japanese expressions based on route information. Using 20 maps as stimuli, we requested descriptions of routes between two points on each map from 40 individuals per route, collecting 1600 route information reference expressions. We determined whether the expressions were based solely on relative reference expressions by using landmarks on the maps. In cases in which only relative reference expressions were used, we labeled the presence or absence of information regarding the starting point, waypoints, and destination. Additionally, we collected clarity ratings for each expression using a survey.

## introduction accurately conveying route information in a language is challenging because it comprises details regarding the starting point, waypoints, and destination. utilizing surrounding landmarks is crucial for effectively conveying positional information, movement direction, and distance. in languages where case elements such as subjects tend to be omitted (e.g., japanese), it is difficult to generate clear expressions of route information. by collecting japanese route information reference expressions using maps as stimuli, this study aims to shed light on important perspectives for conveying route information for language-based direction instructions to autonomous driving systems. for each of the 20 maps, two starting and ending point patterns were established, resulting in 40 stimuli each. through crowdsourcing, we sought to gather 40 expressions of route information references per map, articulated solely using specific or relative location information. following the determination of whether the collected language expressions comprised only specific or relative location information, we annotated the in-clusion of starting point, waypoint, or destination information. furthermore, the clarity ratings for each expression were obtained through surveys. ## related research early work on the analysis of direction-giving conversations was conducted by @xcite , who identified three stages: situating, specifying information and directions, and concluding. they pointed out that during the initial directions, a) starting point, b) destination, c) mode of travel, d) time of travel, and e) membership categorization by the parties of each other are important. in a related study, clark and wilkes-gibbs (1986) introduced a collaborative model to make definite references in conversations. in their model, speakers initiate the process by presenting a noun phrase, which is iteratively refined by participants until a mutually accepted version is reached, thus minimizing joint effort. in such dialogues, there is often a significant amount of coreference information regarding locations. additionally, @xcite focused on the relationship between language and cognition and explored the role of space in cognitive diversity. moreover, @xcite proposed a source-path-goal schema, identifying four structural elements: a) source, b) destination, c) path, and d) direction. @xcite constructed 'scene corpus' in which spatial expressions were collected by showing virtual objects. @xcite performed three experiments based on the fictional environments described by @xcite . they evaluated the mental costs associated with switching between route and survey perspective stimuli. their experiments utilized eight patterns of maps rotated at 45°increments. in contrast, this study collects route expressions by showing a real-world map instead of focusing on positions. ## data statistics in cases where the starting point and destination were mistaken (w), they tended to be less clear than the correct ones. a t-test was conducted, assuming unequal variances between the two samples. the mean clarity rating for @xmath0.79, whereas that for @xmath1.07. the difference between the means was significant (@xmath2.11, @xmath3.001, two-tailed). explanations for the starting point (x) were frequently omitted, with 37.5% (599/1600) of the expressions lacking such an explanation. those without a starting-point explanation exhibited slightly lower clarity than those with an explanation. a t-test assuming unequal variances between the two samples showed a significant difference (@xmath4.54, @xmath5.001, two-tailed), with a mean clarity rating of 2.72 for @xmath6.82 for @xmath7. the absence of route explanations (@xmath8.5% (24/1600) of the expressions, with an average clarity rating of 2.00, which was notably lower than the average clarity rating of 2.79 for expressions with route explanations (@xmath9. a t-test with unequal variances revealed a significant difference between the two groups (@xmath10.94, @xmath11.001, two-tailed). similarly, the absence of explanations for the destination (z) was observed in 3.2% (51/1600) of the responses. these expressions had a lower clarity average of 2.43 compared to those with a destination explanation (clarity average of 2.79). a t-test assuming unequal variances between the two samples revealed a significant difference (@xmath12.78, @xmath13.001, two-tailed), with a mean clarity rating of 2.43 for @xmath14.79 for @xmath15. hence, it is evident that all three aspects -starting point, route, and destination -play crucial roles in effectively conveying route information. addi-tionally, it i s noteworthy that explanations for the starting point (x) are often omitted, with 37.5% (599/1600) of the expressions lacking a starting point explanation. this observation underscores the challenge of referencing a starting point, which contributes to the complexity of comprehensively conveying route information. appendix figure 4 shows one of clearest examples. this example was the clearest explanation (avg. 3.74) of the route when showing the map in 13-a. these expressions included all information regarding starting point(x), route(y), and destination(z). instead of describing complex alleys, the route chosen explains the main streets, selecting landmarks along the route such as hareza tower (ハレザタワー), the elevated highway, and nitori (ニトリ) as reference points. ## conclusions this study aimed to collect clear route information reference expressions using both specific and relative location information for language-based direction instructions to autonomous driving systems. through crowdsourcing, expressions de-scribing route information on maps were gathered. each expression was manually annotated to determine whether it contained information regarding the starting point, route, and destination. the data revealed a tendency for the starting point information to be missing from expressions. additionally, clarity ratings were collected through crowdsourcing, indicating the importance of including information regarding routes and destinations. existing studies, particularly map task corpora, have focused on analyzing interactions involving the transmission of new and old information based on map tasks; they lack attention on clear route information explanations using maps. this study, however, uses maps of real locations instead of virtual environments. generating clearer route information reference expressions from map information requires collecting diverse expressions along with their clarity ratings, and examining what contributes to clarity. this study differs from previous research because it collected route information reference expressions. in future research, we plan to annotate reference expression information. relative reference expressions are inherently based on information from three or more points and are known to be abstracted as according to the double-cross model @xcite . however, utterances that contain information from three or more points cannot be obtained @xcite . data collected in this study include information from three or more points. therefore, the data should be annotated based on the double-cross model. the data gathered in this study, including the map information, classifications, and clarity ratings, are accessible to the public at https://github.com/masayu-a/hri-jp-rire-db .