Generation of Relative Referring Expressions based on Perceptual Grouping

5940

Authors

nan

Abstract

Past work of generating referring expressions mainly utilized attributes of objects and bi- nary relations between objects. However, such an approach does not work well when there is no distinctive attribute among objects. To overcome this limitation, this paper proposes a method utilizing the perceptual groups of ob- jects and n-ary relations among them. The key is to identify groups of objects that are natu- rally recognized by humans. We conducted psy- chological experiments with 42 subjects to col- lect referring expressions in such situations, and built a generation algorithm based on the re- sults. The evaluation using another 23 subjects showed that the proposed method could effec- tively generate proper referring expressions. 1

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nan

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Generation of Relative Referring Expressions based on Perceptual Grouping Kotaro FUNAKOSHI Department of Computer Science Tokyo Institute of Technology Meguro ˆOokayama 2-12-1, Tokyo 152-8552, Japan koh@cl.cs.titech.ac.jp Satoru WATANABE Department of Computer Science Tokyo Institute of Technology Meguro ˆOokayama 2-12-1, Tokyo 152-8552, Japan satoru w@cl.cs.titech.ac.jp Naoko KURIYAMA Department of Human System Science Tokyo Institute of Technology Meguro ˆOokayama 2-12-1, Tokyo 152-8552, Japan kuriyama@hum.titech.ac.jp Takenobu TOKUNAGA Department of Computer Science Tokyo Institute of Technology Meguro ˆOokayama 2-12-1, Tokyo 152-8552, Japan take@cl.cs.titech.ac.jp Abstract Past work of generating referring expressions mainly utilized attributes of objects and bi- nary relations between objects. However, such an approach does not work well when there is no distinctive attribute among objects. To overcome this limitation, this paper proposes a method utilizing the perceptual groups of ob- jects and n-ary relations among them. The key is to identify groups of objects that are natu- rally recognized by humans. We conducted psy- chological experiments with 42 subjects to col- lect referring expressions in such situations, and built a generation algorithm based on the re- sults. The evaluation using another 23 subjects showed that the proposed method could effec- tively generate proper referring expressions. 1 Introduction In the last two decades, many researchers have stud- ied the generation of referring expressions to enable computers to communicate with humans about con- crete objects in the world. For that purpose, most past work (Appelt, 1985; Dale and Haddock, 1991; Dale, 1992; Dale and Reiter, 1995; Heeman and Hirst, 1995; Horacek, 1997; Krahmer and Theune, 2002; van Deemter, 2002; Krahmer et al., 2003) makes use of attributes of an intended object (the target) and binary rela- tions between the target and others (distractors) to distinguish the target from distractors. Therefore, these methods cannot generate proper referring ex- pressions in situations where no significant surface difference exists between the target and distractors, and no binary relation is useful to distinguish the target. Here, a proper referring expression means a concise and natural linguistic expression enabling hearers to distinguish the target from distractors. For example, consider indicating object b to per- son P in the situation shown in Figure 1. Note that person P does not share the label information such as a and b with the speaker. Because object b is not distinguishable from objects a or c by means of their appearance, one would try to use a binary re- lation between object b and the table, i.e., “A ball to the right of the table”. 1 However, “to the right of” is not a discriminatory relation, for objects a and c are also located to the right of the table. Us- ing a and c as a reference object instead of the ta- ble does not make sense, since a and c cannot be uniquely identified because of the same reason that b cannot be identified. Such situations have never drawn much attention, but can occur easily and fre- quently in some domains such as object arrange- ment (Tanaka et al., 2004). van …

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Labeled Text

Past work of generating referring expressions mainly utilized attributes of objects and binary relations between objects. However, such an approach does not work well when there is no distinctive attribute among objects. To overcome this limitation, this paper proposes a method utilizing the perceptual groups of objects and n-ary relations among them. The key is to identify groups of objects that are naturally recognized by humans. We conducted psychological experiments with 42 subjects to collect referring expressions in such situations, and built a generation algorithm based on the results. The evaluation using another 23 subjects showed that the proposed method could effectively generate proper referring expressions. 1

Publication Details

Paper ID:
5940
Year:
2004
Publication:
TIPSTER
Publication Type:
conf
DOI:
nan