Attribute-Centric Referring Expression Generation
نویسندگان
چکیده
The premise of the work presented in this chapter is that much of the existing work on the generation of referring expressions has focused on aspects of the problem that appear to be somewhat artificial when we look more closely at human-produced referring expressions. In particular, we believe that an overemphasis on the extent to which each property in a description performs a discriminatory function has blinded us to alternative approaches to referring expression generation that might be better-placed to provide an explanation of the variety we find in human-produced referring expressions. On the basis of an analysis of a collection of such data, we propose an alternative view of the process of referring expression generation which we believe is more intuitively plausible, is a better match for the observed data, and opens the door to more sophisticated algorithms that are freed of the constraints adopted in the literature so far.
منابع مشابه
Referring Expression Generation through Attribute-Based Heuristics
In this paper, we explore a corpus of human-produced referring expressions to see to what extent we can learn the referential behaviour the corpus represents. Despite a wide variation in the way subjects refer across a set of ten stimuli, we demonstrate that component elements of the referring expression generation process appear to generalise across participants to a significant degree. This l...
متن کاملReferring Expression Generation Using Speaker-based Attribute Selection and Trainable Realization (ATTR)
In the first REG competition, researchers proposed several general-purpose algorithms for attribute selection for referring expression generation. However, most of this work did not take into account: a) stylistic differences between speakers; or b) trainable surface realization approaches that combine semantic and word order information. In this paper we describe and evaluate several end-to-en...
متن کاملCross-linguistic Attribute Selection for REG: Comparing Dutch and English
In this paper we describe a cross-linguistic experiment in attribute selection for referring expression generation. We used a graph-based attribute selection algorithm that was trained and cross-evaluated on English and Dutch data. The results indicate that attribute selection can be done in a largely language independent way.
متن کاملSerial Dependency: Is It a Characteristic of Human Referring Expression Generation?
A key characteristic of many existing referring expression generation (REG) algorithms is serial dependency: attributes are selected for inclusion one at a time, and the decision to include each attribute is dependent on the discriminatory ability of the set of attributes that have already been selected so far. We use a machine learning approach to explore whether serial dependency is a charact...
متن کاملTrainable Speaker-Based Referring Expression Generation
Previous work in referring expression generation has explored general purpose techniques for attribute selection and surface realization. However, most of this work did not take into account: a) stylistic differences between speakers; or b) trainable surface realization approaches that combine semantic and word order information. In this paper we describe and evaluate several end-to-end referri...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2010