Trainable Speaker-Based Referring Expression Generation
نویسندگان
چکیده
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 referring expression generation algorithms that take into consideration speaker style and use data-driven surface realization techniques.
منابع مشابه
Referring 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 کاملTrainable Referring Expression Generation using Overspecification Preferences
Referring expression generation (REG)models that use speaker-dependent information require a considerable amount of training data produced by every individual speaker, or may otherwise perform poorly. In this work we present a simple REG experiment that allows the use of larger training data sets by grouping speakers according to their overspecification preferences. Intrinsic evaluation shows t...
متن کاملCombining Referring Expression Generation and Surface Realization: A Corpus-Based Investigation of Architectures
We suggest a generation task that integrates discourse-level referring expression generation and sentence-level surface realization. We present a data set of German articles annotated with deep syntax and referents, including some types of implicit referents. Our experiments compare several architectures varying the order of a set of trainable modules. The results suggest that a revision-based ...
متن کاملSpeaker-Dependent Variation in Content Selection for Referring Expression Generation
In this paper we describe machine learning experiments that aim to characterise the content selection process for distinguishing descriptions. Our experiments are based on two large corpora of humanproduced descriptions of objects in relatively small visual scenes; the referring expressions are annotated with their semantic content. The visual context of reference is widely considered to be a p...
متن کاملReferring Expression Generation: Taking Speakers' Preferences into Account
We describe a classification-based approach to referring expression generation (REG) making use of standard context-related features, and an extension that adds speaker-related features. Results show that taking speakers’ preferences into account outperforms the standard REG model in four test corpora of definite descriptions.
متن کامل