| Author(s): Show less - |
Lan, Alex Gwo Jen
;
Paraboni, Ivandre
;
Declerck, T
;
Calzolari, N
;
Choukri, K
;
Cieri, C
;
Hasida, K
;
Isahara, H
;
Maegaard, B
;
Mariani, J
;
Moreno, A
;
Odijk, J
;
Piperidis, S
;
Tokunaga, T
;
Goggi, S
;
Mazo, H
Total Authors: 16
|
| Document type: | Journal article |
| Source: | PROCEEDINGS OF THE ELEVENTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION (LREC 2018); v. N/A, p. 6-pg., 2018-01-01. |
| Abstract | |
In Natural Language Generation (NLG), Referring Expression Generation (REG) lexical choice is the subtask that provides words to express a given input meaning representation. Since lexical choices made in real language use tend to vary greatly across speakers, computational models of lexicalisation have long addressed the issue of human variation in the REG field as well. However, studies of this kind will often rely on large collections of pre-recorded linguistic examples produced by every single speaker of interest, and on every domain under consideration, to obtain meaning-to-text mappings from which the lexicalisation model is built. As a result, speaker-dependent lexicalisation may be impractical when suitable annotated corpora are not available. As an alternative to corpus-based approaches of this kind, this paper argues that differences across human speakers may be at least partially influenced by personality, and presents a personality-dependent lexical choice model for REG that is, to the best of our knowledge, the first of its kind. Preliminary results show that our personality-dependent approach outperforms a standard lexicalisation model (i.e., based on meaning-to-text mappings alone), and that the use of personality information may be a viable alternative to strategies that rely on corpus knowledge. (AU) | |
| FAPESP's process: | 16/14223-0 - Computational Treatment of Human Personality for Natural Language Processing Applications |
| Grantee: | Ivandre Paraboni |
| Support Opportunities: | Regular Research Grants |