Summary of the paper
| Title | VERTa: Linguistic features in MT evaluation |
|---|---|
| Authors | Elisabet Comelles, Jordi Atserias, Victoria Arranz and Irene Castellón |
| Abstract | In the last decades, a wide range of automatic metrics that use linguistic knowledge has been developed. Some of them are based on lexical information, such as METEOR; others rely on the use of syntax, either using constituent or dependency analysis; and others use semantic information, such as Named Entities and semantic roles. All these metrics work at a specific linguistic level, but some researchers have tried to combine linguistic information, either by combining several metrics following a machine-learning approach or focusing on the combination of a wide variety of metrics in a simple and straightforward way. However, little research has been conducted on how to combine linguistic features from a linguistic point of view. In this paper we present VERTa, a metric which aims at using and combining a wide variety of linguistic features at lexical, morphological, syntactic and semantic level. We provide a description of the metric and report some preliminary experiments which will help us to discuss the use and combination of certain linguistic features in order to improve the metric performance |
| Topics | Machine Translation, SpeechToSpeech Translation, Evaluation methodologies |
Full paper  |
VERTa: Linguistic features in MT evaluation |
| Bibtex | @InProceedings{COMELLES12.763, author = {Elisabet Comelles and Jordi Atserias and Victoria Arranz and Irene Castellón}, title = {VERTa: Linguistic features in MT evaluation}, booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, month = {may}, date = {23-25}, address = {Istanbul, Turkey}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Uğur Doğan and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-7-7}, language = {english} } |