Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019)

Simon Mille, Anja Belz, Bernd Bohnet, Yvette Graham, Leo Wanner (Editors)


Anthology ID:
D19-63
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | WS
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
URL:
https://aclanthology.org/D19-63
DOI:
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PDF:
https://aclanthology.org/D19-63.pdf

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Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019)
Simon Mille | Anja Belz | Bernd Bohnet | Yvette Graham | Leo Wanner

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Surface Realization Shared Task 2019 (MSR19): The Team 6 ApproachMSR19): The Team 6 Approach
Thiago Castro Ferreira | Emiel Krahmer

This study describes the approach developed by the Tilburg University team to the shallow track of the Multilingual Surface Realization Shared Task 2019 (SR’19) (Mille et al., 2019). Based on Ferreira et al. (2017) and on our 2018 submission Ferreira et al. (2018), the approach generates texts by first preprocessing an input dependency tree into an ordered linearized string, which is then realized using a rule-based and a statistical machine translation (SMT) model. This year our submission is able to realize texts in the 11 languages proposed for the task, different from our last year submission, which covered only 6 Indo-European languages. The model is publicly available.

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The DipInfoUniTo Realizer at SRST’19 : Learning to Rank and Deep Morphology Prediction for Multilingual Surface RealizationDipInfoUniTo Realizer at SRST’19: Learning to Rank and Deep Morphology Prediction for Multilingual Surface Realization
Alessandro Mazzei | Valerio Basile

We describe the system presented at the SR’19 shared task by the DipInfoUnito team. Our approach is based on supervised machine learning. In particular, we divide the SR task into two independent subtasks, namely word order prediction and morphology inflection prediction. Two neural networks with different architectures run on the same input structure, each producing a partial output which is recombined in the final step in order to produce the predicted surface form. This work is a direct successor of the architecture presented at SR’19.

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LORIA / Lorraine University at Multilingual Surface Realisation 2019LORIA / Lorraine University at Multilingual Surface Realisation 2019
Anastasia Shimorina | Claire Gardent

This paper presents the LORIA / Lorraine University submission at the Multilingual Surface Realisation shared task 2019 for the shallow track. We outline our approach and evaluate it on 11 languages covered by the shared task. We provide a separate evaluation of each component of our pipeline, concluding on some difficulties and suggesting directions for future work.

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Back-Translation as Strategy to Tackle the Lack of Corpus in Natural Language Generation from Semantic Representations
Marco Antonio Sobrevilla Cabezudo | Simon Mille | Thiago Pardo

This paper presents an exploratory study that aims to evaluate the usefulness of back-translation in Natural Language Generation (NLG) from semantic representations for non-English languages. Specifically, Abstract Meaning Representation and Brazilian Portuguese (BP) are chosen as semantic representation and language, respectively. Two methods (focused on Statistical and Neural Machine Translation) are evaluated on two datasets (one automatically generated and another one human-generated) to compare the performance in a real context. Also, several cuts according to quality measures are performed to evaluate the importance (or not) of the data quality in NLG. Results show that there are still many improvements to be made but this is a promising approach.