| Challenge: | This paper describes the approach developed by the Tilburg University team for the shallow track of the Multilingual Surface Realization Shared Task 2019 (SR'19). |
| Approach: | They propose a method for the shallow track of the Multilingual Surface Realization Shared Task 2019 using a rule-based and a statistical machine translation (SMT) model. |
| Outcome: | The proposed approach can generate texts in 11 languages, compared with the submission of the same approach for the same task in 2018 which only covered 6 languages. |
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LORIA / Lorraine University at Multilingual Surface Realisation 2019 (D19-63)
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| Challenge: | WO component is easily transferrable between languages, but needs attention for each language separately. |
| Approach: | They present a LORIA / Lorraine University submission to the shared task . they evaluate it on 11 languages covered by the shared challenge . |
| Outcome: | The proposed approach is evaluated on 11 languages covered by the shared task . the main takeaways are that the WO component is easily transferrable between languages . |
The Second Multilingual Surface Realisation Shared Task (SR’19): Overview and Evaluation Results (D19-63)
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| Challenge: | EMNLP’19 Workshop on Multilingual Surface Realisation aims to stimulate the exploration of advanced neural networks for multilingual sentence generation from Universal Dependency (UD) structures. |
| Approach: | They present results from the SR'19 Shared Task, a multilingual surface realisation task organised as part of the EMNLP'19 Workshop on Multilingual Surface Realisation. |
| Outcome: | The SR'19 shared task was organised as part of the EMNLP'19 Workshop on Multilingual Surface Realisation . it consisted of two tracks with different levels of complexity . the shallow track was offered in eleven, and the deep track in three languages . |
Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019) (D19-63)
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| Challenge: | SR'19 participants have put in a lot of effort to make the event successful . |
| Approach: | SR'19 participants acknowledge hard work, creativity and enthusiasm . SR tasks and workshops were well attended and well attended . |
| Outcome: | SR'19 participants acknowledge hard work, reviewers and local organisers . participants' creativity and enthusiasm is what keeps them going . |
The DipInfoUniTo Realizer at SRST’19: Learning to Rank and Deep Morphology Prediction for Multilingual Surface Realization (D19-63)
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| Challenge: | SR is one of the main tasks involved in Natural Language Generation. |
| Approach: | They propose a system which divides the SR task into two independent subtasks, namely word order prediction and morphology inflection prediction. |
| Outcome: | The proposed system is a direct successor to the architecture presented at SR'19. |
IMSurReal: IMS at the Surface Realization Shared Task 2019 (D19-63)
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| Challenge: | a system for shallow and deep completion is presented for the Surface Realization Shared Task 2019 . the system achieves state-of-the-art performance without using external data. |
| Approach: | They propose a surface realization system that takes five steps without external data . they perform detailed error analysis revealing correlation between word order freedom and difficulty . |
| Outcome: | The proposed system achieves state-of-the-art without external data . it achieves highest BLEU scores on tokenized text and human evaluation on four languages . |
Shape of Synth to Come: Why We Should Use Synthetic Data for English Surface Realization (2020.acl-main)
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| Challenge: | In the Surface Realization Shared Tasks of 2018 and 2019, there was little difference in absolute performance between systems trained with and without synthetic data. |
| Approach: | They propose to use synthetic data to explore approaches to surface realization from Universal-Dependency-like trees to surface strings for several languages. |
| Outcome: | The proposed method improves performance of a previously state-of-the-art system by 8 BLEU points over the previous system on the English dataset. |
The Concordia NLG Surface Realizer at SRST 2019 (D19-63)
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| Challenge: | The goal of Natural Language Generation (NLG) is to produce natural texts given structured data. |
| Approach: | They propose a model for the shallow track of the 2019 NLG Surface Realization Shared Task . they divided the problem into two sub-problems: reordering and inflecting . |
| Outcome: | The proposed model reconstructs sentences whose word order and word inflections were removed. |
Improving Language Generation from Feature-Rich Tree-Structured Data with Relational Graph Convolutional Encoders (D19-63)
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| Challenge: | The goal of the multilingual surface realization shared task is to generate fluent text from UD structures. |
| Approach: | They propose to use a graph convolutional network to encode the dependency trees given as input. |
| Outcome: | The proposed system achieves the third rank without data augmentation techniques or additional components. |
The OSU/Facebook Realizer for SRST 2019: Seq2Seq Inflection and Serialized Tree2Tree Linearization (D19-63)
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| Challenge: | Existing linearization methods for shallow surface realization tasks are not available for all languages. |
| Approach: | They propose a system that implements morphological inflection with a baseline linearizer for a shallow surface realization task. |
| Outcome: | The proposed system is competitive across languages, but poor on longer sentences. |
BME-UW at SRST-2019: Surface realization with Interpreted Regular Tree Grammars (D19-63)
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| Challenge: | adaamko's system restores word order and inflection from a graph of typed, directed dependencies between lemmas. |
| Approach: | They propose a method that restores word order and inflection from a graph of typed, directed dependencies between lemmas. |
| Outcome: | The proposed system restores word order and inflection from a graph of typed, directed dependencies between lemmas. |