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. |
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Surface Realization Shared Task 2019 (MSR19): The Team 6 Approach (D19-63)
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| 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. |
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 . |
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. |
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 . |
Surface Realisation Using Full Delexicalisation (D19-1)
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| Challenge: | Existing approaches to surface realisation model word ordering, morphological inflection and contraction generation are evaluated on 10 languages covered by the SR'18 shared task. |
| Approach: | They propose a modular approach which models each of these components separately and an analysis of the differences in word ordering performance across languages. |
| Outcome: | The proposed model is compared with existing models on 10 languages covered by the SR'18 shared task. |
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. |
Learning to Order Graph Elements with Application to Multilingual Surface Realization (D19-63)
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| Challenge: | Recent advances in deep learning have shown promises in solving combinatorial optimization problems, such as sorting variable-sized sequences. |
| Approach: | They propose an encoder-decoder framework that learns the representation for each element and predicts the ordering of each local neighborhood of the graph in turn. |
| Outcome: | The proposed framework outperforms previous frameworks on multilingual surface realization tasks while outperforming those below by a large margin. |
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. |
An Encoder-Decoder Approach to the Paradigm Cell Filling Problem (D18-1)
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| Challenge: | a Paradigm cell filling problem is a problem that asks how speakers of a language can reliably produce inflectional forms without ever witnessing them before. |
| Approach: | They implement novel neural models for the Paradigm Cell Filling Problem in morphology . they evaluate models on 18 data sets in 8 languages and implement them in a new dataset . |
| Outcome: | The proposed model performs comparable to previous work with less training data. |
Improving Low-Resource Morphological Inflection via Self-Supervised Objectives (2025.acl-long)
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| Challenge: | Rapid progress in natural language processing (NLP) has largely been driven by training transformer models on massive amounts of unlabeled data, but such large datasets are scarce for many of the world's languages. |
| Approach: | They propose to train encoder-decoder transformers for 19 languages and 13 auxiliary objectives on massive amounts of unlabeled data. |
| Outcome: | The proposed tasks outperform standard CMLM in character-level tasks when available data is limited. |