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.

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DepDist: Surface realization via regex and learned dependency-distance tolerance (D19-63)

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Challenge: The paper describes a method of inflecting and linearizing a lemmatized dependency tree by: (1) determining a regular expression and substitution to describe each productive wordform rule; (2) learning the dependency distance tolerance for each head-dependent pair; (3) topologically sorting the DAG into a surface realization based on edge weight.
Approach: They propose a method of inflecting and linearizing a lemmatized dependency tree by learning the dependency distance tolerance for each head-dependent pair and topologically sorting the DAG into a surface order based on edge weight.
Outcome: The proposed method generates a morphologically inflected surface order for 11 languages across 18 treebanks.
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.
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.
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.
Realizing Universal Dependencies Structures (D19-63)

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Challenge: UD-SURFR is a symbolic surface realizer for Universal Dependencies structures . it is based on a tree of constituents that is then converted into an English sentence .
Approach: They propose a symbolic system that transforms a dependency tree into a tree of constituents that is transformed into an English sentence by an existing realizer.
Outcome: The proposed system is quite fast and showed competitive results for English sentences using automatic and manual evaluation measures.
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 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 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.
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.
Simpler but More Accurate Semantic Dependency Parsing (P18-2)

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Challenge: Syntactic dependency parsing is the most popular method for automatically extracting low-level relationships between words in a sentence.
Approach: They extend a syntactic dependency parser to train on and generate graph-structured representations that capture between-word relationships that are more closely related to the meaning of a sentence.
Outcome: The proposed system beats the current state-of-the-art system by 0.6% and linguistically richer representations push the margin even higher.

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