scholarly journals Scots Wiki – moving forward

2021 ◽  
Author(s):  
Sara Thomas
Keyword(s):  
Community Development ◽  
Natural Language ◽  
Special Reference ◽  
Volunteer Management ◽  
Linguistic Information ◽  
Language Generation ◽  
Partnership Development ◽  
Real People ◽  
Work Done ◽  
Programme Coordinator

How do you recover after a crisis?  This session will reflect on the work done by and with the sco.wiki community to recover and rebuild after the negative international press attention that surrounded the wiki in 2020. I’ll talk about on- and off- wiki community development, partnership development, the challenges that still face the project, and hopes for the future. I’ll also reflect on care in volunteer management, and why we should always remember that there are real people behind keyboards.  As Scotland Programme Coordinator for Wikimedia UK, I’ve been involved in supporting the community post-crisis, and have been impressed and heartened by the volume of work which has taken place since sco.wiki hit the headlines. I’d like to take this opportunity to tell the story of a group of editors and Scots speakers who are determined that the wiki should survive, grow, and thrive.  Abstract id. 11: In the lead-up to the launch of Abstract Wikipedia, a sufficient body of linguistic information, based on which the text within for a given language can be generated, must be in place so that different sets of functions, some working with concepts and others turning these into word sequences, can work together to produce something natural in that language. To achieve that information body's development requires more thorough consideration of a number of linguistic aspects sooner rather than later.  This session will thus discuss aspects of language planning with respect to Wikidata lexicographical data and natural language generation, including the compositionality and manipulability of lexical units, the breadth and interconnectedness of units of meaning, and the treatment of variation among a language’s lects broadly construed. Special reference to the handling of each of these aspects for Bengali and those linguistic varieties often grouped with it will be presented. 

scholarly journals Preparing languages for natural language generation using Wikidata lexicographical data

2021 ◽  
Author(s):  
Mahir Morshed
Keyword(s):  
Natural Language ◽  
Special Reference ◽  
Language Planning ◽  
Natural Language Generation ◽  
Linguistic Information ◽  
Language Generation

In the lead-up to the launch of Abstract Wikipedia, a sufficient body of linguistic information, based on which the text within for a given language can be generated, must be in place so that different sets of functions, some working with concepts and others turning these into word sequences, can work together to produce something natural in that language. To achieve that information body's development requires more thorough consideration of a number of linguistic aspects sooner rather than later. This session will thus discuss aspects of language planning with respect to Wikidata lexicographical data and natural language generation, including the compositionality and manipulability of lexical units, the breadth and interconnectedness of units of meaning, and the treatment of variation among a language’s lects broadly construed. Special reference to the handling of each of these aspects for Bengali and those linguistic varieties often grouped with it will be presented.

scholarly journals Integrating Text Planning and Linguistic Choice Without Abandoning Modularity: The IGEN Generator

2000 ◽  
Vol 26 (2) ◽  
pp. 107-138
Author(s):  
Robert Rubinoff
Keyword(s):  
Natural Language ◽  
Natural Language Generation ◽  
Linguistic Knowledge ◽  
Language Generation ◽  
Linguistic Resources ◽  
Text Planning ◽  
Abstract Description ◽  
Final Output ◽  
Work Done

Natural language generation is usually divided into separate text planning and linguistic components. This division, though, assumes that the two components can operate independently, which is not always true. The IGEN generator eliminates the need for this assumption; it handles interactions between the components without sacrificing the advantages of modularity. IGEN accomplishes this by means of annotations that its linguistic component places on the structures it builds; these annotations provide an abstract description of the effects of particular linguistic choices, allowing the planner to evaluate these choices without needing any linguistic knowledge. This approach allows IGEN to vary the work done by each component independently, even in cases where the final output depends on interactions between them. In addition, since IGEN explicitly models the effects of linguistic choices, it can gracefully handle situations where the available time or linguistic resources are limited.

The Story Of Computational Narratology

2018 ◽  
Author(s):  
Sharath Srivatsa ◽  
Shyam Kumar V N ◽  
Srinath Srinivasa
Keyword(s):  
Natural Language ◽  
Computational Modeling ◽  
Narrative Structure ◽  
Complex Problem ◽  
General Intelligence ◽  
Language Understanding ◽  
Language Generation ◽  
Narrative Generation ◽  
Growing Body ◽  
Computational Narratology

In recent times, computational modeling of narratives has gained enormous interest in fields like Natural Language Understanding (NLU), Natural Language Generation (NLG), and Artificial General Intelligence (AGI). There is a growing body of literature addressing understanding of narrative structure and generation of narratives. Narrative generation is known to be a far more complex problem than narrative understanding [20].

scholarly journals The Rare Word Issue in Natural Language Generation: A Character-Based Solution

Informatics ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 20
Author(s):  
Giovanni Bonetta ◽  
Marco Roberti ◽  
Rossella Cancelliere ◽  
Patrick Gallinari
Keyword(s):  
Natural Language ◽  
Error Probability ◽  
Essential Feature ◽  
Proper Names ◽  
Neural Model ◽  
Natural Language Generation ◽  
Training Phase ◽  
Tabular Data ◽  
Rare Word ◽  
Language Generation

In this paper, we analyze the problem of generating fluent English utterances from tabular data, focusing on the development of a sequence-to-sequence neural model which shows two major features: the ability to read and generate character-wise, and the ability to switch between generating and copying characters from the input: an essential feature when inputs contain rare words like proper names, telephone numbers, or foreign words. Working with characters instead of words is a challenge that can bring problems such as increasing the difficulty of the training phase and a bigger error probability during inference. Nevertheless, our work shows that these issues can be solved and efforts are repaid by the creation of a fully end-to-end system, whose inputs and outputs are not constrained to be part of a predefined vocabulary, like in word-based models. Furthermore, our copying technique is integrated with an innovative shift mechanism, which enhances the ability to produce outputs directly from inputs. We assess performance on the E2E dataset, the benchmark used for the E2E NLG challenge, and on a modified version of it, created to highlight the rare word copying capabilities of our model. The results demonstrate clear improvements over the baseline and promising performance compared to recent techniques in the literature.

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