Datasets:

WINGNUS
/

ACL-OCL

	{
	"paper_id": "2021",
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	"title": "AfriKI: Machine-in-the-Loop Afrikaans Poetry Generation",
	"authors": [
	{
	"first": "Imke",
	"middle": [],
	"last": "Van Heerden",
	"suffix": "",
	"affiliation": {
	"laboratory": "",
	"institution": "Ko\u00e7 University",
	"location": {
	"settlement": "Istanbul",
	"country": "Turkey"
	}
	},
	"email": "ivanheerden@ku.edu.tr"
	},
	{
	"first": "Anil",
	"middle": [],
	"last": "Bas",
	"suffix": "",
	"affiliation": {
	"laboratory": "",
	"institution": "Marmara University",
	"location": {
	"settlement": "Istanbul",
	"country": "Turkey"
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	},
	"email": "anil.bas@marmara.edu.tr"
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	"year": "",
	"venue": null,
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	"abstract": "This paper proposes a generative language model called AfriKI. Our approach is based on an LSTM architecture trained on a small corpus of contemporary fiction. With the aim of promoting human creativity, we use the model as an authoring tool to explore machine-inthe-loop Afrikaans poetry generation. To our knowledge, this is the first study to attempt creative text generation in Afrikaans.",
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	"abstract": [
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	"text": "This paper proposes a generative language model called AfriKI. Our approach is based on an LSTM architecture trained on a small corpus of contemporary fiction. With the aim of promoting human creativity, we use the model as an authoring tool to explore machine-inthe-loop Afrikaans poetry generation. To our knowledge, this is the first study to attempt creative text generation in Afrikaans.",
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	"text": "Afrikaans 1 is a language spoken largely in South Africa, Namibia, Botswana and Zimbabwe. Masakhane (\u2200 et al., 2020a,b) draws important attention to the current disproportion of NLP research and resources with respect to African languages. In fact, in the entire ACL Anthology, 2 of the thirteen studies that mention \"Afrikaans\" in their titles, only four (Sanby et al., 2016; Augustinus et al., 2016; Dirix et al., 2017; Ralethe, 2020) appeared in the last five years. By no means do we ignore studies with inclusive (Eiselen and Puttkammer, 2014) and multilingual approaches (Ziering and Van der Plas, 2016) or those published via other platforms (Van Zaanen and Van Huyssteen, 2003) . This is simply an indication that NLP research in Afrikaans is limited, especially in comparison to resource-rich languages, i.e. the so-called \"winners\" in the taxonomy of Joshi et al. (2020) .",
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	"text": "(\u2200 et al., 2020a,b)",
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	"text": "(Sanby et al., 2016;",
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	"text": "Augustinus et al., 2016;",
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	"text": "Dirix et al., 2017;",
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	"text": "Ralethe, 2020)",
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	"text": "(Eiselen and Puttkammer, 2014)",
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	"text": "(Ziering and Van der Plas, 2016)",
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	"text": "(Van Zaanen and Van Huyssteen, 2003)",
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	"text": "Joshi et al. (2020)",
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	"section": "Introduction",
	"sec_num": "1"
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	"text": "In this paper, we present a generative language model called AfriKI, an abbreviation for \"Afrikaanse Kunsmatige Intelligensie\" (Afrikaans 1 The Constitution of the Republic of South Africa recognises Afrikaans as one of eleven official languages, alongside Sepedi, Sesotho, Setswana, siSwati, Tshivenda, Xitsonga, English, isiNdebele, isiXhosa and isiZulu (Assembly, 1996) . In South Africa, there are approximately 6.9 million firstlanguage speakers of Afrikaans, according to the most recent census (Lehohla, 2012 ).",
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	"text": "1",
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	"text": "Sepedi, Sesotho, Setswana, siSwati, Tshivenda, Xitsonga, English, isiNdebele, isiXhosa and isiZulu (Assembly, 1996)",
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	"text": "(Lehohla, 2012",
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	"section": "Introduction",
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	"text": "2 https://www.aclweb.org/anthology/ Artificial Intelligence). We use this model as an authoring tool to explore machine-in-the-loop poetry generation in Afrikaans. Machine-in-theloop frameworks promote human creativity through computational assistance, as opposed to human-inthe-loop pipelines, which aim to strengthen machine learning models (Clark et al., 2018) . We treat poetry generation as a hybrid system, an experimental approach that enables the generation of high-quality poetic text with very limited data. To our knowledge, this is the first study in creative text generation as well as an initial step towards automatic poetry generation in Afrikaans. Whereas NLG in its quest for full automation may frown upon human involvement, our humancentred framework does the opposite. According to Lubart (2005) , one criticism of artificial intelligence programs that claim to be creative is exactly that a human plays a role at some point, which reduces the autonomy of the machine. From the HCI perspective [...] these \"failed\" AI creativity programs are examples of successful human-computer interactions to facilitate creativity.",
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	"section": "Introduction",
	"sec_num": "1"
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	"text": "This study demonstrates that human-machine collaboration could enhance human creativity. We agree with Shneiderman (2002) that support tools \"make more people more creative more often\".",
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	"section": "Introduction",
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	"text": "Several computational models focus on automatic poetry generation. First approaches follow rulebased, template-based systems (Gerv\u00e1s, 2001; D\u00edaz-Agudo et al., 2002) . Levy (2001) and Manurung et al. (2012) apply genetic algorithms while Jiang and Zhou (2008) and He et al. (2012) use statistical machine translation, with Yan et al. (2013) utilising text summarisation to generate poetry. Oliveira (2009) provides a clear overview of early systems and presents a comparable method (2012).",
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	"text": "Manurung et al. (2012)",
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	"text": "Jiang and Zhou (2008)",
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	"text": "He et al. (2012)",
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	"text": "Yan et al. (2013)",
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	"section": "Related Work",
	"sec_num": "2"
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	"text": "Starting with Zhang and Lapata (2014) , we have seen great advancements in poetry generation using neural networks. Wang et al. (2016a) extend this using the attention mechanism (Bahdanau et al., 2015) . There are many attempts to improve the quality of learning-based generated poetry, by using planning models (Wang et al., 2016b) , finitestate machinery (Ghazvininejad et al., 2016) , reinforcement learning (Yi et al., 2018) as well as variational autoencoders (Yang et al., 2018) .",
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	"text": "are not suitable for learning long range dependencies (Wang et al., 2016a) due to the vanishing gradient problem (Bengio et al., 1994) . Long short-term memory (LSTM) networks (Hochreiter and Schmidhuber, 1997) address this issue and are widely used for language modeling (Sundermeyer et al., 2012) . Tikhonov and Yamshchikov (2018) propose word-based LSTM to generate poetry. Potash et al. 2015 The remarkable quality and results of these studies are indisputable. However, they all concentrate on data-rich languages such as English, Chinese, Italian and Russian. For example, the character language model of Hopkins and Kiela (2017) uses a poetry corpus consisting of 7.56 million words and 34.34 million characters. Likewise, a recent study by Liu et al. (2020) trained on over 200 thousand poems and 3 million ancient Chinese prose texts.",
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	"section": "Conventional recurrent neural networks (RNN)",
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	"text": "We trained an LSTM network for poetic text generation as well. However, our approach differs in significant ways. First, whereas these studies generate verse in a fully automatic manner, we emphasise human creativity, introducing a strong computational component to the creative writing process. Second, the aforementioned studies either trained on comprehensive poetry datasets or model poetic qualities. To illustrate the latter, the recent work of Van de Cruys (2020) focuses on specifically nonpoetic text in English and French, however, is able to model the rhyme constraint using phonetic representation of words from Wiktionary. Since there is no publicly available large-scale poetry dataset in Afrikaans, we follow an alternative approach, constructing our model as a text generator that pro- duces individual sentences and phrases instead of stanzas of verse. In other words, the model outputs a set of lines, which we arrange vertically into short poems without modification.",
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	"section": "Conventional recurrent neural networks (RNN)",
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	"text": "In this section, we explain the dataset, model architecture as well as the co-creative poetry generation process.",
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	"text": "Corpus: AfriKI trained on a lengthy (208,616word) literary novel titled Die Biblioteek aan die Einde van die W\u00eareld (The Library at the End of the World) (Van Heerden, 2019) by the South African novelist Etienne van Heerden. In 2020, the book was awarded the University of Johannesburg Prize for Literature (Pienaar, 2020) . This work of new journalism combines fictional techniques with documentary language, and is particularly suitable given its use of rich imagery, figurative language as well as different Afrikaans varieties like Kaaps (or Cape Afrikaans) and Standard Afrikaans. Figure 1 shows a word cloud of its most commonly used words.",
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	"text": "Model Architecture: Experimenting with several architectures, including LSTM, Multi-Layer LSTM and Bi-LSTM, we obtain best results with the following two-layer LSTM architecture. We use a vanilla LSTM structure (Hochreiter and Schmidhuber, 1997) and, to avoid repetitiveness, omit to describe the network diagram and equations, similar to Sundermeyer et al. (2012) . We start with 100-dimensional word embeddings with a vocabulary size of 23,317 words, where weights are randomly initialised from a normal distribution with zero mean and standard deviation 0.01. Next, we stack two LSTM layers with 50 units in each layer followed by dropout layers with the rate of 0.2. This is followed by a fully connected layer and a softmax layer. We use the Adam optimiser (Kingma and Ba, 2015) with a learning rate = 0.001, batch size = 16, and train for 300 epochs. Although tweaking the parameters did change the model performance, it was not significant.",
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	"text": "Machine-in-the-Loop: Human-machine collaboration for the enhancement of creative writing has been examined under automated assistance (Roemmele and Gordon, 2015, 2018), co-authorship (Tucker, 2019) , co-creativity (Manjavacas et al., 2017; Kantosalo and Riihiaho, 2019; Calderwood et al., 2020) , interactive storytelling (Swanson and Gordon, 2012; Brahman et al., 2020) and machinein-the-loop (Clark et al., 2018; Akoury et al., 2020) . Applying Clark et al. (2018) 's terminology, we employ an iterative interaction structure that follows a push method of initiation with low intrusiveness. To clarify, our process consists of a single loop with two stages. First, the model generates a sizable set of unique individual lines (hundreds). Although memory networks may repeat parts of the training data (Ghazvininejad et al., 2016) , the generated phrases are highly distinct from the dataset, with hardly any repetition of word order. Second, the first author responds by choosing phrases at will. To create the final artefact, the author arranges the selected lines vertically. Generated text is used strictly without modification (except for some capitalisation and punctuation). The result of our collaborative writing system is short, compelling works of poetry that draw inspiration from the literary movements Imagism (Hughes, 1972) and Surrealism (Balakian, 1986) . Table 1 presents three examples of poems produced by means of the co-creative process. Here, we discuss quality from a literary perspective.",
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	"text": "Trained on prose, the text is generated as free verse (i.e. free from the restrictions of rhythm and rhyme) which we associate with contemporary poetry. In the lines, various poetic devices can be identified, such as alliteration (e.g. \"golwe van gister\") and assonance (e.g. \"maak 'n vraag waarbinne\").",
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	"text": "The generated lines abound with figurative language as well. As an instance of an extended metaphor, the first stanza of the second poem suggests sensitivity to the country's turbulent history. Personification is particularly prevalent, lending a visceral quality to the text: Africa drinks, the landscape tilts its back, the sea breathes, and Table Mountain poses a question. The imagery is vivid, portraying sight (Tonight's curtains / glitter at sliding windows / in the city lights), smell (the introspective sea / lifts the scents and utters / a breath) and sound (roar of a wave). The language can be described as minimalist, evocative and abstract, and therefore open to interpretation, resembling Imagist and Surrealist poetry.",
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	"text": "Afrikaans has a rich poetic tradition (Brink and Opperman, 2000), and we believe that creative text generation has the potential to enrich poetic lan-guage. Alongside Afrikaans varieties, the corpus contains some English as well, which influenced the generated text in interesting ways. As one example, it is grammatically incorrect in Standard Afrikaans to use \"sun\" as both noun and verb, e.g. \"to sun in the garden\". The model, however, adopted this and other patterns from the English, generating novel phrases (that do not sound anglicised) such as \"sonlig son die promenade\" -sunlight suns the promenade.",
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	"section": "Results",
	"sec_num": "4"
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	"text": "In this study, we present Afrikaans poetry generation in a machine-in-the-loop setting. Each and every line of poetry is automatically generated by the proposed LSTM network. In order to clearly identify the machine's contribution to the process, the human writer's interaction is limited to the selection and vertical arrangement of the lines -without any modification. We believe this is the first creative text generation study in the Afrikaans language. More broadly, the work encourages humancentred design in low-resource languages. Creative industries would benefit from co-creative tools and methods (Hsu et al., 2019) , perhaps more than fully automatic approaches.",
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	"section": "Conclusion",
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	"text": "There are many ways in which this work can be extended.",
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	"section": "Future Work",
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	"text": "First, similar to Yi et al. (2017), we could follow line-to-line poem generation, where the network takes the previous line as prompt and generates a new line which, in turn, is the prompt for the next entry. We could also experiment with different architectures, such as Transformer (Vaswani et al., 2017) , as well as training schemes. For example, we could borrow AfriBERT (Ralethe, 2020), the recent BERT (Devlin et al., 2019) adaptation for Afrikaans, to apply transfer learning.",
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	"text": "Second, as demonstrated in Van de Cruys (2020), poetry generation is also possible by training on prosaic (non-poetic) text and modeling poetic constraints (e.g. rhyme). This way, we could expand to fully automatic poetry generation. Naturally, this would require an extensive literature corpus.",
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	"section": "Future Work",
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	"text": "Third, regarding the unconventional use of some nouns as verbs in Afrikaans, future research could explore how prevalent this type of novel, crosslanguage variation is. To improve textual quality, we could incorporate Afrikaans datasets such as the NCHLT Annotated Text Corpora (Eiselen and Puttkammer, 2014; as well as the Afrikaans treebank (Augustinus et al., 2016) , which are available via SADiLaR (Roux, 2016) in addition to others.",
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	"text": "Finally, a promising direction to pursue would be the involvement of poets and writers to investigate whether this approach could inform and improve their creative writing practices.",
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	"back_matter": [
	{
	"text": "This paper has been produced benefiting from the 2232 International Fellowship for Outstanding Researchers Program of T\u00dcB\u0130TAK (Project No: 118C285). However, the entire responsibility of the paper belongs to the owner of the paper. The financial support received from T\u00dcB\u0130TAK does not mean that the content of the publication is approved in a scientific sense by T\u00dcB\u0130TAK.We would like to thank Etienne van Heerden for providing his manuscript to be used in this study.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Acknowledgments",
	"sec_num": null
	}
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