O Poeta Artificial 2.0: Increasing Meaningfulness in a Poetry Generation Twitter bot
Hugo Gonc¸alo Oliveira CISUC, Department of Informatics Engineering University of Coimbra, Portugal [email protected]
Abstract a poetry generation platform, and originally used the latter for producing poetry from a set of fre- O Poeta Artificial is a bot that tweets poems, in Por- quent keywords in tweets that mentioned the target tuguese, inspired by the latest Twitter trends. Built on trend. O Poeta Artificial 2.0, hereafter shortened top of PoeTryMe, a poetry generation platform, so far to Poeta 2.0, resulted from recent developments on it had only produced poems based on a set of keywords in tweets about a trend. This paper presents recently im- the original version, aimed at increasing the inter- plemented features for increasing the connection between pretability of its results through a higher connection the produced text and the target trend through the reuti- with the trend. The new features enable the reutilisa- lisation and production of new text fragments, for high- tion of fragments of human-produced tweets, possi- lighting the trend, paraphrasing text by Twitter users, or bly with a word replaced by its synonym, as well as based on extracted or inferred semantic relations. the inclusion of fragments that highlight the trend, or fragments obtained from relations extracted from 1 Introduction tweets about the trend, or even inferred from the lat- ter. Produced poems may include some new frag- Poetry generation is a popular task at the intersection ments and others produced by the original proce- of Computational Creativity and Natural Language dure (hereafter, the classic way), based on the ex- Generation. It aims at producing text that exhibits tracted keywords, while keeping a regular metre and poetic features at formal and content level, while, to favouring the presence of rhymes. some extent, syntactic rules should still be followed The remainder of this paper starts with a brief and a meaningful message should be transmitted, of- overview on poetry generation systems and creative ten through figurative language. Instead of generat- Twitter bots, followed by a short introduction to ing a poem that uses a set of user-given keywords PoeTryMe and how it is used by the Twitter bot. Af- or around an abstract concept, several poetry gener- ter this, the new features of Poeta 2.0 are described, ators produce poetry inspired by a given prose docu- with a strong focus on new kinds of fragments pro- ment. Besides the potential application to entertain- duced by this system. Before concluding, the results ment, this provides a specific and tighter context for of Poeta 2.0 are illustrated with some poems pro- assessing the poem’s interpretability. duced, using different kinds of fragments. This paper presents new features of O Poeta Arti- ficial (Portuguese for “The Artificial Poet”), a com- 2 Related Work putational system that produces poems written in Portuguese, inspired by the latest trends on the so- Automatic poetry generators are a specific kind of cial network Twitter and, similarly to other creative Natural Language Generation (NLG) systems where systems, posts them in the same network, through the produced text exhibits poetic features, such the @poetartificial account. O Poeta Artificial is as a pre-defined metre and rhymes, together with built on top of PoeTryMe (Gonc¸alo Oliveira, 2012), some level of abstraction and figurative language. Many poetry generators have been developed and tem (Veale, 2013), more focused on content and not described in the literature (see Gonc¸alo Oliveira so much on form. (2017b)), especially in the domain of Computational Despite the growing number of intelligent bots, Creativity. They are typically knowledge-intensive Twitter has many other bots, some of which intelligent systems that deal with several levels of performing tasks that are typically in the do- language, from lexical choice to semantics. main of creativity, but through not so intelli- Several systems produce poetry based on given gent and knowledge-poor processes. Those in- stimuli, which can be a set of semantic predi- clude @MetaphorMinute, which generates random cates (Manurung, 2003), one (Charnley et al., 2014) metaphors, or @pentametron, which retweets pair- or more seed words (Gonc¸alo Oliveira, 2012), a ings of random rhyming tweets, both with ten met- prose description of a message (Gervas,´ 2001), or rical syllables. a longer piece of text, such as blog posts (Misztal Besides bots, other creative systems produce con- and Indurkhya, 2014) or newspaper articles (Colton tent inspired by information circulating on Twitter, et al., 2012; Rashel and Manurung, 2014; Toiva- including poetry. FloWr (Charnley et al., 2014) is a nen et al., 2014; Tobing and Manurung, 2015; platform for implementing creative systems, which Gonc¸alo Oliveira and Alves, 2016). Longer docu- has been used for producing poetry by selecting ments used as inspiration can be reflected in the po- phrases from human-produced tweets, based on sen- ems through the use of keywords (Rashel and Ma- timent and theme, and organising them according nurung, 2014), associations (Toivanen et al., 2014), to a target metre and rhyme. TwitSonnet (Lamb et phrases (Charnley et al., 2014), similes (Colton al., 2017) builds poems with tweets retrieved with a et al., 2012), dependency (Tobing and Manurung, given keyword in a time interval, scored according 2015) or semantic relations (Gonc¸alo Oliveira and to the poetic criteria of: reaction (presence of words Alves, 2016) extracted from them, and may also that transmit a desired emotion), meaning (pres- transmit the same sentiment (Colton et al., 2012) or ence of given keywords and frequent tri-grams), and emotions (Misztal and Indurkhya, 2014). Poems are craft (metre and rhyme, plus words with strong im- typically built from templates, either handcrafted or agery). Several poems by TwitSonnet were posted extracted from human-produced poems, then filled on Tumblr, another micro-blogging social network. with information from the inspiration document. Instead of templates, the previous systems reuse Twitter has become a popular platform for bots, complete text fragments extracted from Twitter. mostly because of its nature – many users posting short messages (tweets), available on real time – 3 PoeTryMe and O Poeta Artificial and its friendly API, which exposes much informa- tion, easily used by computational systems. This is O Poeta Artificial (Gonc¸alo Oliveira, 2016) is also true for creative bots. Some use Twitter merely a Twitter bot that tweets poems written in Por- as a showcase for exhibiting their results, possibly tuguese and inspired by recent trends in the Por- enabling some kind of user interaction, liking or tuguese Twitter community. It is built on top of retweeting. Those include, for instance, bots for PoeTryMe (Gonc¸alo Oliveira, 2012), a poetry gen- producing riddles (Guerrero et al., 2015) or Inter- eration platform with a modular architecture, so far net Memes (Gonc¸alo Oliveira et al., 2016). Other adapted to produce poetry in different languages and bots also exploit information on Twitter for pro- from different stimuli. ducing their contents. This happens, for instance, PoeTryMe’s architecture, explained in detail else- for @poetartificial (Gonc¸alo Oliveira, 2016), which where (Gonc¸alo Oliveira et al., 2017), is based on produces poetry, in Portuguese, roughly inspired two core modules – a Generation Strategy and the by current trends, and is the focus of the follow- Lines Generator – and some complementary ones. ing sections. It is also the case of @Metaphor- To some extent, a parallelism can be made between Magnet (Veale et al., 2015) and its “brother” bots, this architecture and the traditional ‘strategy’ and who produce novel metaphors, through the same ‘tactical’ components of a NLG system (Thompson, generating mechanisms as a poetry generation sys- 1977). The Generation Strategy implements a plan for producing poems according to user-given param- the seeds, following a generate-and-test strategy at eters. It may have different implementations and the line level. The poem with the highest score for interact with the Syllable Utils for metre scansion metre and presence of rhymes is tweeted. and rhyme identification. The Lines Generator in- In the original version of O Poeta Artificial, the re- teracts with a semantic network and a context-free sult was always a block of four lines, generally with grammar for producing semantically-coherent frag- 10 syllables each, and with occasional rhymes. Due ments of text, to be used as lines of a poem. Each to their generation process, lines were syntactically- of those lines will generally use two words that, in correct and semantically coherent, but the connec- the semantic network, are connected by some rela- tion with the trend was often too shallow. For in- tion R. Those words fill a line template, provided by stance, as the trend is typically a hashtag or a named the grammar, which is generalised to suit all pairs of entity, it is not in the semantic network and thus words related by R. For instance, the line template never used in the poem. The following section de- you’re the X of my Y can be used for render- scribes recent developments towards a higher con- ing partOf relations, such as: nection with the trend, thus contributing to an im- • estuary partOf river proved meaningfulness. → you’re the estuary of my river • periscope partOf submarine 4 Poeta Artificial 2.0: beyond seed words → you’re the periscope of my submarine • fiber partOf personality Poeta 2.0 aims at improving the meaningfulness of → you’re the fiber of my personality the original bot by increasing the connection of the In most instantiations of PoeTryMe, a set of seed produced poems with the target trend and with what words is provided as input for setting the poem do- people are saying about it. main. This constrains the semantic network to re- A minor improvement occurs in the seed selection lations that involve one of the seeds, with a proba- process. Instead of relying exclusively on the fre- bility of selecting also relations with indirectly re- quency of each content word in the tweets, Poeta 2.0 lated words (known as the ‘surprise factor’). There divides it by its frequency in a large Portuguese cor- is also a module for expanding the set of seeds with pus (Santos and Bick, 2000). This aims to use more structurally-relevant words, possibly constrained by relevant words, and can be seen as an application of a target polarity (positive or negative). Though orig- the tf.idf weighting scheme. inally developed for Portuguese, poetry may also be Yet, the main feature of Poeta 2.0 is that, besides generated in Spanish or English, depending on the seed words, it also provides a set of pre-generated underlying linguistic resources, namely the seman- text fragments to PoeTryMe, somehow connected to tic network, the lexicons and the grammars (Gonc¸alo the target trend and that may be used as poem lines. Oliveira et al., 2017). For every line of the poem to fill, there is a probabil- O Poeta Artificial adds an initial layer for select- ity of using one of the generated fragments instead ing the seed words to use. Before generation, it: of a line produced in the classic way, based on the se- (i) Selects one of the top trends in the Portuguese mantic network and generation grammar. This prob- Twitter (the highest not used in the last three po- ability is proportional to the number of fragments of ems); (ii) Retrieves recent tweets (currently, up to this kind available for the target number of syllables. 200), written in Portuguese and mentioning the tar- One of the previous fragments is also used if it has get trend; (iii) Processes each tweet and extracts exactly the target number of syllables and rhymes every content word used; (iv) Selects top frequent with one of the previously used lines. content words (currently, 4) to be used as seeds; Another new feature is that, based on the pro- (v) May expand the seeds, either according to the duced fragments, Poeta 2.0 sets the target length of main sentiment of the tweets (based on the pres- the poem lines, though having in mind the maximum ence of emoticons) or, if there is a Wikipedia article of 140 characters a tweet can contain. More pre- about the trend, with content words from its abstract. cisely, it counts the number of syllables of each text PoeTryMe is then used for producing 25 poems from fragment produced and selects a number, between 5 and 10, for which there are more fragments avail- Original fragment: able. Poems by Poeta 2.0 are still blocks of four Salvador com duvidas´ em aceitar (Salvador with doubts whether to accept) lines, but each line will have the selected number of Synonyms: syllables or close. duvidas´ = {indecisoes˜ , hesitac¸oes˜ , incertezas} The remainder of this section describes the differ- (doubts = {indecisions, hesitations, uncertainties})) ent types of text fragments that Poeta 2.0 produces, aceitar = {aprovar, acatar, adoptar} (accept = {aprove, obey, adopt}) namely fragments that highlight the trend, fragments Paraphrases: of the processed tweets, paraphrases of the former, Salvador com indecisoes˜ em aceitar and fragments based on semantic relations. All are Salvador com hesitac¸oes˜ em aceitar put together in a set of usable fragments. PoeTryMe Salvador com incertezas em aceitar will have no idea of how they were produced. Salvador com duvidas´ em aprovar Salvador com duvidas´ em acatar Salvador com duvidas´ em adoptar 4.1 Fragments Highlighting the Trend Table 2: Tweet and some generated paraphrases. The first kind of fragments is based on a small set of templates with a placeholder for the target trend, provided to PoeTryMe. The main difference be- each highlighting the latter by referring to it as a re- tween Poeta 2.0 and other poetry generators that use cent topic that many people are talking about. Some human-written tweets is that Poeta 2.0 mixes them of those templates are shown in table 1, where T is with the other kinds of fragments it produces. the trend placeholder. 4.3 Paraphrases of Tweets andam a escrever/falar sobre T Besides human-written tweets, Poeta 2.0 produces (they are writing/talking about T ) hoje fala-se de T variations of them. More precisely, it retrieves syn- (today, people are talking about T ) onyms of the content words in the previous frag- sobre T vim escrever ments from PoeTryMe’s semantic network, and pro- (about T I came to write) duces new fragments by replacing each content word interesse por T e´ global? with one of its synonyms. Poeta 2.0 may thus find (interest for T is global) T e´ tendenciaˆ social alternative ways of expressing the same messages (T is a social trend) humans did, possibly also covering a wider range of T e´ um assunto recente metres. This has similarities with Tobing and Ma- (T is a recent topic) nurung (2015), though Poeta 2.0 does not perform fala de T muita gente! word sense disambiguation because PoeTryMe’s se- (many people chatting about T ) T , porque falam de ti? mantic network is not organised in word senses. Al- (T , why do they chat about you?) though some issues may result from ambiguity, we T , T , e T prefer to think that, though not completely inten- (T , T , and T ) tional, using synonyms that only apply for other Table 1: Examples of trend-highlighting templates. senses may create interesting domain shifts. Table 2 illustrates this procedure for a specific fragment. 4.2 Fragments of Tweets In order to avoid poems where all lines paraphrase Similarly to other systems (Charnley et al., 2014; each other, a maximum of 5 paraphrases are gener- Lamb et al., 2017), Poeta 2.0 may reuse text from ated for each content word in a fragment. If a word human-produced tweets. Recall that, in order to has more than 5 synonyms, 5 are randomly selected. select the most relevant words for the target trend, 200 tweets written in Portuguese and mentioning 4.4 Semantic Relation-based Fragments this trend are used as an inspiration set. Among In order to keep the philosophy behind PoeTryMe, the processing steps, those tweets are broken into the natural way of increasing interpretability would smaller units, when possible, following simple rules, be to extract semantic relations from the tweets men- such as line breaks or punctuation signs. Each of tioning the trend and adding them to the set of rela- the obtained units is added to the set of fragments tions to use. To some extent, we kept this philoso- phy, but we also wanted Poeta 2.0 to be independent coded knowledge and provides higher control on the from the core of PoeTryMe. This enables the extrac- results than for machine learning approaches. tion of relations of different types, more focused on Currently, four different relation types are ex- Twitter text, on the trends, and possibly not so well- tracted from the inspiration tweets. This is per- defined, which can be managed without changing formed with the help of a small set of patterns, re- PoeTryMe. The same happens for a new set of line vealed in table 3 and with possible results illustrated templates based on the extracted relations, smaller in table 4. In both tables, T stands for the trend, and but more controlled than the line templates covered a rough translation of the patterns, from Portuguese by PoeTryMe’s grammars, most of which acquired to English, is provided. automatically from collections of poetry. The extracted relations – isA, hasProperty, has, Another important reason for this decision is that, can – are tied to the extraction patterns but are not in Portuguese, determiners, adjectives and other as semantically well-defined as relations in a word- words are declined according to gender and num- net or ontology. Yet, as long as we are aware of this ber. In PoeTryMe, this is handled by a morphology in the following steps, it is not a critical issue. lexicon and different grammar productions are still required, depending on the gender and number of the 4.4.2 Relation Inference related words. Yet, while the same lexicon could be Based on the extracted relations, implicit in the used for acquiring the gender and number of nouns text, other relations are inferred, when combined or adjectives extracted from Twitter, it would not with relations in PoeTryMe’s semantic network. For cover all the trends, which are typically named en- Portuguese, the network currently used includes tities, or hashtags. Therefore, the templates for the all the relations in at least two out of nine Por- Twitter relations are, as much as possible, gender tuguese lexical-semantic knowledge bases, includ- and number independent, and only consider these at- ing wordnets and dictionaries (Gonc¸alo Oliveira, tributes when they can be obtained from PoeTryMe. 2017a). Therefore, it covers a rich set of rela- In order to produce text fragments based on se- tion types including not only synonymy, hyper- mantic relations involving the trend, Poeta 2.0 re- nymy and partOf, but also others, such as isSaid- lies primarily on a small set of line templates com- OfWhatDoes (in Portuguese, dizSeDoQue), isSaid- patible with each of the covered semantic relations. About (dizSeSobre), hasQuality (temQualidade), has- Yet, it goes further and combines the extracted re- State (temEstado), antonymyOf (antonimoDe), is- lations with the relations in PoeTryMe’s semantic Part/Member/MaterialOf (parte/membro/materialDe), network for inferring new relations and increasing, and isPartOfWhatIs (parteDeAlgoComPropriedade), once again, the set of available fragments. The fol- which are exploited by Poeta 2.0 lowing sections describe the three steps involved in A set of rules was handcrafted for inferring new the production of relation-based fragments: extrac- relations from a combination of one relation ex- tion, inference, and text generation. tracted from the tweets and another in PoeTryMe’s semantic network. Although more inference rules 4.4.1 Relation Extraction may be defined in the future, possibly exploiting ad- Since Hearst (1992) proposed a set of lexical- ditional relations, the current rules are in figure 1. syntactic patterns for the automatic acquisition of Again, the inferred relations are not as well-defined hyponym-hypernym pairs from text, much work has as those in a wordnet. Some are of the same types as targeted the automatic extraction of semantic rela- the relations originally extracted, but new types are tions from text, sometimes with much more sophis- introduced (e.g. isLike, isNot, withQuality, with- ticated approaches. Yet, when recall is not critical, State, mayCause), some of which may result in one of the arguments is fixed (the trend), and we are metaphors or less obvious connections, and are thus focused on a closed set of relation types, relying on useful for poetry generation. Table 5 illustrates some a small set of lexical-syntactical patterns is proba- of the previous rules with examples of relations ex- bly the fastest way for achieving this goal. More- tracted, known (i.e. in PoeTryMe’s semantic net- over, it avoids the need for large quantities of en- work), and inferred. Pattern Relation ...
Text Relation Bruno Mars e´ o rei do pop. Bruno Mars isA rei (Bruno Mars is the king of pop.) (Bruno Mars isA king) O Centeno e´ mesmo brilhante... Centeno hasProperty brilhante (Centeno is really brilliant...) (Centeno hasProperty brilliant) Wagner Moura foi tao˜ sincero quanto Lula. Lula hasProperty sincero (Wagner Moura was as sincere as Lula.) (Lula hasProperty sincere) O Antonio´ Costa esta´ feliz da vida! Antonio´ Costa hasProperty feliz (Antonio´ Costa is happy of his life!) (Antonio´ Costa hasProperty happy) Lorde tem talento demais. Lorde has talento (Lorde has too much talent.) (Lorde has talent) Manuel Serrao˜ a pensar exatamente o mesmo que eu. Manuel Serrao˜ can pensar (Manuel Serrao˜ is thinking exactly the same as I.) (Manuel Serrao˜ can think) Cantar como a Adele e´ dific´ılimo! Adele can cantar (To sing like Adele is so hard!) (Adele can sing) Table 4: Examples of extracted relations. Extracted Known Inferred T isA rei (king) real (royal) isSaidAbout rei T hasProperty real T hasProperty brilhante brilhante isSaidAbout luminosidade (light) T isLike luminosidade (brilliant) brilhante hasQuality brilhantismo (brilliance) T isLike brilhantismo T hasProperty sincero sincero hasQuality sinceridade (sincerity) T withQuality sinceridade (sincere) sincero antonymOf hipocrita´ (hipocrit) T isNot hipocrita´ T has talento capaz (capable) saidAbout talento T is capaz (talent) talento isPartOfWhatIs talentoso (talented) T is talentoso T can pensar pensante (thinker) saidOfWhatDoes pensar T is pensante (think) pensar causes pensamento (thought) T mayCause pensamento Table 5: Examples of inferred relations.
4.4.3 Semantic Relations as Text 5 Examples
Both extracted and inferred relations are used for This section presents some poems produced by producing text fragments by filling, with the rela- Poeta 2.0, their rough English translations, and a tion arguments, a small set of handcrafted templates, short discussion on the fragments used. Despite compatible with each relation type. Table 6 illus- the new features introduced, sometimes, poems still trates this with examples of fragments produced for have all of their lines generated in the classic way. a set of relations. Some fragments use both relation This happens especially when no tweets are reused, arguments, while others only use the second argu- possibly due to their long size, and when no relations ment, and not the trend, to avoid much repetition. are extracted. The following poem is of this kind: Relation Example fragments Bruno Mars isA rei ser rei como Bruno Mars (being a king like Bruno Mars) por ser rei (for being a king) Centeno hasProperty brilhante quero ser brilhante como Centeno (I want to be brilliant like Centeno) dizem que e´ brilhante (people say he’s brilliant) Lorde has talento Lorde tem talento (Lorde has talent) tem mesmo talento! (she really has talent!) Adele can cantar a cantar como Adele (singing like Adele) dizem que sabe cantar! (people say she knows how to sing!) Centeno isLike luminosidade Centeno lembra a luminosidade (Centeno resembles brightness) com uma luminosidade tal (with such a brightness) Lula withQuality sinceridade a sinceridade do Lula (Lula’s sincerity) a demonstrar sinceridade (showing sincerity) Lula isNot hipocrita´ Lula nao˜ sera´ hipocrita´ (Lula is probably not a hipocrit) nada hipocrita´ (not a hipocrit) Manuel Serrao˜ is pensante Manuel Serrao˜ parece pensante (Manuel Serrao˜ seems to be a thinker) tambem´ quero ser pensante (I also want to be a thinker) Manuel Serrao˜ mayCause pensamento como o pensamento de Manuel Serrao˜ ? (Iike Manuel Serrao’s˜ thought?) a gerar pensamento (generating a thought) Table 6: Relations and examples of produced fragments.
• If (T isA X) ∧ eral cases with great public impact. All lines rhyme X isSaidOfWhatDoes Y → T can Y and all have 10 syllables, except the last, which Y saidAbout X → T is Y has only 9. The seeds collected from the tweets X hasQuality Y → T withQuality Y were delac¸ao˜ (denunciation), advogada (lawyer), X hasState Y → T withState Y telefonica´ (of telephone), cita (citation). The first X antonymOf Y → T isNot Y line was produced from the semantic relation ‘de- Y isPart/Member/MaterialOf X → T has Y latar causes delac¸ao˜ ’, the second and third from • If (T hasProperty X) ∧ ‘acusac¸ao˜ synonymOf delac¸ao˜ ’, and the fourth from X isSaidOfWhatDoes Y → T can Y X isSaidAbout Y → T isLike Y ‘citac¸ao˜ synonymOf cita’. X hasQuality Y → T withQuality Y The following example was produced on the X hasState Y → T withState Y morning of 4th of June 2017, after the attacks at X antonymOf Y → T isNot Y London Bridge, when there was a trending hashtag Y hasQuality X → T isLike Y #LondonBridge: Y hasState X → T isLike Y fala de #LondonBridge muita gente! • If (T has X) ∧ Many people talking about O universo e´ mesmo doente #LondonBridge! Y isSaidAbout X → T is Y The universe is really sick Pol´ıcia procura suspeitosos Police searching for suspects Y hasQuality X → T is Y Pol´ıcia procura duvidosos Police searching for dubious Y hasState X → T is Y X isPartOfWhatIs Y → T is Y All the lines have 10 syllables, except the first, • If (T can X) ∧ because the syllable division tool considered the # Y isSaidOfWhatDoes X → T is Y as a syllable. Every line ends in rhyme: the first pair X causes Y → T mayCause Y of lines ends in -ente and the second in -osos. The first line highlights the trend and the remaining are Figure 1: Rules for relation inference paraphrases of the following fragments from human- written tweets: delatar sempre causa delac¸ao˜ To denounce always causes denunciation O mundo e´ mesmo doente (The world is really sick) delac¸ao˜ negra sem acusac¸ao˜ Black denunciation without accusation Accusation in half denunciation Pol´ıcia procura suspeitos (Police looking for suspects) acusac¸ao˜ em meia delac¸ao˜ Without quotation or citation sem achar cita, nem citac¸ao˜ The next example was produced for the trend It was generated for the trend Carlos Alexandre, Rui Santos, a Portuguese football commentator, two the name of a Portuguese judge in charge of sev- days after Benfica won the Portuguese Football Cup (30th May 2017). It uses three lines based on sible, used in the poems. This paper described the relations extracted from the processed tweets: production of those fragments.
Rui Santos consegue falar Rui Santos can speak The first impression of the poems now generated tambem´ posso ser miliar? can I be very small as well? is positive, which is also shown by the examples in- tambem´ quero ser miliar I also want to be very small cluded in this paper. Some poems are still produced greasy at hand, parwise seboso a par, par a par in the classic way, where the only connection be- All of its lines have 8 syllables and all have the tween lines and trend is the presence of associated same termination. The first line was produced from words in semantically-coherent sentences. Yet, sev- the relation ‘Rui Santos can falar’ (talk), extracted eral have now lines that highlight the trend, lines that from more than one tweet, including the following: are built from relations involving the trend, or lines No lugar do Rui Vitoria´ deixava o seboso do Rui Santos a falar that reuse text by other users about the trend, thus sozinho com o seu trofeu.´ making them more meaningful. Each kind of frag- (If I were Rui Vitoria,´ I would leave greasy Rui Santos talking alone ments may be further augmented, for instance, by with his trophy) exploiting additional patterns and semantic relations Another relation is ‘Rui Santos has dimensao˜ ’ (di- in the tweets, but the manual labour involved is a mension), extracted from: practical issue, as it may become quite complex to Rui Santos tem dimensao˜ para o Sporting. E´ pequenino. manage all the patterns and inference rules. (Rui Santos has dimension for Sporting. He is little.) Another limitation is that the semantic relation- The second and third lines of the poem were pro- based fragments have to be gender and number in- duced from the relation ‘Rui Santos is miliar’ (very dependent. This may be minimised in the future, small), inferred from the previous, due to the rela- if the determiners frequently used before the trends tion ‘dimensao˜ isPartOfWhatIs miliar’. are considered for identifying the previous proper- The final example was produced for the trend ties. Yet, as there are other kinds of fragments, other Ronaldo, one day after the football player Cris- relations, and poems only have four lines, this is cur- tiano Ronaldo won the fourth European Champions rently not critical. League of his career (5th June 2017). It mixes dif- Most limitations of PoeTryMe (Gonc¸alo Oliveira ferent kinds of fragments: et al., 2017) are also present. For instance, despite
Ronaldo e´ muito falado Ronaldo is widely spoken targeting the same semantic domain, lines are gener- arte e danc¸a amor calado art and dance silent love ated independently of each other, not always result- num estado de felicidade in a state of happiness Ronaldo shows simplicity ing in the most logical sequence. This could be min- Ronaldo mostra simplicidade imised if a reordering procedure was applied, similar All lines have 9 syllables, with two rhymes: the to the one by Lamb et al. (2017), where abstraction first pair ends in -ado and the second in -ade. and imagery are considered. The first line highlights the trend. Due to a The extraction of long-term information on the video of Ronaldo dancing, one of the seeds ex- trend may also be improved. Currently, if the trend tracted was danc¸a (dance), which originated the has a Wikipedia article, associations are extracted second line, based on the relation ‘arte hyperny- from its abstract. In the future, relations may be ex- mOf danc¸a’. The remaining lines result from two tracted directly from DBPedia. relations: ‘Ronaldo withQuality simplicidade’ (in- A final issue, not yet discussed, is that the sys- ferred from ‘Ronaldo hasProperty feliz’ and ‘fe- tem may reuse fragments that contain typos, thus de- liz hasState felicidade’), and ‘Ronaldo withQuality creasing the quality of the poems. Of course, every simples’ (inferred from ‘Ronaldo hasProperty sim- word could be spellchecked and words with typos ples’ and ‘simples hasQuality simplicidade’). could be corrected, possibly to a different word than it should be, or their fragments could be discarded, 6 Concluding Remarks possibly with many false positives. In order to increase the connection between poems As it happened for the original bot, every by a Twitter bot and a recent trend, more meaning- two hours, Poeta 2.0 tweets through the account ful text fragments are now produced and, when pos- @poetartificial, which has about 260 followers. References Hugo Gonc¸alo Oliveira. 2017b. A survey on intelligent poetry generation: Languages, features, techniques, John Charnley, Simon Colton, and Maria Teresa Llano. reutilisation and evaluation. In Proceedings of 10th In- 2014. The FloWr framework: Automated flowchart ternational Conference on Natural Language Genera- construction, optimisation and alteration for creative tion, INLG 2017, page (in press), Santiago de Com- systems. In 5th International Conference on Compu- postela, Spain. ACL Press. tational Creativity, ICCC 2014, Ljubljana, Slovenia. 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