Zero-shot Sonnet Generation with Discourse-level Planning and Aesthetics Features
Abstract
Poetry generation, and creative language generation in general, usually suffers from the lack of large training data.
In this paper, we present a novel framework to generate sonnets that does not require training on poems.
We design a hierarchical framework which plans the poem sketch before decoding.
Specifically, a content planning module is trained on non-poetic texts to obtain discourse-level coherence; then a rhyme module generates rhyme words and a polishing module introduces imagery and similes for aesthetics purposes.
Finally, we design a constrained decoding algorithm to impose the meter-and-rhyme constraint of the generated sonnets.
Automatic and human evaluation show that our multi-stage approach without training on poem corpora generates more coherent, poetic, and creative sonnets than several strong baselines.
Introduction
A sonnet is a fourteen-line poem with rigorous meter-and-rhyme constraints.
In this paper, we aim at generating full-length sonnets that are logically and aesthetically coherent, without training on poetic texts.
There are several challenges for this ambitious goal.
First, there are limited number of sonnets available to train a fully supervised model.
The only resource is a mere 3,355 sonnets collected by Lau et al.
(2018) in Project Gutenberg (Hart, 2004), one of the largest free online libraries for English literature.
While it is possible to train on related corpus such as general poems or English lyrics (Ghazvininejad et al., 2016), such approaches are not applicable to many languages for which sizable poetry/lyrics data do not exist.
Moreover, even if large-scale creative texts exist, learning from and mimicking existing corpora is not creative by definition and is unlikely to result in novel content.
[Figure]
Second, coherence remains a known issue among previous works on poetry generation.
Existing works mainly focus on conforming to the format constraints (i.e., meter-and-rhyme), or generating a small stanza with a typical length of four (Lau et al., 2018; Liu et al., 2019;
Yi et al., 2020).
For full-length sonnets, Ghazvininejad et al.
(2016) propose to use topical words as rhyme words to achieve topical relatedness, but the generated son- nets are not discourse-level coherent.
They later generate discourse-level coherent English sonnets through French-English translation (Ghazvininejad et al., 2018).
Generating logically and aesthetically ordered poems without relying on content translation from other languages remains a challenge.
With all these in mind, we propose to generate sonnets without training on any poetic data .
Our framework, Zest , is shown in Figure 1, and consists of four components: content planning, rhyme pairing, polishing for aesthetics and final decoding.
The first three steps provide salient points for the sonnet as a sketch.
The last step is responsible of “translating” the sketch into well-formed sonnets.
Specifically, the content planning step is trained on a combination of news and stories to generate keywords per sentence, which aims at equipping the model with general world knowledge to construct a coherent text world .
However, the language used by poems is different from that of standard texts because it follows certain rhetorical rhythm and is full of vivid descriptions that appeals to readers’ senses and imagination (Gibbs Jr et al., 1994).
To this end, in the polishing step we leverage external knowledge and incorporate two figurative speeches (i.e., simile and imagery) into the planned keywords to boost vividness and imagination.
Step rhyme and final decoding steps are designed to impose the meter-and-rhyme constraints.
We would like to emphasize that while previous works on plan-and-write rely on training data from the target task domain (e.g., stories, dialogue conversations) for improved performance, we on the other hand adopt content planning to disentangle the training from the decoding step and circumvent the shortage of training data.
We summarize our contributions as follow:
• We propose Zest , a Ze ro-Shot s onne t generation framework, by disentangling training from decoding.
Specifically, we first learn to predict context and rhyme words from news and story dataset, and then polish the predicted keywords to promote creativity.
A constrained decoding algorithm is designed to impose the meter-and-rhyme constraints while incorporating the keywords.
• We develop two novel evaluation metrics to measure the quality of the generated poems: automatic format checking and novelty evaluation (i.e., diversity and imageability).
• Human evaluation shows that Zest generates more discourse-level coherent, poetic, creative, and emotion-evoking sonnets than its baselines.
Background
In this section, we introduce the characteristics of sonnets in terms of structure, meter and rhyme.
We then define important terminologies.
2. The Structures of Sonnets
We aim to generate the two most representative sonnets: Shakespearean and Petrarchan .
Sonnets make use of rhymes in a repeating pattern called rhyme schemes as shown in Table 1.
For example, when writing a Shakespearean sonnet, poets usually adopt the rhyme scheme of ABABCDCDEFEFGG.
Although all sonnets have 14 lines, a Petrarchan sonnet consists of an 8-line stanza called an octave followed by a 6-line stanza called a sestet.
On the other hand, a Shakespearean sonnet consists of three 4-line quatrains and a 2-line rhyming couplet which leaves the reader with a lasting impression.
[Table]
2. Meter Constraints
Most sonnet conform to iambic pentameter, a sequence of ten syllables alternating between unstressed ( x or da ) and stressed syllables ( / or DUM ).
Strictly speaking, each line reads with the rhythm ( da-DUM ) 5 , which enhances the tone for the poem and operates like an echo.
In reality, there are many rhythmic variations.
For example, the first foot is often reversed to sound more assertive, and can be written as ( DUM-da * ( da-DUM ) 4 ).
Another departure from the standard ten-syllable pattern is to append an addition unstressed syllable to the end, forming feminine rhymes which can be written as (( da-DUM ) 5 * da ).
2. Rhyme Words, Couplets and Patterns
A pair of rhyme words consists of two words that have the same or similar ending sound.
A rhyming couplet is a pair of rhymed lines.
For example,
Line 1&3, 2&4 in Figure 1 are two pairs of rhyming couplets.
From the CMU pronunciation dictionary (Weide, 1998), we know that “fall" and “thaw" in Figure 1 are strict rhyming pairs because they have exactly the same phonetic endings: " AO L" .
“Leaves" ( " IY V Z" ) and “trees" ( " IY Z" ) are slant rhymes, because they have the same stressed vowels, while the ending consonants are similar but not identical.
2. Terminology
We formally define the following terms:
• Sketch:
The sketch of a poem contains three aspects: 1) content words that cover the key concepts or main ideas, 2) the rhyme words to appear at the end of each line, and 3) the modification of keywords for aesthetics.
• Keywords K : content words and rhyme words combined.
They contain main ideas of a poem and define the rhyming pattern.
• Content words C : keywords that do not appear in the end of each line.
We target at predictingcontext words per line, C i 1 and C i 2 .
• Rhyme words R : words in the end of each line.
For example, in a Shakespearean sonnet with the rhyme scheme ABABCDCDEFEFGG, there are seven pairs of rhyme words: R
R
, R 2 R
and R 13 R
• Initial rhyming lines
I Init : index of the lines that the first rhyme word in a rhyming couplet appears (e.g., I Init =
for a
Shakesperean sonnet and I Init =
1, 2, 9, 10, 11] for a Petrarchan sonnet).
Approach
Overview
As is shown in Figure 1, our sonnet generation model can be divided into four steps.
At step a, we train a title-to-outline module by finetuning T5 (Raffel et al., 2019) on keywords extracted from news reports and stories.
During inference time, we generate a fourteen-line sonnet sketch that contain those content words C (Section 3.1).
At step b, we aim at forming the correct rhyming pairs.
We first select the initial rhyme words from C i for i ∈ I Init , and then generate the remaining rhyme words (i.e., for i ∈ I Init ) by forcing the decoder to sample from a vocabulary pool that contains strict and slant rhyme words (Section 3.2).
At step c, we infuse imagery and simile as two figurative devices to C (Section 3.3).
In the last step, we leverage a fine-tuned language model with constrained decoding algorithm to impose the meter-and-rhyme constraints (Section 3.4).
[Figure]
3. Content Planning
For each piece of news or stories, we train a title-tokeywords framework that predicts the outline.
To this end, we first extract three most salient words per line using the RAKE (Rose et al., 2010) algorithm, which is a domain-independent keyword extraction technique.
Controllable Text Formatting
We then leverage the task adaptability of the pretrained T5 (Raffel et al., 2019) to predict the keywords of the whole body.
As a unified framework that treats every text processing task as a “text-to-text” problem,
T5 can be easily adapted to our task as shown in
Figure 2.A, where the input is an instruction to generate the sketch given the title, and the outputs are multiple keywords for each line.
However, we need a mechanism to specify the number of lines and keywords to be generated, since we train on prosaic texts with varying formats but infer only on the 14-line sonnets.
To solve this problem and gain control over the poem structures, we format the input and output as shown in Figure 2.B.
Specifically, we use [MASK] tokens as placeholders for the keywords.
Now that one [MASK] token on the input side corresponds to exactly one word on the output side, we are able to specify the number of lines and keywords during the inference time.
3. Generating Rhyme Words
Our title-to-outline model is trained to generate keywords, regardless of the rhyme constraints.
In this section, we describe the procedure to generate rhyme pairs.
Specifically, we force the model to generate a 14-line outline, with two or three content words for each line depending on whether the line
“Title: The Four Seasons.
Keywords : [plums, autumn, leaves].
Keywords : [trees, quivering, fall].
Keywords : [tangled, branches, R 3 ].
Keywords : [fog, snow, R 4 ].
… Keywords : [blossoming, lemon, fell].
Keywords : [swirl, air, R 14 ].”
Keywords
[Equation]
where K ij represents the j -th keyword in the ith line.
Among the three keywords in the initial rhyming lines, we select the last word as the initial rhyme word.
Rhyme Pairs Generation
Given the initial rhyme words, we then retrieve all the possible rhyme words R based on their phonetics information from the CMU prounounciation dictionary (Weide, 1998).
This include strict rhymes and slant rhymes.
For instance, in Figure 3, the retrieved rhyme word candidates R for ‘leaves’ are [‘achieves’, ‘believes’, ‘Steves’, ‘trees’, ...].
The probability distribution for generating the rhyme word w R from the candidate list R is modified as: P [Equation] otherwise
[Equation]
where p ( w R |· ) is the original word probability yielded by the title-to-outline decoder.
Context Words for Aesthetics
Now, we have the generated context words and rhyme words that are discourse-level coherent yet less vivid.
To this end, we use external knowledge to incorporate two figurative devices into the planned keywords: imagery and simile.
Imagery
We leverage the <symbol, imagery> pairs (e.g., <love, rose>) in the ConceptNet dataset (Liu and Singh, 2004) and finetune a imagery generation model from a pretrained model called
COMmonsEnse Transformer (Bosselut et al., 2019) (COMeT).
It is trained on imagery pairs to generate the imagery word given the symbolism word as input.
At inference time, we randomly sample multiple nouns from the sketch to predict their imageries, and only make replacement for the two most confident generations.
For example in Figure 1, both <day, sun> and <love, rose> are generated, yet we only replace ‘love’ with ‘rose’, because the probability of generating the latter pair is much higher than the former pair.
Simile
A simile phrase consists of two parts: the adjective and the figurative vehicle.
For example, ‘sudden like a flash’ is a simile phrase where ‘a flash’ is the figurative vehicle of ‘sudden’.
We leverage the simile generation model by
Chakrabarty et al.
(2020) as an off-the-shelf tool to generate simile vehicles from adjectives and calculate the possibility.
2 At inference time, we randomly sample multiple adjectives from the sketch to predict their figurative vehicles, and only keep the most confident ones.
In addition, we also make sure the generated simile phrase conforms to the iambic-meter constraint.
For example in Figure 1, the phrase ‘bright like diamond’ (/x/x) follows the iambic meter, whereas another phrase such as ‘shining like diamond’ (/xx/x) will be disregarded.
3. Sketch to Sonnet Generation
In order to write fluent and poetic languages that meet the meter-and-rhyme constraints, we make the following adaptations.
First, generating the full sonnet requires more powerful pretrained model than generating the outlines.
Therefore, we finetune GPT-Neo-2.7B on the same combination of news and stories data as a language model to gen- erate the sonnet.
Second, to effectively incorporate the rhyme words at the end of each line, we follow previous methods (Ghazvininejad et al., 2016; Van de Cruys, 2020)
and generate the whole sonnet line-by-line in reverse , starting from the final rhyme word to the first word.
That is to say, our language model is finetuned to generate right to left.
Third, we include the sketch in the prompt, so that the decoder will learn to give higher probability for these keywords.
We then use lexically constrained decoding similar to that of Grid Beam
Search (Hokamp and Liu, 2017) to incorporate the keywords.
In addition, we also include the previously generated lines in the prompt to generate more coherent sonnets.
A simile phrase in the sketch is considered fixed that cannot be modified.
Namely, we force to generate the whole phrase when the first word in the phrase is decoded.
Last, we modifies the beam search algorithm to impose the meter-and-rhyme constraint.
Algorithm 1 displays the skeleton of our decoding strategy.
At each decoding step, we apply rhythm control, so that only those tokens that satisfy the iambicpentameter and its two variations (listed in Section 2.2) are kept in the beams.
We recursively generate the next token until 10 or 11 syllables are generated and make up a metric line where all the context words are incorporated.
Experimental Setup 4. Dataset
Our approach does not require poem data.
The training dataset for the content planing module and the decoding module is a combination of [-4,
-]4,
CNN news summary (Hermann et al., 2015) and 16,000 short stories crawled from Reddit 3 .
We remove those articles that contain conversations, urls, or are too long (>50 lines) or too short (< lines).
During decoding, we generate sonnets using top-k sampling and set no_repeat_ngram_size toto promote creativity and avoid repetition.
We finetune the pretrained T5 for 10 epochs for the “content planning” component, and finetune
GPT-Neo-2.7B for 6 epochs for the decoding component.
We use one Nvidia A100 40GB GPU.
The average training time is 5 ∼ 10 hours for each experiment.
4. Baselines
Hafez
A program that is trained on lyrics data and generates sonnets on a user-supplied topic (Ghazvininejad et al., 2018).
It combines
RNNs with a finite state automata to meet the meter and rhyme constraints.
Hafez is the state-of-the-art model that generates full-length sonnets but it does not train on standard, non-poetic texts.
Few-shot GPT-
We utilize the most capable model in the GPT-3 family (Brown et al., 2020),
GPT3-davinci 4 , as a strong baseline to follow instructions and generate sonnets.
In the prompt, we provide two examples of standard sonnets and then instruct the model to generate a sonnet given the title.
We force the output to be exactly 14 lines.
Ablations of our own model
To test the effectiveness of our sketch-before-writing mechanism, we also compare variations of our own model:
Prosaic
An stronger version of nmf (Van de Cruys, 2020), the first (and only) model to generate rhyming verses from prosaic texts. Topical and rhyme consistency are achieved by modifying the word probability of rhyme and topical words.
For fair comparison, we replace the original vanilla encoder-decoder with GPT2 that Zest is finetuned on, and force the output to be 14 lines.
Model comparison between Prosaic and Zest serves as ablations of the keyword-planning component (versus end-to-end generation).
Zest w/o fig
The model consisting of step a, c, and d as illustrated in Figure 1, but without the polishing the sketch for figurative devices.
Our full model consisting of 4 modules is called Zest .
4. Decoding Strategy
For decoding, we generate sonnets from our models using a top-k random sampling scheme where k is set to 50.
At each time step, the GPT2 model generates subwords instead of complete words.
In order to impose the meter and rhyme constraints while decoding for each word, we ask the language model to continue to generate until a complete word is generated as indicated by special space token ‘ ˙G’.
To avoid repetition and encourage creativity, we set no_repeat_ngram_size to 3 and use a softmax temperature of 0.85.
4. Automatic Evaluation
It is difficult and thus uncommon to automatically evaluate the quality of poems.
For example, Ghazvininejad et al.
and Van de Cruys (2020) exclude automatic evaluation, with the later stating “Automatic evaluation measures that compute the overlap of system output with gold reference texts such as BLEU or ROUGE are of little use when it comes to creative language generation." In addition, Yang et al.
(2020) show current metrics have very low correlation with human.
Hence, we propose to evaluate the generated poems in two novel aspects: format and novelty.
Format Checking
For rhyme checking, we count the percentage of rhyme pairs that belong to strict or slant rhymes.
For meter checking, we consider the following most common scenarios mentioned in Section 2.2: the standard Iambic Pentameter; the first foot reversed; and a feminine rhyme.
In all scenarios, words that are monosyllables can serve as both stressed and unstressed syllables.
For a looser standard, we also calculate the percentage of valid lines that contain either 10 or 11 syllables.
Novelty
We follow the settings in exsiting works
Yi et al.
and calculate the Distinctscores (Li et al., 2015) to measure the diversity of generated poems.
Besides, imagery is another important feature of poems as pointed out by linguistic studies Kao and Jurafsky (2012); Silk (2006).
Here, we calculate Imageability score to assess how well a poem invokes mental pictures of concrete objects.
Specifically, we extracted the features from the resource by Tsvetkov et al.
(2014), who use a supervised learning algorithm to calculate the imageability ratings of 150,114 terms.
For each poem, we average the ratings of all its words after removing the stop words.
4. Human Expert Judgement
Considering the expertise required to appreciate sonnets, we recruit 6 professionals that hold a bachelor’s degree in English literature or related majors as domain experts to annotate the generated sonnets.
We provide detailed instructions and ask them to evaluate the each poem on a scale from 1 (not at all) to 5 (very) on the following criteria: 1) Discourse
Coherence : whether the sonnet is well organized, with the sentences smoothly connected and flow together logically and aesthetically, 2) Originality/Creativity : the usage of original ideas in the [Table] [Table] poem, including imagination, rhetorical devices, etc., 3) Poetic in language : how well the poem adopts descriptive and vivid language that often has an economical or condensed usage, 4)
Emotion Evoking : if the poem is emotionally abundant and make the readers emphasize with the writer.
At last, we ask the annotators to judge if the sonnet is written by a poet with serious goals to write a poem.
In total, we evaluate 50 sonnets for each baseline and the gold standard (human) model.
Each sonnet is rated by three professionals.
The average inter-annotator agreement (IAA) in terms of Pearson correlation is 0.61 with p-value <0.01, meaning that our collected ratings are highly reliable.
We also conduct paired t-test for significance testing.
The difference between our best performing model and the best baseline is significant.
Considering the expertise required, human evaluators are paid $25 per hour.
Results and Analysis 5. Results of Automatic Evaluation
Table 2 summarizes the format checking and novelty scores of our model compared to the baselines.
We can see that human poets tend to incorporate more variations and do not strictly follow the meter and rhyme constraints, which computers are good at.
GPT-3 fails to learn the sonnet formats through massive pretraining and few-shot learning despite its gigantic size.
Prosaic falls short of meter-checking because is only trained to generate rhyming verses.
Since we utilize the the phonetics information provided in the CMU dictionary, Zest achieves 100% success in rhyme words pairing.
As for novelty, Zest generates most diversely and is best at that arousing mental pictures of concrete objects among machines.
5. Results of Human Evaluation
Table 3 presents the performance of the aforementioned evaluation criteria: coherence, originality, poeticness, and emotion-evoking.
Our models ( Zest w/o fig , and Zest ) outperform the baselines in all aspects by a large margin.
Comparison between our own models. Compared with Prosaic which also generates poems from non-poetic texts, our models generates more coherent sonnets with great statistical significance (p-value < 0.01), showing the superiority of explicit sketch planning over generating from scratch (i.e., end-to-end generation).
Zest w/o fig generates more coherently than
Zest (p-value < 0.10).
However, Zest achieves high scores in originality, poeticness by a large margin (+0.2).
Hence, we still consider it as our best model.
It is also noteworthy that Zest is the most emotion-evoking system among all machines even though we do not have explicit sentiment control.
Poem theories have shown that emotion appeals lie in the following aspects: the general topic, the word choice, vivid descriptions, figurative language, insights and experience (Scheub, 2002).
We posit that aesthetic features in the Zest arouse emotion appeals.
Analysis for high poeticness.
Zest is on par with humans in terms of poeticness score, meaning that our models generate highly descriptive, vivid, and condensed text.
With manual examination, we attribute such high poeticness to three aspects.
First, the imagery and similes clearly represents traits of poems.
Second, in keyword-planning we ensure that at least three concepts will be presented per line, and thus the generation module naturally become economical in word usage to include all the information.
Lastly, with the constraint decoding algorithm to insert keywords, we inevitably become less natural (e.g., miss conjunctions and auxiliary verbs).
While this can be a drawback in other generation tasks, the occasional omission of such auxiliary words is just opportune for sonnets, and adds to the flavor of a poem.
The examples in table 4 helps demonstrate these points.
Qualitative Analysis 6. Case Study
We conduct case study to better understand the advantages of our model over the baselines.
Table 4 lists the generated sonnets by Hafez, Prosaic and
Zest given the same title: “The Four Seasons".
Problems with the Baselines
Hafez chooses words that are related to the title as rhyme words.
However, topically related rhyme words are not sufficient for overall coherence.
While it is locally understandable, the sonnet generated by Hafez is divergent and disconnected when sentences are put together.
On the other hand, Prosaic mimics the rhyme and topical properties of poems, but still generate highly prosaic and colloquial sentences that are not poetic at all.
Advantages of Our Model
Thanks to content planning, Zest w/o fig generates a well-organized sonnet that describes the four seasons from winter to autumn in a logical order.
Despite minor grammar errors, the full model Zest benefits from vivid descriptions and natural imagery such as ‘whispers rumors of a winter coming’, ‘blossom of the season’, and ‘sudden like a flash’.
6. Impact of Keywords
By comparing Zest w/o fig versus Prosaic, our human evaluation results already show that content planning contributes to discourse-level coherence.
In addition, we provide the keywords along with the sonnet generated by Zest , and ask human annotators to judge if the sonnet can be condensed into those keywords.
Results are shown in Figure 4.
We observe that 82% of the time the planed keywords successfully guide the generation by providing salient points of the sonnet.
We then conduct error analysis on the rest 18%.
Top two reasons [Table] Falling snow, [Table]
feel
[Table]
little cooler
[Table]
Is
[Table]
sign of stormy winter coming
[Table]
Meet
[Table]
honest friends and
[Table]
Happy spring and just admire the blooming
[Table]
The heat and ocean air becoming warm, Little birds and beautiful songs in May,
Leaves and flowers probably just the storm, Among the lunar moons and summer day.
Sudden rain and downpour from the thunder, And summer always fill hotels with crowds,
Take a shower and give the spring a wonder, Watch the blue sky and far behind the clouds.
In months the future vegetables reap, The years and seasons never really keep.
[Table]
The snow is falling, whether getting cooler
[Table]
among the fail cases are: 1) the decoding step generates novel contents that are not represented by the keywords (8%), and 2) the polishing step alters the original meaning of planed keywords (6%).
6. Limitation and Future Direction
Sonnets are divided in to multiple stanzas.
Lines within a stanza are more interlaced than across stanza, and the start of a new one usually indicates transition to another viewpoint.
Our current approach could not capture such structural characteristics during planning and generation, and we hope to investigate these features in future work.
We also plan to extend this poem generation pipeline to other languages.
For example, pretrained LMs (e.g. multilingual T5) and existing rhyming resources (r.g. rhymes.woxikon.com provides rhymes in 13 languages) already made the first and second component transferable to other languages.
Related Work Poetry Generation
Automatic poetry generation before the deep learning age relies heavily on templates, norms, or rule-based approaches (Gervás, 2001; Manurung, 2004; Manurung et al., 2012).
Neural approaches to automatic poetry generation
pay little attention to the coherence issue of long poems.
For example, Wang et al.
; Lau et al.
; Yi et al.
Liu et al.
(2019) merely target at generating the first stanza (four lines) of a poem.
For longer poems such as sonnets,
Ghazvininejad et al.
(2016) propose to use related words as rhyme words to achieve topical relatedness, and later propose to generate discourse-level coherent English sonnets by French-English translation (Ghazvininejad et al., 2018).
Van de Cruys (2020) propose a naive RNN framework to generate rhyming verses from prosaic texts by imposing a priori word probability constraints.
We on the other hand achieve discourse-level coherence by learning from standard, non-poetic texts.
Other related works to boost the creativity of generated poems include adding rhetorical (Liu et al., 2019) and influence factors (e.g., historical background) as latent variables (Yi et al., 2020).
To the best of our knowledge, we are the first to explore adding both figurative speeches and meterand-rhyme constraints to poetry generation without relying on poetry data.
Content Planning
Content planning for automatic text generation originates in the 1970s (Meehan, 1977).
Recently, the plan-and-write generation framework has shown to be efficient in creative content generation (Wang et al., 2016; Martin et al., 2018; Yao et al., 2019; Gao et al., 2019).
The framework employs a hierarchical paradigm and helps to produce more coherent and controllable generation than generating from scratch (Fan et al., 2019;
Goldfarb-Tarrant et al., 2020).
However, all existing works under this line learn the storyline/plot from the target domain for improved coherence.
We on the other hand adopt content planning to disentangle the training from the decoding step which aims at circumventing the shortage of sizable creative contents for training supervised models.
Conclusion
We investigate the possibility of generating sonnets without training on poems at all.
We propose a hierarchical planning-based framework to generate sonnets which first plans the high-level content of the poem, refine the predicted keywords by adding poetic features, and then achieve decoding-time control to impose the meter-and-rhyme constraints.
Extensive automatic and expert evaluation show that our model can generate sonnets that use rich imagery and are globally coherent, poetic, and emotion provoking.
Acknowledgments
The authors would like to thank the members of
PLUSLab and the anonymous reviewers for helpful comments.
Yufei is partially supported by an
Amazon Fellowship.