Encoder-Decoder Framework for Interactive Free Verses with Generation with Controllable High-Quality Rhyming

• URL: http://arxiv.org/abs/2405.05176v1
• Date: Wed, 8 May 2024 16:13:40 GMT
• Title: Encoder-Decoder Framework for Interactive Free Verses with Generation with Controllable High-Quality Rhyming
• Authors: Tommaso Pasini, Alejo López-Ávila, Husam Quteineh, Gerasimos Lampouras, Jinhua Du, Yubing Wang, Ze Li, Yusen Sun,
• Abstract summary: We propose a novel fine-tuning approach that prepends the rhyming word at the start of each lyric. We conducted extensive experiments to compare this fine-tuning against the current state-of-the-art strategies for rhyming. We furnish a high-quality dataset in English and 12 other languages, analyse the approach's feasibility in a multilingual context, and propose metrics to compare methods in the future.
• Score: 15.595206559574017
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Composing poetry or lyrics involves several creative factors, but a challenging aspect of generation is the adherence to a more or less strict metric and rhyming pattern. To address this challenge specifically, previous work on the task has mainly focused on reverse language modeling, which brings the critical selection of each rhyming word to the forefront of each verse. On the other hand, reversing the word order requires that models be trained from scratch with this task-specific goal and cannot take advantage of transfer learning from a Pretrained Language Model (PLM). We propose a novel fine-tuning approach that prepends the rhyming word at the start of each lyric, which allows the critical rhyming decision to be made before the model commits to the content of the lyric (as during reverse language modeling), but maintains compatibility with the word order of regular PLMs as the lyric itself is still generated in left-to-right order. We conducted extensive experiments to compare this fine-tuning against the current state-of-the-art strategies for rhyming, finding that our approach generates more readable text and better rhyming capabilities. Furthermore, we furnish a high-quality dataset in English and 12 other languages, analyse the approach's feasibility in a multilingual context, provide extensive experimental results shedding light on good and bad practices for lyrics generation, and propose metrics to compare methods in the future.

Related papers

• Rhyme-aware Chinese lyric generator based on GPT [1.2813386930534034]
  GPT, pre-trained on large-scale corpora, can effectively capture rich semantic patterns from plain text. However, existing pre-trained language models used to generate lyrics rarely consider rhyme information, which is crucial in lyrics.
  arXiv  Detail & Related papers  (2024-08-19T16:17:20Z)
• Retrieval is Accurate Generation [99.24267226311157]
  We introduce a novel method that selects context-aware phrases from a collection of supporting documents. Our model achieves the best performance and the lowest latency among several retrieval-augmented baselines.
  arXiv  Detail & Related papers  (2024-02-27T14:16:19Z)
• Syllable-level lyrics generation from melody exploiting character-level language model [14.851295355381712]
  We propose to exploit fine-tuning character-level language models for syllable-level lyrics generation from symbolic melody. In particular, our method endeavors to incorporate linguistic knowledge of the language model into the beam search process of a syllable-level Transformer generator network.
  arXiv  Detail & Related papers  (2023-10-02T02:53:29Z)
• Unsupervised Melody-to-Lyric Generation [91.29447272400826]
  We propose a method for generating high-quality lyrics without training on any aligned melody-lyric data. We leverage the segmentation and rhythm alignment between melody and lyrics to compile the given melody into decoding constraints. Our model can generate high-quality lyrics that are more on-topic, singable, intelligible, and coherent than strong baselines.
  arXiv  Detail & Related papers  (2023-05-30T17:20:25Z)
• Unsupervised Melody-Guided Lyrics Generation [84.22469652275714]
  We propose to generate pleasantly listenable lyrics without training on melody-lyric aligned data. We leverage the crucial alignments between melody and lyrics and compile the given melody into constraints to guide the generation process.
  arXiv  Detail & Related papers  (2023-05-12T20:57:20Z)
• SongRewriter: A Chinese Song Rewriting System with Controllable Content and Rhyme Scheme [32.60994266892925]
  We propose a controllable Chinese lyrics generation and editing system which assists users without prior knowledge of melody composition. The system is trained by a randomized multi-level masking strategy which produces a unified model for generating entirely new lyrics or editing a few fragments.
  arXiv  Detail & Related papers  (2022-11-28T03:52:05Z)
• Pre-training via Paraphrasing [96.79972492585112]
  We introduce MARGE, a pre-trained sequence-to-sequence model learned with an unsupervised multi-lingual paraphrasing objective. We show it is possible to jointly learn to do retrieval and reconstruction, given only a random initialization. For example, with no additional task-specific training we achieve BLEU scores of up to 35.8 for document translation.
  arXiv  Detail & Related papers  (2020-06-26T14:43:43Z)
• Toward Better Storylines with Sentence-Level Language Models [54.91921545103256]
  We propose a sentence-level language model which selects the next sentence in a story from a finite set of fluent alternatives. We demonstrate the effectiveness of our approach with state-of-the-art accuracy on the unsupervised Story Cloze task.
  arXiv  Detail & Related papers  (2020-05-11T16:54:19Z)
• Improving Adversarial Text Generation by Modeling the Distant Future [155.83051741029732]
  We consider a text planning scheme and present a model-based imitation-learning approach to alleviate the aforementioned issues. We propose a novel guider network to focus on the generative process over a longer horizon, which can assist next-word prediction and provide intermediate rewards for generator optimization.
  arXiv  Detail & Related papers  (2020-05-04T05:45:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.