Multi-Figurative Language Generation

• URL: http://arxiv.org/abs/2209.01835v1
• Date: Mon, 5 Sep 2022 08:48:09 GMT
• Title: Multi-Figurative Language Generation
• Authors: Huiyuan Lai and Malvina Nissim
• Abstract summary: Figurative language generation is the task of reformulating a given text in the desired figure of speech while still being faithful to the original context. We take the first step towards multi-figurative language modelling by providing a benchmark for the automatic generation of five common figurative forms in English.
• Score: 14.13782709351219
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Figurative language generation is the task of reformulating a given text in the desired figure of speech while still being faithful to the original context. We take the first step towards multi-figurative language modelling by providing a benchmark for the automatic generation of five common figurative forms in English. We train mFLAG employing a scheme for multi-figurative language pre-training on top of BART, and a mechanism for injecting the target figurative information into the encoder; this enables the generation of text with the target figurative form from another figurative form without parallel figurative-figurative sentence pairs. Our approach outperforms all strong baselines. We also offer some qualitative analysis and reflections on the relationship between the different figures of speech.

Related papers

• Towards Visual Text Design Transfer Across Languages [49.78504488452978]
  We introduce a novel task of Multimodal Style Translation (MuST-Bench) MuST-Bench is a benchmark designed to evaluate the ability of visual text generation models to perform translation across different writing systems. In response, we introduce SIGIL, a framework for multimodal style translation that eliminates the need for style descriptions.
  arXiv  Detail & Related papers  (2024-10-24T15:15:01Z)
• A Perspective on Literary Metaphor in the Context of Generative AI [0.6445605125467572]
  This study explores the role of literary metaphor and its capacity to generate a range of meanings. To investigate whether the inclusion of original figurative language improves textual quality, we trained an LSTM-based language model in Afrikaans. The paper raises thought-provoking questions on aesthetic value, interpretation and evaluation.
  arXiv  Detail & Related papers  (2024-09-02T08:27:29Z)
• Multilingual Conceptual Coverage in Text-to-Image Models [98.80343331645626]
  "Conceptual Coverage Across Languages" (CoCo-CroLa) is a technique for benchmarking the degree to which any generative text-to-image system provides multilingual parity to its training language in terms of tangible nouns. For each model we can assess "conceptual coverage" of a given target language relative to a source language by comparing the population of images generated for a series of tangible nouns in the source language to the population of images generated for each noun under translation in the target language.
  arXiv  Detail & Related papers  (2023-06-02T17:59:09Z)
• Multilingual Multi-Figurative Language Detection [14.799109368073548]
  figurative language understanding is highly understudied in a multilingual setting. We introduce multilingual multi-figurative language modelling, and provide a benchmark for sentence-level figurative language detection. We develop a framework for figurative language detection based on template-based prompt learning.
  arXiv  Detail & Related papers  (2023-05-31T18:52:41Z)
• I Spy a Metaphor: Large Language Models and Diffusion Models Co-Create Visual Metaphors [38.70166865926743]
  We propose a new task of generating visual metaphors from linguistic metaphors. This is a challenging task for diffusion-based text-to-image models, since it requires the ability to model implicit meaning and compositionality. We create a high-quality dataset containing 6,476 visual metaphors for 1,540 linguistic metaphors and their associated visual elaborations.
  arXiv  Detail & Related papers  (2023-05-24T05:01:10Z)
• IRFL: Image Recognition of Figurative Language [20.472997304393413]
  Figurative forms are often conveyed through multiple modalities (e.g., both text and images) We develop the Image Recognition of Figurative Language dataset. We introduce two novel tasks as a benchmark for multimodal figurative language understanding.
  arXiv  Detail & Related papers  (2023-03-27T17:59:55Z)
• Testing the Ability of Language Models to Interpret Figurative Language [69.59943454934799]
  Figurative and metaphorical language are commonplace in discourse. It remains an open question to what extent modern language models can interpret nonliteral phrases. We introduce Fig-QA, a Winograd-style nonliteral language understanding task.
  arXiv  Detail & Related papers  (2022-04-26T23:42:22Z)
• Investigating Robustness of Dialog Models to Popular Figurative Language Constructs [30.841109045790862]
  We analyze the performance of existing dialog models in situations where the input dialog context exhibits use of figurative language. We propose lightweight solutions to help existing models become more robust to figurative language.
  arXiv  Detail & Related papers  (2021-10-01T23:55:16Z)
• Direct speech-to-speech translation with discrete units [64.19830539866072]
  We present a direct speech-to-speech translation (S2ST) model that translates speech from one language to speech in another language without relying on intermediate text generation. We propose to predict the self-supervised discrete representations learned from an unlabeled speech corpus instead. When target text transcripts are available, we design a multitask learning framework with joint speech and text training that enables the model to generate dual mode output (speech and text) simultaneously in the same inference pass.
  arXiv  Detail & Related papers  (2021-07-12T17:40:43Z)
• InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training [135.12061144759517]
  We present an information-theoretic framework that formulates cross-lingual language model pre-training. We propose a new pre-training task based on contrastive learning. By leveraging both monolingual and parallel corpora, we jointly train the pretext to improve the cross-lingual transferability of pre-trained models.
  arXiv  Detail & Related papers  (2020-07-15T16:58:01Z)
• PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation [92.7366819044397]
  Self-supervised pre-training has emerged as a powerful technique for natural language understanding and generation. This work presents PALM with a novel scheme that jointly pre-trains an autoencoding and autoregressive language model on a large unlabeled corpus. An extensive set of experiments show that PALM achieves new state-of-the-art results on a variety of language generation benchmarks.
  arXiv  Detail & Related papers  (2020-04-14T06:25:36Z)

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.