Density Matrices for Metaphor Understanding

• URL: http://arxiv.org/abs/2408.11846v1
• Date: Mon, 12 Aug 2024 11:21:56 GMT
• Title: Density Matrices for Metaphor Understanding
• Authors: Jay Owers, Ekaterina Shutova, Martha Lewis,
• Abstract summary: We consider metaphor as a type of lexical ambiguity, and examine whether metaphorical meaning can be effectively modelled using mixtures of word senses. Our best-performing density matrix method outperforms simple baselines as well as some neural language models.
• Score: 12.568794861914451
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In physics, density matrices are used to represent mixed states, i.e. probabilistic mixtures of pure states. This concept has previously been used to model lexical ambiguity. In this paper, we consider metaphor as a type of lexical ambiguity, and examine whether metaphorical meaning can be effectively modelled using mixtures of word senses. We find that modelling metaphor is significantly more difficult than other kinds of lexical ambiguity, but that our best-performing density matrix method outperforms simple baselines as well as some neural language models.

Related papers

• Conjuring Semantic Similarity [59.18714889874088]
  The semantic similarity between two textual expressions measures the distance between their latent'meaning' We propose a novel approach whereby the semantic similarity among textual expressions is based not on other expressions they can be rephrased as, but rather based on the imagery they evoke. Our method contributes a novel perspective on semantic similarity that not only aligns with human-annotated scores, but also opens up new avenues for the evaluation of text-conditioned generative models.
  arXiv  Detail & Related papers  (2024-10-21T18:51:34Z)
• That was the last straw, we need more: Are Translation Systems Sensitive to Disambiguating Context? [64.38544995251642]
  We study semantic ambiguities that exist in the source (English in this work) itself. We focus on idioms that are open to both literal and figurative interpretations. We find that current MT models consistently translate English idioms literally, even when the context suggests a figurative interpretation.
  arXiv  Detail & Related papers  (2023-10-23T06:38:49Z)
• Metaphor Detection via Explicit Basic Meanings Modelling [12.096691826237114]
  We propose a novel metaphor detection method, which models the basic meaning of the word based on literal annotation from the training set. Empirical results show that our method outperforms the state-of-the-art method significantly by 1.0% in F1 score.
  arXiv  Detail & Related papers  (2023-05-26T21:25:05Z)
• LMs stand their Ground: Investigating the Effect of Embodiment in Figurative Language Interpretation by Language Models [0.0]
  Figurative language is a challenge for language models since its interpretation deviates from their conventional order and meaning. Yet, humans can easily understand and interpret metaphors as they can be derived from embodied metaphors. This study shows how larger language models perform better at interpreting metaphoric sentences when the action of the metaphorical sentence is more embodied.
  arXiv  Detail & Related papers  (2023-05-05T11:44:12Z)
• Metaphorical Polysemy Detection: Conventional Metaphor meets Word Sense Disambiguation [9.860944032009847]
  Linguists distinguish between novel and conventional metaphor, a distinction which the metaphor detection task in NLP does not take into account. In this paper, we investigate the limitations of treating conventional metaphors in this way. We develop the first MPD model, which learns to identify conventional metaphors in the English WordNet.
  arXiv  Detail & Related papers  (2022-12-16T10:39:22Z)
• It's not Rocket Science : Interpreting Figurative Language in Narratives [48.84507467131819]
  We study the interpretation of two non-compositional figurative languages (idioms and similes) Our experiments show that models based solely on pre-trained language models perform substantially worse than humans on these tasks. We additionally propose knowledge-enhanced models, adopting human strategies for interpreting figurative language.
  arXiv  Detail & Related papers  (2021-08-31T21:46:35Z)
• Metaphor Generation with Conceptual Mappings [58.61307123799594]
  We aim to generate a metaphoric sentence given a literal expression by replacing relevant verbs. We propose to control the generation process by encoding conceptual mappings between cognitive domains. We show that the unsupervised CM-Lex model is competitive with recent deep learning metaphor generation systems.
  arXiv  Detail & Related papers  (2021-06-02T15:27:05Z)
• MERMAID: Metaphor Generation with Symbolism and Discriminative Decoding [22.756157298168127]
  Based on a theoretically-grounded connection between metaphors and symbols, we propose a method to automatically construct a parallel corpus. For the generation task, we incorporate a metaphor discriminator to guide the decoding of a sequence to sequence model fine-tuned on our parallel data. A task-based evaluation shows that human-written poems enhanced with metaphors are preferred 68% of the time compared to poems without metaphors.
  arXiv  Detail & Related papers  (2021-03-11T16:39:19Z)
• Lexical semantic change for Ancient Greek and Latin [61.69697586178796]
  Associating a word's correct meaning in its historical context is a central challenge in diachronic research. We build on a recent computational approach to semantic change based on a dynamic Bayesian mixture model. We provide a systematic comparison of dynamic Bayesian mixture models for semantic change with state-of-the-art embedding-based models.
  arXiv  Detail & Related papers  (2021-01-22T12:04:08Z)
• Modelling Lexical Ambiguity with Density Matrices [3.7692411550925664]
  We present three new neural models for learning density matrices from a corpus. Test their ability to discriminate between word senses on a range of compositional datasets.
  arXiv  Detail & Related papers  (2020-10-12T13:08:45Z)
• Metaphoric Paraphrase Generation [58.592750281138265]
  We use crowdsourcing to evaluate our results, as well as developing an automatic metric for evaluating metaphoric paraphrases. We show that while the lexical replacement baseline is capable of producing accurate paraphrases, they often lack metaphoricity. Our metaphor masking model excels in generating metaphoric sentences while performing nearly as well with regard to fluency and paraphrase quality.
  arXiv  Detail & Related papers  (2020-02-28T16:30:33Z)

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.