Dancing with Deer: A Constructional Perspective on MWEs in the Era of LLMs

• URL: http://arxiv.org/abs/2508.15977v1
• Date: Thu, 21 Aug 2025 21:42:50 GMT
• Title: Dancing with Deer: A Constructional Perspective on MWEs in the Era of LLMs
• Authors: Claire Bonial, Julia Bonn, Harish Tayyar Madabushi,
• Abstract summary: We argue for the benefits of understanding multiword expressions from the perspective of usage-based, construction grammar approaches.<n>We describe a successful case study leveraging constructional templates for representing multiword expressions in English PropBank.<n>We include a second case study leveraging constructional templates for representing these multi-morphemic expressions in Uniform Meaning Representation.
• Score: 6.449214426814449
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this chapter, we argue for the benefits of understanding multiword expressions from the perspective of usage-based, construction grammar approaches. We begin with a historical overview of how construction grammar was developed in order to account for idiomatic expressions using the same grammatical machinery as the non-idiomatic structures of language. We cover a comprehensive description of constructions, which are pairings of meaning with form of any size (morpheme, word, phrase), as well as how constructional approaches treat the acquisition and generalization of constructions. We describe a successful case study leveraging constructional templates for representing multiword expressions in English PropBank. Because constructions can be at any level or unit of form, we then illustrate the benefit of a constructional representation of multi-meaningful morphosyntactic unit constructions in Arapaho, a highly polysynthetic and agglutinating language. We include a second case study leveraging constructional templates for representing these multi-morphemic expressions in Uniform Meaning Representation. Finally, we demonstrate the similarities and differences between a usage-based explanation of a speaker learning a novel multiword expression, such as "dancing with deer," and that of a large language model. We present experiments showing that both models and speakers can generalize the meaning of novel multiword expressions based on a single exposure of usage. However, only speakers can reason over the combination of two such expressions, as this requires comparison of the novel forms to a speaker's lifetime of stored constructional exemplars, which are rich with cross-modal details.

Related papers

• Finding Structure in Language Models [3.882018118763685]
  This thesis is about whether language models possess a deep understanding of grammatical structure similar to that of humans. We will develop novel interpretability techniques that enhance our understanding of the complex nature of large-scale language models.
  arXiv  Detail & Related papers  (2024-11-25T14:37:24Z)
• Investigating Idiomaticity in Word Representations [9.208145117062339]
  We focus on noun compounds of varying levels of idiomaticity in two languages (English and Portuguese) We present a dataset of minimal pairs containing human idiomaticity judgments for each noun compound at both type and token levels. We define a set of fine-grained metrics of Affinity and Scaled Similarity to determine how sensitive the models are to perturbations that may lead to changes in idiomaticity.
  arXiv  Detail & Related papers  (2024-11-04T21:05:01Z)
• Exploring syntactic information in sentence embeddings through multilingual subject-verb agreement [1.4335183427838039]
  We take the approach of developing curated synthetic data on a large scale, with specific properties.<n>We use a new multiple-choice task and datasets, Blackbird Language Matrices, to focus on a specific grammatical structural phenomenon.<n>We show that despite having been trained on multilingual texts in a consistent manner, multilingual pretrained language models have language-specific differences.
  arXiv  Detail & Related papers  (2024-09-10T14:58:55Z)
• A Method for Studying Semantic Construal in Grammatical Constructions with Interpretable Contextual Embedding Spaces [11.564029462243631]
  We study semantic construal in grammatical constructions using large language models. We show that a word in subject position is interpreted as more agentive than the very same word in object position. Our method can probe the distributional meaning of syntactic constructions at a templatic level, abstracted away from specific lexemes.
  arXiv  Detail & Related papers  (2023-05-29T20:30:38Z)
• 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)
• Do Language Models Learn Position-Role Mappings? [1.4548651568912523]
  We test whether well-performing pertained language models (BERT, RoBERTa, and DistilBERT) exhibit knowledge of position-role mappings. In Experiment 1, we show that these neural models do indeed recognize distinctions between theme and recipient roles. In Experiment 2, we show that fine-tuning these language models on novel theme- and recipient-like tokens in one paradigm allows the models to make correct predictions about their placement in other paradigms.
  arXiv  Detail & Related papers  (2022-02-08T02:50:53Z)
• Understanding Synonymous Referring Expressions via Contrastive Features [105.36814858748285]
  We develop an end-to-end trainable framework to learn contrastive features on the image and object instance levels. We conduct extensive experiments to evaluate the proposed algorithm on several benchmark datasets.
  arXiv  Detail & Related papers  (2021-04-20T17:56:24Z)
• Accurate Word Representations with Universal Visual Guidance [55.71425503859685]
  This paper proposes a visual representation method to explicitly enhance conventional word embedding with multiple-aspect senses from visual guidance. We build a small-scale word-image dictionary from a multimodal seed dataset where each word corresponds to diverse related images. Experiments on 12 natural language understanding and machine translation tasks further verify the effectiveness and the generalization capability of the proposed approach.
  arXiv  Detail & Related papers  (2020-12-30T09:11:50Z)
• Unsupervised Distillation of Syntactic Information from Contextualized Word Representations [62.230491683411536]
  We tackle the task of unsupervised disentanglement between semantics and structure in neural language representations. To this end, we automatically generate groups of sentences which are structurally similar but semantically different. We demonstrate that our transformation clusters vectors in space by structural properties, rather than by lexical semantics.
  arXiv  Detail & Related papers  (2020-10-11T15:13:18Z)
• SPLAT: Speech-Language Joint Pre-Training for Spoken Language Understanding [61.02342238771685]
  Spoken language understanding requires a model to analyze input acoustic signal to understand its linguistic content and make predictions. Various pre-training methods have been proposed to learn rich representations from large-scale unannotated speech and text. We propose a novel semi-supervised learning framework, SPLAT, to jointly pre-train the speech and language modules.
  arXiv  Detail & Related papers  (2020-10-05T19:29:49Z)
• BURT: BERT-inspired Universal Representation from Twin Structure [89.82415322763475]
  BURT (BERT inspired Universal Representation from Twin Structure) is capable of generating universal, fixed-size representations for input sequences of any granularity. Our proposed BURT adopts the Siamese network, learning sentence-level representations from natural language inference dataset and word/phrase-level representations from paraphrasing dataset. We evaluate BURT across different granularities of text similarity tasks, including STS tasks, SemEval2013 Task 5(a) and some commonly used word similarity tasks.
  arXiv  Detail & Related papers  (2020-04-29T04:01:52Z)

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.