Can Pre-trained Language Models Interpret Similes as Smart as Human?

• URL: http://arxiv.org/abs/2203.08452v1
• Date: Wed, 16 Mar 2022 07:57:34 GMT
• Title: Can Pre-trained Language Models Interpret Similes as Smart as Human?
• Authors: Qianyu He, Sijie Cheng, Zhixu Li, Rui Xie, Yanghua Xiao
• Abstract summary: We design a novel task named Simile Property Probing to let pre-trained language models infer the shared properties of similes. Our empirical study shows that PLMs can infer similes' shared properties while still underperforming humans. To bridge the gap with human performance, we additionally design a knowledge-enhanced training objective by incorporating the simile knowledge into PLMs.
• Score: 15.077252268027548
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Simile interpretation is a crucial task in natural language processing. Nowadays, pre-trained language models (PLMs) have achieved state-of-the-art performance on many tasks. However, it remains under-explored whether PLMs can interpret similes or not. In this paper, we investigate the ability of PLMs in simile interpretation by designing a novel task named Simile Property Probing, i.e., to let the PLMs infer the shared properties of similes. We construct our simile property probing datasets from both general textual corpora and human-designed questions, containing 1,633 examples covering seven main categories. Our empirical study based on the constructed datasets shows that PLMs can infer similes' shared properties while still underperforming humans. To bridge the gap with human performance, we additionally design a knowledge-enhanced training objective by incorporating the simile knowledge into PLMs via knowledge embedding methods. Our method results in a gain of 8.58% in the probing task and 1.37% in the downstream task of sentiment classification. The datasets and code are publicly available at https://github.com/Abbey4799/PLMs-Interpret-Simile.

Related papers

• HARGPT: Are LLMs Zero-Shot Human Activity Recognizers? [9.414529772034985]
  We show that Large Language Models (LLMs) can comprehend raw IMU data and perform human activity recognition tasks in a zero-shot manner. We benchmark HARGPT on GPT4 using two public datasets of different inter-class similarities and compare various baselines both based on traditional machine learning and state-of-the-art deep classification models. Remarkably, LLMs successfully recognize human activities from raw IMU data and consistently outperform all the baselines on both datasets.
  arXiv  Detail & Related papers  (2024-03-05T07:34:51Z)
• CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotation [94.59630161324013]
  We propose CoAnnotating, a novel paradigm for Human-LLM co-annotation of unstructured texts at scale. Our empirical study shows CoAnnotating to be an effective means to allocate work from results on different datasets, with up to 21% performance improvement over random baseline.
  arXiv  Detail & Related papers  (2023-10-24T08:56:49Z)
• Character-LLM: A Trainable Agent for Role-Playing [67.35139167985008]
  Large language models (LLMs) can be used to serve as agents to simulate human behaviors. We introduce Character-LLM that teach LLMs to act as specific people such as Beethoven, Queen Cleopatra, Julius Caesar, etc.
  arXiv  Detail & Related papers  (2023-10-16T07:58:56Z)
• Unsupervised Sentiment Analysis of Plastic Surgery Social Media Posts [91.3755431537592]
  The massive collection of user posts across social media platforms is primarily untapped for artificial intelligence (AI) use cases. Natural language processing (NLP) is a subfield of AI that leverages bodies of documents, known as corpora, to train computers in human-like language understanding. This study demonstrates that the applied results of unsupervised analysis allow a computer to predict either negative, positive, or neutral user sentiment towards plastic surgery.
  arXiv  Detail & Related papers  (2023-07-05T20:16:20Z)
• How Does Pretraining Improve Discourse-Aware Translation? [41.20896077662125]
  We introduce a probing task to interpret the ability of pretrained language models to capture discourse relation knowledge. We validate three state-of-the-art PLMs across encoder-, decoder-, and encoder-decoder-based models. Our findings are instructive to understand how and when discourse knowledge in PLMs should work for downstream tasks.
  arXiv  Detail & Related papers  (2023-05-31T13:36:51Z)
• Can Large Language Models Transform Computational Social Science? [79.62471267510963]
  Large Language Models (LLMs) are capable of performing many language processing tasks zero-shot (without training data) This work provides a road map for using LLMs as Computational Social Science tools.
  arXiv  Detail & Related papers  (2023-04-12T17:33:28Z)
• ElitePLM: An Empirical Study on General Language Ability Evaluation of Pretrained Language Models [78.08792285698853]
  We present a large-scale empirical study on general language ability evaluation of pretrained language models (ElitePLM) Our empirical results demonstrate that: (1) PLMs with varying training objectives and strategies are good at different ability tests; (2) fine-tuning PLMs in downstream tasks is usually sensitive to the data size and distribution; and (3) PLMs have excellent transferability between similar tasks.
  arXiv  Detail & Related papers  (2022-05-03T14:18:10Z)
• Awakening Latent Grounding from Pretrained Language Models for Semantic Parsing [28.9192914266566]
  We show that pretrained language models (PLMs) can discover which token should be grounded to which concept. Our approach can awaken latent grounding which is understandable to human experts, even if not exposed to such labels during training.
  arXiv  Detail & Related papers  (2021-09-22T06:46:29Z)
• Writing Polishment with Simile: Task, Dataset and A Neural Approach [9.38000305423665]
  We propose a new task of Writing Polishment with Simile (WPS) to investigate whether machines are able to polish texts with similes as we human do. Our model firstly locates where the simile should happen, and then generates a location-specific simile. We also release a large-scale Chinese Simile dataset containing 5 million similes with context.
  arXiv  Detail & Related papers  (2020-12-15T06:39:54Z)
• oLMpics -- On what Language Model Pre-training Captures [84.60594612120173]
  We propose eight reasoning tasks, which require operations such as comparison, conjunction, and composition. A fundamental challenge is to understand whether the performance of a LM on a task should be attributed to the pre-trained representations or to the process of fine-tuning on the task data.
  arXiv  Detail & Related papers  (2019-12-31T12:11:35Z)

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.