Comparative analysis of word embeddings in assessing semantic similarity of complex sentences

• URL: http://arxiv.org/abs/2010.12637v3
• Date: Fri, 9 Jul 2021 21:15:24 GMT
• Title: Comparative analysis of word embeddings in assessing semantic similarity of complex sentences
• Authors: Dhivya Chandrasekaran and Vijay Mago
• Abstract summary: We study the sentences in existing benchmark datasets and analyze the sensitivity of various word embeddings with respect to the complexity of the sentences. The results show the increase in complexity of the sentences has a significant impact on the performance of the embedding models.
• Score: 8.873705500708196
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semantic textual similarity is one of the open research challenges in the field of Natural Language Processing. Extensive research has been carried out in this field and near-perfect results are achieved by recent transformer-based models in existing benchmark datasets like the STS dataset and the SICK dataset. In this paper, we study the sentences in these datasets and analyze the sensitivity of various word embeddings with respect to the complexity of the sentences. We build a complex sentences dataset comprising of 50 sentence pairs with associated semantic similarity values provided by 15 human annotators. Readability analysis is performed to highlight the increase in complexity of the sentences in the existing benchmark datasets and those in the proposed dataset. Further, we perform a comparative analysis of the performance of various word embeddings and language models on the existing benchmark datasets and the proposed dataset. The results show the increase in complexity of the sentences has a significant impact on the performance of the embedding models resulting in a 10-20% decrease in Pearson's and Spearman's correlation.

Related papers

• Towards Enhancing Coherence in Extractive Summarization: Dataset and Experiments with LLMs [70.15262704746378]
  We propose a systematically created human-annotated dataset consisting of coherent summaries for five publicly available datasets and natural language user feedback. Preliminary experiments with Falcon-40B and Llama-2-13B show significant performance improvements (10% Rouge-L) in terms of producing coherent summaries.
  arXiv  Detail & Related papers  (2024-07-05T20:25:04Z)
• How Well Do Text Embedding Models Understand Syntax? [50.440590035493074]
  The ability of text embedding models to generalize across a wide range of syntactic contexts remains under-explored. Our findings reveal that existing text embedding models have not sufficiently addressed these syntactic understanding challenges. We propose strategies to augment the generalization ability of text embedding models in diverse syntactic scenarios.
  arXiv  Detail & Related papers  (2023-11-14T08:51:00Z)
• Importance of Synthesizing High-quality Data for Text-to-SQL Parsing [71.02856634369174]
  State-of-the-art text-to-weighted algorithms did not further improve on popular benchmarks when trained with augmented synthetic data. We propose a novel framework that incorporates key relationships from schema, imposes strong typing, and schema-weighted column sampling.
  arXiv  Detail & Related papers  (2022-12-17T02:53:21Z)
• Domain Adaptation in Multilingual and Multi-Domain Monolingual Settings for Complex Word Identification [0.27998963147546146]
  Complex word identification (CWI) is a cornerstone process towards proper text simplification. CWI is highly dependent on context, whereas its difficulty is augmented by the scarcity of available datasets. We propose a novel training technique for the CWI task based on domain adaptation to improve the target character and context representations.
  arXiv  Detail & Related papers  (2022-05-15T13:21:02Z)
• Structurally Diverse Sampling Reduces Spurious Correlations in Semantic Parsing Datasets [51.095144091781734]
  We propose a novel algorithm for sampling a structurally diverse set of instances from a labeled instance pool with structured outputs. We show that our algorithm performs competitively with or better than prior algorithms in not only compositional template splits but also traditional IID splits. In general, we find that diverse train sets lead to better generalization than random training sets of the same size in 9 out of 10 dataset-split pairs.
  arXiv  Detail & Related papers  (2022-03-16T07:41:27Z)
• What Makes Sentences Semantically Related: A Textual Relatedness Dataset and Empirical Study [31.062129406113588]
  We introduce a dataset for Semantic Textual Relatedness, STR-2022, that has 5,500 English sentence pairs manually annotated. We show that human intuition regarding relatedness of sentence pairs is highly reliable, with a repeat annotation correlation of 0.84. We also show the utility of STR-2022 for evaluating automatic methods of sentence representation and for various downstream NLP tasks.
  arXiv  Detail & Related papers  (2021-10-10T16:23:54Z)
• Text-to-SQL in the Wild: A Naturally-Occurring Dataset Based on Stack Exchange Data [3.06261471569622]
  SEDE is a dataset with 12,023 pairs of utterances andsql queries collected from real usage on the Stack Exchange website. We show that these pairs contain a variety of real-world challenges which were rarely reflected so far in any other semantic parsing dataset.
  arXiv  Detail & Related papers  (2021-06-09T12:09:51Z)
• A Comparative Study on Structural and Semantic Properties of Sentence Embeddings [77.34726150561087]
  We propose a set of experiments using a widely-used large-scale data set for relation extraction. We show that different embedding spaces have different degrees of strength for the structural and semantic properties. These results provide useful information for developing embedding-based relation extraction methods.
  arXiv  Detail & Related papers  (2020-09-23T15:45:32Z)
• Semantic Complexity in End-to-End Spoken Language Understanding [20.184305170102082]
  We analyze the relationship between the performance of STI models and the difficulty of the use case to which they are applied. We show that near-perfect performance metrics for STI models reported in the literature were obtained with datasets with low semantic complexity values.
  arXiv  Detail & Related papers  (2020-08-06T20:18:53Z)
• CLARITY -- Comparing heterogeneous data using dissimiLARITY [0.39146761527401414]
  Many scientific questions can be phrased around whether the (dis)similarities between entities are conserved across such different data. Our method, CLARITY, quantifies consistency across datasets, identifies where inconsistencies arise, and aids in their interpretation.
  arXiv  Detail & Related papers  (2020-05-29T20:56:32Z)
• Extractive Summarization as Text Matching [123.09816729675838]
  This paper creates a paradigm shift with regard to the way we build neural extractive summarization systems. We formulate the extractive summarization task as a semantic text matching problem. We have driven the state-of-the-art extractive result on CNN/DailyMail to a new level (44.41 in ROUGE-1)
  arXiv  Detail & Related papers  (2020-04-19T08:27:57Z)

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.