SQUARE: Automatic Question Answering Evaluation using Multiple Positive and Negative References

• URL: http://arxiv.org/abs/2309.12250v1
• Date: Thu, 21 Sep 2023 16:51:30 GMT
• Title: SQUARE: Automatic Question Answering Evaluation using Multiple Positive and Negative References
• Authors: Matteo Gabburo, Siddhant Garg, Rik Koncel Kedziorski, Alessandro Moschitti
• Abstract summary: We propose a new evaluation metric: SQuArE (Sentence-level QUestion AnsweRing Evaluation) We evaluate SQuArE on both sentence-level extractive (Answer Selection) and generative (GenQA) QA systems.
• Score: 73.67707138779245
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Evaluation of QA systems is very challenging and expensive, with the most reliable approach being human annotations of correctness of answers for questions. Recent works (AVA, BEM) have shown that transformer LM encoder based similarity metrics transfer well for QA evaluation, but they are limited by the usage of a single correct reference answer. We propose a new evaluation metric: SQuArE (Sentence-level QUestion AnsweRing Evaluation), using multiple reference answers (combining multiple correct and incorrect references) for sentence-form QA. We evaluate SQuArE on both sentence-level extractive (Answer Selection) and generative (GenQA) QA systems, across multiple academic and industrial datasets, and show that it outperforms previous baselines and obtains the highest correlation with human annotations.

Related papers

• PEDANTS (Precise Evaluations of Diverse Answer Nominee Text for Skinflints): Efficient Evaluation Analysis and Benchmarking for Open-Domain Question Answering [10.367359022491181]
  We provide guidelines and datasets for evaluating machine QA adopted from human QA community. We also propose an efficient, low-resource, and interpretable QA evaluation method more stable than an exact match and neural methods.
  arXiv  Detail & Related papers  (2024-02-17T01:56:19Z)
• An Empirical Comparison of LM-based Question and Answer Generation Methods [79.31199020420827]
  Question and answer generation (QAG) consists of generating a set of question-answer pairs given a context. In this paper, we establish baselines with three different QAG methodologies that leverage sequence-to-sequence language model (LM) fine-tuning. Experiments show that an end-to-end QAG model, which is computationally light at both training and inference times, is generally robust and outperforms other more convoluted approaches.
  arXiv  Detail & Related papers  (2023-05-26T14:59:53Z)
• RQUGE: Reference-Free Metric for Evaluating Question Generation by Answering the Question [29.18544401904503]
  We propose a new metric, RQUGE, based on the answerability of the candidate question given the context. We demonstrate that RQUGE has a higher correlation with human judgment without relying on the reference question.
  arXiv  Detail & Related papers  (2022-11-02T21:10:09Z)
• Improving Unsupervised Question Answering via Summarization-Informed Question Generation [47.96911338198302]
  Question Generation (QG) is the task of generating a plausible question for a passage, answer> pair. We make use of freely available news summary data, transforming declarative sentences into appropriate questions using dependency parsing, named entity recognition and semantic role labeling. The resulting questions are then combined with the original news articles to train an end-to-end neural QG model.
  arXiv  Detail & Related papers  (2021-09-16T13:08:43Z)
• Generating Diverse and Consistent QA pairs from Contexts with Information-Maximizing Hierarchical Conditional VAEs [62.71505254770827]
  We propose a conditional variational autoencoder (HCVAE) for generating QA pairs given unstructured texts as contexts. Our model obtains impressive performance gains over all baselines on both tasks, using only a fraction of data for training.
  arXiv  Detail & Related papers  (2020-05-28T08:26:06Z)
• KPQA: A Metric for Generative Question Answering Using Keyphrase Weights [64.54593491919248]
  KPQA-metric is a new metric for evaluating correctness of generative question answering systems. Our new metric assigns different weights to each token via keyphrase prediction. We show that our proposed metric has a significantly higher correlation with human judgments than existing metrics.
  arXiv  Detail & Related papers  (2020-05-01T03:24:36Z)
• Asking and Answering Questions to Evaluate the Factual Consistency of Summaries [80.65186293015135]
  We propose an automatic evaluation protocol called QAGS (pronounced "kags") to identify factual inconsistencies in a generated summary. QAGS is based on the intuition that if we ask questions about a summary and its source, we will receive similar answers if the summary is factually consistent with the source. We believe QAGS is a promising tool in automatically generating usable and factually consistent text.
  arXiv  Detail & Related papers  (2020-04-08T20:01:09Z)

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.