Streamlining Cross-Document Coreference Resolution: Evaluation and Modeling

• URL: http://arxiv.org/abs/2009.11032v3
• Date: Fri, 23 Oct 2020 13:40:30 GMT
• Title: Streamlining Cross-Document Coreference Resolution: Evaluation and Modeling
• Authors: Arie Cattan, Alon Eirew, Gabriel Stanovsky, Mandar Joshi, and Ido Dagan
• Abstract summary: Recent evaluation protocols for Cross-document (CD) coreference resolution have often been inconsistent or lenient. Our primary contribution is proposing a pragmatic evaluation methodology which assumes access to only raw text. Our model adapts and extends recent neural models for within-document coreference resolution to address the CD coreference setting.
• Score: 25.94435242086499
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent evaluation protocols for Cross-document (CD) coreference resolution have often been inconsistent or lenient, leading to incomparable results across works and overestimation of performance. To facilitate proper future research on this task, our primary contribution is proposing a pragmatic evaluation methodology which assumes access to only raw text -- rather than assuming gold mentions, disregards singleton prediction, and addresses typical targeted settings in CD coreference resolution. Aiming to set baseline results for future research that would follow our evaluation methodology, we build the first end-to-end model for this task. Our model adapts and extends recent neural models for within-document coreference resolution to address the CD coreference setting, which outperforms state-of-the-art results by a significant margin.

Related papers

• A Survey of Low-shot Vision-Language Model Adaptation via Representer Theorem [38.84662767814454]
  Key challenge to address under the condition of limited training data is how to fine-tune pre-trained vision-language models in a parameter-efficient manner. This paper proposes a unified computational framework to integrate existing methods together, identify their nature and support in-depth comparison. As a demonstration, we extend existing methods by modeling inter-class correlation between representers in reproducing kernel Hilbert space (RKHS)
  arXiv  Detail & Related papers  (2024-10-15T15:22:30Z)
• Deep Model Interpretation with Limited Data : A Coreset-based Approach [0.810304644344495]
  We propose a coreset-based interpretation framework that utilizes coreset selection methods to sample a representative subset of the large dataset for the interpretation task. We propose a similarity-based evaluation protocol to assess the robustness of model interpretation methods towards the amount data they take as input.
  arXiv  Detail & Related papers  (2024-10-01T09:07:24Z)
• Top-K Pairwise Ranking: Bridging the Gap Among Ranking-Based Measures for Multi-Label Classification [120.37051160567277]
  This paper proposes a novel measure named Top-K Pairwise Ranking (TKPR) A series of analyses show that TKPR is compatible with existing ranking-based measures. On the other hand, we establish a sharp generalization bound for the proposed framework based on a novel technique named data-dependent contraction.
  arXiv  Detail & Related papers  (2024-07-09T09:36:37Z)
• Coherent Entity Disambiguation via Modeling Topic and Categorical Dependency [87.16283281290053]
  Previous entity disambiguation (ED) methods adopt a discriminative paradigm, where prediction is made based on matching scores between mention context and candidate entities. We propose CoherentED, an ED system equipped with novel designs aimed at enhancing the coherence of entity predictions. We achieve new state-of-the-art results on popular ED benchmarks, with an average improvement of 1.3 F1 points.
  arXiv  Detail & Related papers  (2023-11-06T16:40:13Z)
• Investigating Crowdsourcing Protocols for Evaluating the Factual Consistency of Summaries [59.27273928454995]
  Current pre-trained models applied to summarization are prone to factual inconsistencies which misrepresent the source text or introduce extraneous information. We create a crowdsourcing evaluation framework for factual consistency using the rating-based Likert scale and ranking-based Best-Worst Scaling protocols. We find that ranking-based protocols offer a more reliable measure of summary quality across datasets, while the reliability of Likert ratings depends on the target dataset and the evaluation design.
  arXiv  Detail & Related papers  (2021-09-19T19:05:00Z)
• Realistic Evaluation Principles for Cross-document Coreference Resolution [19.95214898312209]
  We argue that models should not exploit the synthetic topic structure of the standard ECB+ dataset. We demonstrate empirically the drastic impact of our more realistic evaluation principles on a competitive model.
  arXiv  Detail & Related papers  (2021-06-08T09:05:21Z)
• Cross-document Coreference Resolution over Predicted Mentions [19.95214898312209]
  We introduce the first end-to-end model for CD coreference resolution from raw text. Our model achieves competitive results for event and entity coreference resolution on gold mentions.
  arXiv  Detail & Related papers  (2021-06-02T14:56:28Z)
• Reliable Evaluations for Natural Language Inference based on a Unified Cross-dataset Benchmark [54.782397511033345]
  Crowd-sourced Natural Language Inference (NLI) datasets may suffer from significant biases like annotation artifacts. We present a new unified cross-datasets benchmark with 14 NLI datasets and re-evaluate 9 widely-used neural network-based NLI models. Our proposed evaluation scheme and experimental baselines could provide a basis to inspire future reliable NLI research.
  arXiv  Detail & Related papers  (2020-10-15T11:50:12Z)
• Evaluating Text Coherence at Sentence and Paragraph Levels [17.99797111176988]
  We investigate the adaptation of existing sentence ordering methods to a paragraph ordering task. We also compare the learnability and robustness of existing models by artificially creating mini datasets and noisy datasets. We conclude that the recurrent graph neural network-based model is an optimal choice for coherence modeling.
  arXiv  Detail & Related papers  (2020-06-05T03:31:49Z)
• Pre-training Is (Almost) All You Need: An Application to Commonsense Reasoning [61.32992639292889]
  Fine-tuning of pre-trained transformer models has become the standard approach for solving common NLP tasks. We introduce a new scoring method that casts a plausibility ranking task in a full-text format. We show that our method provides a much more stable training phase across random restarts.
  arXiv  Detail & Related papers  (2020-04-29T10:54:40Z)
• Document Ranking with a Pretrained Sequence-to-Sequence Model [56.44269917346376]
  We show how a sequence-to-sequence model can be trained to generate relevance labels as "target words" Our approach significantly outperforms an encoder-only model in a data-poor regime.
  arXiv  Detail & Related papers  (2020-03-14T22:29:50Z)

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.