Parallel Data Helps Neural Entity Coreference Resolution

• URL: http://arxiv.org/abs/2305.17709v1
• Date: Sun, 28 May 2023 12:30:23 GMT
• Title: Parallel Data Helps Neural Entity Coreference Resolution
• Authors: Gongbo Tang, Christian Hardmeier
• Abstract summary: We propose a model to exploit coreference knowledge from parallel data. In addition to the conventional modules learning coreference from annotations, we introduce an unsupervised module to capture cross-lingual coreference knowledge. Our proposed cross-lingual model achieves consistent improvements, up to 1.74 percentage points, on the OntoNotes 5.0 English dataset.
• Score: 1.0914300987810126
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Coreference resolution is the task of finding expressions that refer to the same entity in a text. Coreference models are generally trained on monolingual annotated data but annotating coreference is expensive and challenging. Hardmeier et al.(2013) have shown that parallel data contains latent anaphoric knowledge, but it has not been explored in end-to-end neural models yet. In this paper, we propose a simple yet effective model to exploit coreference knowledge from parallel data. In addition to the conventional modules learning coreference from annotations, we introduce an unsupervised module to capture cross-lingual coreference knowledge. Our proposed cross-lingual model achieves consistent improvements, up to 1.74 percentage points, on the OntoNotes 5.0 English dataset using 9 different synthetic parallel datasets. These experimental results confirm that parallel data can provide additional coreference knowledge which is beneficial to coreference resolution tasks.

Related papers

• A Morphologically-Aware Dictionary-based Data Augmentation Technique for Machine Translation of Under-Represented Languages [31.18983138590214]
  We propose strategies to synthesize parallel data relying on morpho-syntactic information and using bilingual lexicons. Our methodology adheres to a realistic scenario backed by the small parallel seed data. It is linguistically informed, as it aims to create augmented data that is more likely to be grammatically correct.
  arXiv  Detail & Related papers  (2024-02-02T22:25:44Z)
• Unified Model Learning for Various Neural Machine Translation [63.320005222549646]
  Existing machine translation (NMT) studies mainly focus on developing dataset-specific models. We propose a versatile'' model, i.e., the Unified Model Learning for NMT (UMLNMT) that works with data from different tasks. OurNMT results in substantial improvements over dataset-specific models with significantly reduced model deployment costs.
  arXiv  Detail & Related papers  (2023-05-04T12:21:52Z)
• Ensemble Transfer Learning for Multilingual Coreference Resolution [60.409789753164944]
  A problem that frequently occurs when working with a non-English language is the scarcity of annotated training data. We design a simple but effective ensemble-based framework that combines various transfer learning techniques. We also propose a low-cost TL method that bootstraps coreference resolution models by utilizing Wikipedia anchor texts.
  arXiv  Detail & Related papers  (2023-01-22T18:22:55Z)
• On the Role of Parallel Data in Cross-lingual Transfer Learning [30.737717433111776]
  We examine the usage of unsupervised machine translation to generate synthetic parallel data. We find that even model generated parallel data can be useful for downstream tasks. Our findings suggest that existing multilingual models do not exploit the full potential of monolingual data.
  arXiv  Detail & Related papers  (2022-12-20T11:23:04Z)
• An Empirical Investigation of Commonsense Self-Supervision with Knowledge Graphs [67.23285413610243]
  Self-supervision based on the information extracted from large knowledge graphs has been shown to improve the generalization of language models. We study the effect of knowledge sampling strategies and sizes that can be used to generate synthetic data for adapting language models.
  arXiv  Detail & Related papers  (2022-05-21T19:49:04Z)
• Bridging the Data Gap between Training and Inference for Unsupervised Neural Machine Translation [49.916963624249355]
  A UNMT model is trained on the pseudo parallel data with translated source, and natural source sentences in inference. The source discrepancy between training and inference hinders the translation performance of UNMT models. We propose an online self-training approach, which simultaneously uses the pseudo parallel data natural source, translated target to mimic the inference scenario.
  arXiv  Detail & Related papers  (2022-03-16T04:50:27Z)
• Tracing Origins: Coref-aware Machine Reading Comprehension [43.352833140317486]
  We imitated the human's reading process in connecting the anaphoric expressions and leverage the coreference information to enhance the word embeddings from the pre-trained model. We demonstrated that the explicit incorporation of the coreference information in fine-tuning stage performed better than the incorporation of the coreference information in training a pre-trained language models.
  arXiv  Detail & Related papers  (2021-10-15T09:28:35Z)
• Exploring Unsupervised Pretraining Objectives for Machine Translation [99.5441395624651]
  Unsupervised cross-lingual pretraining has achieved strong results in neural machine translation (NMT) Most approaches adapt masked-language modeling (MLM) to sequence-to-sequence architectures, by masking parts of the input and reconstructing them in the decoder. We compare masking with alternative objectives that produce inputs resembling real (full) sentences, by reordering and replacing words based on their context.
  arXiv  Detail & Related papers  (2021-06-10T10:18:23Z)
• Learning Contextualised Cross-lingual Word Embeddings and Alignments for Extremely Low-Resource Languages Using Parallel Corpora [63.5286019659504]
  We propose a new approach for learning contextualised cross-lingual word embeddings based on a small parallel corpus. Our method obtains word embeddings via an LSTM encoder-decoder model that simultaneously translates and reconstructs an input sentence.
  arXiv  Detail & Related papers  (2020-10-27T22:24:01Z)

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.