Zero-Resource Cross-Lingual Named Entity Recognition

Bari, Mehwish; Joty, Shafiq; Jwalapuram, Prathyusha

doi:10.1609/aaai.v34i05.6237

Cited by 35 publications

(34 citation statements)

References 19 publications

(29 reference statements)

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“…Experimental results on three datasets show that our proposed approach sets a new state-of-the-art result in various evaluation metrics. In the future, we will apply our models on more tasks, such as information retrieval applications (Huang and Hu, 2009;Huang et al, 2003;Yin et al, 2013;Huang et al, 2005), sentiment analysis (Liu et al, 2007;Yu et al, 2012), learning from imbalanced or unlabeled datasets (Liu et al, 2006;Bari et al, 2019;Bari et al, 2020), and automatic chart question answering (Kim et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

WSL-DS: Weakly Supervised Learning with Distant Supervision for Query Focused Multi-Document Abstractive Summarization

Laskar¹,

Hoque²,

Huang³

2020

Proceedings of the 28th International Conference on Computational Linguistics

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In the Query Focused Multi-Document Summarization (QF-MDS) task, a set of documents and a query are given where the goal is to generate a summary from these documents based on the given query. However, one major challenge for this task is the lack of availability of labeled training datasets. To overcome this issue, in this paper, we propose a novel weakly supervised learning approach via utilizing distant supervision. In particular, we use datasets similar to the target dataset as the training data where we leverage pre-trained sentence similarity models to generate the weak reference summary of each individual document in a document set from the multidocument gold reference summaries. Then, we iteratively train our summarization model on each single-document to alleviate the computational complexity issue that occurs while training neural summarization models in multiple documents (i.e., long sequences) at once. Experimental results on the Document Understanding Conferences (DUC) datasets show that our proposed approach sets a new state-of-the-art result in terms of various evaluation metrics.

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Section: Discussionmentioning

confidence: 99%

WSL-DS: Weakly Supervised Learning with Distant Supervision for Query Focused Multi-Document Abstractive Summarization

Laskar¹,

Hoque²,

Huang³

2020

Proceedings of the 28th International Conference on Computational Linguistics

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“…Following most existing works (Lample et al, 2016;Lin and Lu, 2018;Bari et al, 2019), Word Processor is proposed to achieve basic understandings over the words in the entity mentions. Given an input entity mention X w = (t 1 , ..., t K ) with K tokens, each token t k is represented as [w k ; c k ].…”

Section: Word Processormentioning

confidence: 99%

MZET: Memory Augmented Zero-Shot Fine-grained Named Entity Typing

Zhang

Xia

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics

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Named entity typing (NET) is a classification task of assigning an entity mention in the context with given semantic types. However, with the growing size and granularity of the entity types, few previous researches concern with newly emerged entity types. In this paper, we propose MZET, a novel memory augmented FNET (Fine-grained NET) model, to tackle the unseen types in a zero-shot manner. MZET incorporates character-level, word-level, and contextural-level information to learn the entity mention representation. Besides, MZET considers the semantic meaning and the hierarchical structure into the entity type representation. Finally, through the memory component which models the relationship between the entity mention and the entity type, MZET transfers the knowledge from seen entity types to the zero-shot ones. Extensive experiments on three public datasets show the superior performance obtained by MZET, which surpasses the state-of-the-art FNET neural network models with up to 8% gain in Micro-F1 and Macro-F1 score.

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“…In existing studies, researchers have begun to improve low-resource word representations via knowledge transfer from high-resource languages using bilingual lexicons [3]. Bari [4] proposed an unsupervised cross-lingual NER model that can transfer NER knowledge from one language to another in a completely unsupervised way without relying on any bilingual dictionary or parallel data. Xie [5] proposed a method that finds translations based on bilingual word embeddings which use the unsupervised transfer of natural language processing models in named entity recognition.…”

Section: Introductionmentioning

confidence: 99%

Low-Resource Named Entity Recognition via the Pre-Training Model

Chen

Pei

et al. 2021

Symmetry

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Named entity recognition (NER) is an important task in the processing of natural language, which needs to determine entity boundaries and classify them into pre-defined categories. For low-resource languages, most state-of-the-art systems require tens of thousands of annotated sentences to obtain high performance. However, there is minimal annotated data available about Uyghur and Hungarian (UH languages) NER tasks. There are also specificities in each task—differences in words and word order across languages make it a challenging problem. In this paper, we present an effective solution to providing a meaningful and easy-to-use feature extractor for named entity recognition tasks: fine-tuning the pre-trained language model. Therefore, we propose a fine-tuning method for a low-resource language model, which constructs a fine-tuning dataset through data augmentation; then the dataset of a high-resource language is added; and finally the cross-language pre-trained model is fine-tuned on this dataset. In addition, we propose an attention-based fine-tuning strategy that uses symmetry to better select relevant semantic and syntactic information from pre-trained language models and apply these symmetry features to name entity recognition tasks. We evaluated our approach on Uyghur and Hungarian datasets, which showed wonderful performance compared to some strong baselines. We close with an overview of the available resources for named entity recognition and some of the open research questions.

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Zero-Resource Cross-Lingual Named Entity Recognition

Cited by 35 publications

References 19 publications

WSL-DS: Weakly Supervised Learning with Distant Supervision for Query Focused Multi-Document Abstractive Summarization

WSL-DS: Weakly Supervised Learning with Distant Supervision for Query Focused Multi-Document Abstractive Summarization

MZET: Memory Augmented Zero-Shot Fine-grained Named Entity Typing

Low-Resource Named Entity Recognition via the Pre-Training Model

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