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Research on overlapped and discontinuous named entity recognition (NER) has received increasing attention.
The majority of previous work focuses on either overlapped or discontinuous entities.
In this paper, we propose a novel span-based model that can recognize both overlapped and discontinuous entities jointly.
The model includes two major steps.
First, entity fragments are recognized by traversing over all possible text spans,
thus, overlapped entities can be recognized.
Second, we perform relation classification to judge whether a given pair of entity fragments to be overlapping or succession.
In this way, we can recognize not only discontinuous entities, and meanwhile doubly check the overlapped entities.
As a whole, our model can be regarded as a relation extraction paradigm essentially.
Experimental results on multiple benchmark datasets (i.e., CLEF, GENIA and ACE05) show that our model is highly competitive for overlapped and discontinuous NER.

ACL-IJCNLP 2021

A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition

span-based

discontinuous

overlapped

named entity recognition

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technical paper

**Welcome to ACL-IJCNLP 2021!**

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The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

The choice of token vocabulary affects the performance of machine translation. 
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Vocabulary Learning via Optimal Transport for Neural Machine Translation

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De-biasing Distantly Supervised Named Entity Recognition via Causal Intervention

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LEWIS: Levenshtein Editing for Unsupervised Text Style Transfer

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Cluster-Former: Clustering-based Sparse Transformer for Question Answering

With the increasing interest in low-resource languages, unsupervised morphological segmentation has become an active area of research, where approaches based on Adaptor Grammars achieve state-of-the-art results. We demonstrate the power of harnessing linguistic knowledge as priors within Adaptor Grammars in a minimally-supervised learning fashion. 
We introduce two types of priors: 1) grammar definition, where we design language-specific grammars; and 2) linguist-provided affixes, collected by an expert in the language 
and seeded into the grammars. We use Japanese and Georgian as respective case studies for the two types of priors and introduce new datasets for these languages, with gold morphological segmentation for evaluation. We show that the use of priors results in error reductions of 8.9% and 34.2%, respectively, over the equivalent state-of-the-art unsupervised system.

Minimally-Supervised Morphological Segmentation using Adaptor Grammars with Linguistic Priors

Recent work in cross-topic argument mining attempts to learn models that generalize across topics rather than merely relying on within-topic spurious correlations. We examine the effectiveness of this approach by analyzing the output of single-task and multi-task models for cross-topic argument mining through a combination of linear approximations of their decision boundaries, manual feature grouping, challenge examples, and ablations across the input vocabulary. Surprisingly, we show that cross-topic models still rely mostly on spurious correlations and only generalize within closely related topics, e.g., a model trained only on closed-class words and a few common open-class words outperforms a state-of-the-art cross-topic model on distant target topics.

Spurious Correlations in Cross-Topic Argument Mining

We introduce a new approach for smoothing and improving the quality of word embeddings. We consider a method of fusing word embeddings that were trained on the same corpus but with different initializations. We project all the models to a shared vector space using an efficient implementation of the Generalized Procrustes Analysis (GPA) procedure, previously used in multilingual word translation. Our word representation demonstrates consistent improvements over the raw models as well as their simplistic average, on a range of tasks. As the new representations are more stable and reliable, there is a noticeable improvement in rare word evaluations.

Denoising Word Embeddings by Averaging in a Shared Space

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Vocabulary Learning via Optimal Transport for Neural Machine Translation

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T2-NER: A Two-Stage Span-based Framework For Unified Named Entity Recognition with Templates | VIDEO

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Vocabulary Learning via Optimal Transport for Neural Machine Translation

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