The current framework for sequence labeling encompasses a feature extractor and a sequence tagger. This study introduces a unified framework named SLGAN, which harnesses the capabilities of Generative Adversarial Networks to address the challenges associated with Sequence Labeling tasks. SLGAN not only mitigates the limitation of GANs in backpropagating loss to discrete data but also exhibits strong adaptability to various sequence labeling tasks. Unlike traditional GANs, the discriminator within SLGAN does not discriminate whether data originates from the discriminator or the generator; instead, it focuses on predicting the correctness of each tag within the tag sequence. We conducted evaluations on six different tasks spanning four languages, including Chinese, Japanese, and Korean Word Segmentation, Chinese and English Named Entity Recognition, and Chinese Part-of-Speech Tagging. Our experimental results illustrate that SLGAN represents a versatile and highly effective solution, consistently achieving state-of-the-art or competitive performance results, irrespective of the specific task or language under consideration.

When Generative Adversarial Networks Meet Sequence Labeling Challenges

Existing algorithms consider Word Segmentation as the sequence tagging task and the dominant tagging schemas are “BMES”, ”BIS”, “START-NONSTART”, and “SEP-APP”. For the existing tagging schemas, there are two issues:
1. The implicit information of position restricts the tag-to-tag transition. For the “BMES” tagging schema, tag “B” can not be transferred to tag “B” or “S”.
2. The first tag in the sentence must be “B”, “START” or “SEP”. 
Therefore, we propose the "Connection (C) – NoConnection (NC)" tagging schema to directly describe the connection state of every two adjacent characters. "C-NC" eliminates the implicit restriction of position for the existing tagging schemas.
Our Separation Inference framework is constructed on the "Connection (C) – NoConnection (NC)" tagging schema and its specially tailored bigram features. Our Separation Inference framework is evaluated on five public datasets, is demonstrated to work for machine learning and deep learning models, and outperforms state-of-the-art performance for CWS in all experiments. Performance boosts on Japanese Word Segmentation (JWS) and Korean Word Segmentation (KWS) further prove the framework is universal and effective for East Asian Languages.

Word Segmentation by Separation Inference for East Asian Languages

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## Welcome to EMNLP 2024! 
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Posters: Phonology, Morphology and Word Segmentation

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
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ACL 2022

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

Yu Tong

2

SHORT BIO

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When Generative Adversarial Networks Meet Sequence Labeling Challenges

Word Segmentation by Separation Inference for East Asian Languages

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