Papers › Transferring from Formal Newswire Domain with Hypernet for Twitter POS Tagging

Transferring from Formal Newswire Domain with Hypernet for Twitter POS Tagging

1 Oct 2018EMNLP 2018 10archive 2025-07-28

Tao Gui, Qi Zhang, Jingjing Gong, Minlong Peng, Di Liang, Keyu Ding, Xuanjing Huang

Part-of-Speech (POS) tagging for Twitter has received considerable attention in recent years. Because most POS tagging methods are based on supervised models, they usually require a large amount of labeled data for training. However, the existing labeled datasets for Twitter are much smaller than those for newswire text. Hence, to help POS tagging for Twitter, most domain adaptation methods try to leverage newswire datasets by learning the shared features between the two domains. However, from a linguistic perspective, Twitter users not only tend to mimic the formal expressions of traditional media, like news, but they also appear to be developing linguistically informal styles. Therefore, POS tagging for the formal Twitter context can be learned together with the newswire dataset, while POS tagging for the informal Twitter context should be learned separately. To achieve this task, in this work, we propose a hypernetwork-based method to generate different parameters to separately model contexts with different expression styles. Experimental results on three different datasets show that our approach achieves better performance than state-of-the-art methods in most cases.

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Tasks

Domain Adaptation Multi-Task Learning POS POS Tagging Part-Of-Speech Tagging Stock Prediction

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Part-Of-Speech Tagging	ARK	Gui et al., 2018	Acc	92.4	#3 of 3	Archive leaderboard	report
Part-Of-Speech Tagging	Ritter	Gui et al., 2018	Acc	91.2	#2 of 4	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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