Papers by Wanzheng Zhu
GRUEN for Evaluating Linguistic Quality of Generated Text (2020.findings-emnlp)
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| Challenge: | Existing evaluation metrics focus on content selection, not linguistic quality . proposed GRUEN measures Grammaticality, non-redundancy, focUs, structure and coherence of generated text. |
| Approach: | They propose to use a BERT-based model and a class of syntactic, semantic, and contextual features to examine the system output. |
| Outcome: | Experiments show that the proposed metric correlates highly with human judgments. |
Euphemistic Phrase Detection by Masked Language Model (2021.findings-emnlp)
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| Challenge: | euphemisms are ordinary-sounding words with a secret meaning that are used to conceal information . a primary motive of their use on social media is to evade content moderation efforts . |
| Approach: | They propose to use social media to detect euphemisms without human effort . they first perform phrase mining on a raw text corpus to extract quality phrases . then they use word embedding similarities to select a set of euphoristic phrase candidates . |
| Outcome: | The proposed algorithm shows 20-50% higher detection accuracies than baselines. |
Generate, Prune, Select: A Pipeline for Counterspeech Generation against Online Hate Speech (2021.findings-acl)
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| Challenge: | Off-the-shelf methods to generate hate speech are limited in that they generate repetitive and safe responses regardless of the hate speech. |
| Approach: | They propose a three-module pipeline approach to generate diverse and relevant counterspeech . they first generate various counterspeak candidates by a generative model, then filter ungrammatical ones using a BERT model . |
| Outcome: | The proposed pipeline generates diverse and relevant counterspeech responses on three datasets. |