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Chris Biemann

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3 papers

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ECAI Conference 2023 Conference Paper

Dimensions of Similarity: Towards Interpretable Dimension-Based Text Similarity

Hans Ole Hatzel
Fynn Petersen-Frey
Tim Fischer 0002
Chris Biemann

This paper paves the way for interpretable and configurable semantic similarity search, by training state-of-the-art models for identifying textual similarity guided by a set of aspects or dimensions. The similarity models are analyzed as to which interpretable dimensions of similarity they place the most emphasis on. We conceptually introduce configurable similarity search for finding documents similar in specific aspects but dissimilar in others. To evaluate the interpretability of these dimensions, we experiment with downstream retrieval tasks using weighted combinations of these dimensions. Configurable similarity search is an invaluable tool for exploring datasets and will certainly be helpful in many applied natural language processing research applications.

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ECAI Conference 2023 Conference Paper

Using Self-Supervised Dual Constraint Contrastive Learning for Cross-Modal Retrieval

Xintong Wang 0001
Xiaoyu Li
Liang Ding 0006
Sanyuan Zhao
Chris Biemann

In this work, we present a self-supervised dual constraint contrastive method for efficiently fine-tuning the vision-language pre-trained (VLP) models that have achieved great success on various cross-modal tasks, since full fine-tune these pre-trained models is computationally expensive and tend to result in catastrophic forgetting restricted by the size and quality of labeled datasets. Our approach freezes the pre-trained VLP models as the fundamental, generalized, and transferable multimodal representation and incorporates lightweight parameters to learn domain and task-specific features without labeled data. We demonstrated that our self-supervised dual contrastive model performs better than previous fine-tuning methods on MS COCO and Flickr 30K datasets on the cross-modal retrieval task, with an even more pronounced improvement in zero-shot performance. Furthermore, experiments on the MOTIF dataset prove that our self-supervised approach remains effective when trained on a small, out-of-domain dataset without overfitting. As a plug-and-play method, our proposed method is agnostic to the underlying models and can be easily integrated with different VLP models, allowing for the potential incorporation of future advancements in VLP models.

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AAAI Conference 2021 Conference Paper

HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection

Binny Mathew
Punyajoy Saha
Seid Muhie Yimam
Chris Biemann
Pawan Goyal
Animesh Mukherjee

Hate speech is a challenging issue plaguing the online social media. While better models for hate speech detection are continuously being developed, there is little research on the bias and interpretability aspects of hate speech. In this paper, we introduce HateXplain, the first benchmark hate speech dataset covering multiple aspects of the issue. Each post in our dataset is annotated from three different perspectives: the basic, commonly used 3-class classification (i. e. , hate, offensive or normal), the target community (i. e. , the community that has been the victim of hate speech/offensive speech in the post), and the rationales, i. e. , the portions of the post on which their labelling decision (as hate, offensive or normal) is based. We utilize existing state-of-the-art models and observe that even models that perform very well in classification do not score high on explainability metrics like model plausibility and faithfulness. We also observe that models, which utilize the human rationales for training, perform better in reducing unintended bias towards target communities. We have made our code and dataset public1 for other researchers2.

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