Haishuo Fang


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Transformers with Learnable Activation Functions
Haishuo Fang | Ji-Ung Lee | Nafise Sadat Moosavi | Iryna Gurevych
Findings of the Association for Computational Linguistics: EACL 2023

Activation functions can have a significant impact on reducing the topological complexity of input data and therefore, improving a model’s performance. However, the choice of activation functions is seldom discussed or explored in Transformer-based language models. As a common practice, commonly used activation functions like Gaussian Error Linear Unit (GELU) are chosen beforehand and then remain fixed from pre-training to fine-tuning. In this paper, we investigate the impact of activation functions on Transformer-based models by utilizing rational activation functions (RAFs). In contrast to fixed activation functions (FAF), RAFs are capable of learning the optimal activation functions from data. Our experiments show that the RAF-based Transformer model (RAFT) achieves a better performance than its FAF-based counterpart (). For instance, we find that RAFT outperforms on the GLUE benchmark by 5.71 points when using only 100 training examples and by 2.05 points on SQuAD with all available data. Analyzing the shapes of the learned RAFs further unveils that they vary across different layers and different tasks; opening a promising way to better analyze and understand large, pre-trained language models.

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UKP-SQuARE: An Interactive Tool for Teaching Question Answering
Haishuo Fang | Haritz Puerto | Iryna Gurevych
Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)

The exponential growth of question answering (QA) has made it an indispensable topic in any Natural Language Processing (NLP) course. Additionally, the breadth of QA derived from this exponential growth makes it an ideal scenario for teaching related NLP topics such as information retrieval, explainability, and adversarial attacks among others. In this paper, we introduce UKP-SQuARE as a platform for QA education. This platform provides an interactive environment where students can run, compare, and analyze various QA models from different perspectives, such as general behavior, explainability, and robustness. Therefore, students can get a first-hand experience in different QA techniques during the class. Thanks to this, we propose a learner-centered approach for QA education in which students proactively learn theoretical concepts and acquire problem-solving skills through interactive exploration, experimentation, and practical assignments, rather than solely relying on traditional lectures. To evaluate the effectiveness of UKP-SQuARE in teaching scenarios, we adopted it in a postgraduate NLP course and surveyed the students after the course. Their positive feedback shows the platform’s effectiveness in their course and invites a wider adoption.

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UKP-SQuARE v3: A Platform for Multi-Agent QA Research
Haritz Puerto | Tim Baumgärtner | Rachneet Sachdeva | Haishuo Fang | Hao Zhang | Sewin Tariverdian | Kexin Wang | Iryna Gurevych
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)

The continuous development of Question Answering (QA) datasets has drawn the research community’s attention toward multi-domain models. A popular approach is to use multi-dataset models, which are models trained on multiple datasets to learn their regularities and prevent overfitting to a single dataset. However, with the proliferation of QA models in online repositories such as GitHub or Hugging Face, an alternative is becoming viable. Recent works have demonstrated that combining expert agents can yield large performance gains over multi-dataset models. To ease research in multi-agent models, we extend UKP-SQuARE, an online platform for QA research, to support three families of multi-agent systems: i) agent selection, ii) early-fusion of agents, and iii) late-fusion of agents. We conduct experiments to evaluate their inference speed and discuss the performance vs. speed trade-off compared to multi-dataset models. UKP-SQuARE is open-source and publicly available.