Papers by Huiyuan Lai
Responsibility Perspective Transfer for Italian Femicide News (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing work has shown that different descriptions of gender-based violence influence the reader’s perception of who is to blame for the violence. |
| Approach: | They propose to automatically rewrite GBV descriptions to alter the perceived level of blame on the perpetrator. |
| Outcome: | The proposed task alters perceived responsibility levels for perpetrators by using unsupervised, zero-shot and few-shot methods. |
Multilingual Pre-training with Language and Task Adaptation for Multilingual Text Style Transfer (2022.acl-short)
Copied to clipboard
| Challenge: | Text style transfer is a text generation task where a given sentence must be rewritten changing its style while preserving its meaning. |
| Approach: | They propose a modular approach for multilingual formality transfer using machine translated data and gold aligned English sentences. |
| Outcome: | The proposed approach achieves competitive performance without monolingual task-specific parallel data and can be applied to other style transfer tasks as well as to other languages. |
Thank you BART! Rewarding Pre-Trained Models Improves Formality Style Transfer (2021.acl-short)
Copied to clipboard
| Challenge: | Formality style transfer models have limited success in preserving content due to the scarcity of parallel data. |
| Approach: | They propose to fine-tune pre-trained language and sequence-to-sequence models with rewards that target style and content to enhance content preservation. |
| Outcome: | The proposed models can be fine-tuned with rewards that target style and content, and achieve good performance even with limited amounts of parallel data. |
Generic resources are what you need: Style transfer tasks without task-specific parallel training data (2021.emnlp-main)
Copied to clipboard
| Challenge: | Text style transfer is a task aimed at converting a text of one style into another while preserving its content. |
| Approach: | They propose a multi-step procedure which builds on a generic pre-trained sequence-to-sequence model and an iterative back-translation approach to train two models in a transfer direction. |
| Outcome: | The proposed method outperforms existing unsupervised approaches on the two most popular style transfer tasks: formality transfer and polarity swap. |
Fine-tuning with HED-IT: The impact of human post-editing for dialogical language models (2024.findings-acl)
Copied to clipboard
Daniela Occhipinti, Michele Marchi, Irene Mondella, Huiyuan Lai, Felice Dell’Orletta, Malvina Nissim, Marco Guerini
| Challenge: | a recent study has focused on the quality of data generated by automatic methods for fine-tuning Language Models in languages less resourced than English. |
| Approach: | They investigate whether human intervention improves the quality of machine-generated dialogues . they use a large-scale dataset to fine-tune three different sizes of an LM . |
| Outcome: | The results show that human intervention can improve the quality of training data . larger models are less sensitive to data quality, while smaller models are more sensitive . |
Multi-perspective Alignment for Increasing Naturalness in Neural Machine Translation (2025.acl-long)
Copied to clipboard
| Challenge: | Neural machine translation systems amplify lexical biases, rendering outputs artificially impoverished . Attempts to increase naturalness in NMT can fall short in terms of content preservation . |
| Approach: | They propose a method that rewards both naturalness and content preservation . they use multiple perspectives to produce more natural translations . |
| Outcome: | The proposed method produces translations that are lexically richer and exhibit more properties of human-written language without loss in translation accuracy. |
mCoT: Multilingual Instruction Tuning for Reasoning Consistency in Language Models (2024.acl-long)
Copied to clipboard
| Challenge: | Existing models show low performance for lesser resourced languages, but they can achieve surprising performance on complex reasoning tasks in natural language processing (NLP). |
| Approach: | They compile the first large-scale multilingual math reasoning dataset, *mCoT-MATH*, covering eleven diverse languages. |
| Outcome: | The proposed model achieves impressive consistency across languages and comparable performance to close- and open-source models even of much larger sizes. |
Multilingual Multi-Figurative Language Detection (2023.findings-acl)
Copied to clipboard
| Challenge: | Figures of speech help people express abstract concepts and emotions, but it's understudied in a multilingual setting and when considering more than one figure of speech at the same time. |
| Approach: | They propose a framework for sentence-level figurative language detection based on template-based prompt learning and use it to unify multiple detection tasks that are interrelated across multiple figures of speech and languages. |
| Outcome: | The proposed framework outperforms baselines and may serve as blueprint for the joint modelling of other interrelated tasks. |
Pre-Trained Language-Meaning Models for Multilingual Parsing and Generation (2023.findings-acl)
Copied to clipboard
| Challenge: | Pre-trained language models (PLMs) have been used for tasks in computational semantics but meaning representations are not included in PLMs. |
| Approach: | They propose to include meaning representations besides natural language texts in the same model . they propose to use DRSs to improve performance of non-English tasks . |
| Outcome: | The proposed approach achieves the best performance on multilingual parsing and DRS-to-text generation tasks. |
Multi-Figurative Language Generation (2022.coling-1)
Copied to clipboard
| Challenge: | Figurative language generation is the task of reformulating a given text in the desired figure of speech while still being faithful to the original context. |
| Approach: | They propose a scheme for multi-figurative language pre-training on top of BART and a mechanism for injecting the target figurative information into the encoder to generate text with the target figure from another figurativ form without parallel figura-figura pairs. |
| Outcome: | The proposed model outperforms all baselines and qualitatively examines the relationship between the different figures of speech. |