Papers by Hannah Chen
Balanced Adversarial Training: Balancing Tradeoffs between Fickleness and Obstinacy in NLP Models (2022.emnlp-main)
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| Challenge: | Traditional adversarial examples involve finding a small perturbation that does not change an input’s true label but confuses the classifier into outputting a different prediction. |
| Approach: | They propose to use contrastive learning to increase model robustness against fickle adversarial examples by reducing the vulnerability of adversarials to fickle ones. |
| Outcome: | The proposed method improves model robustness against fickle and obstinate adversarial examples. |
Finding Friends and Flipping Frenemies: Automatic Paraphrase Dataset Augmentation Using Graph Theory (2020.findings-emnlp)
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| Challenge: | Having high quality annotated data is crucial for training supervised machine learning models. |
| Approach: | They propose automated methods to improve NLP datasets by viewing them as graphs with expected semantic properties. |
| Outcome: | The proposed methods improve paraphrase models on pre-trained datasets. |
Social IQa: Commonsense Reasoning about Social Interactions (D19-1)
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| Challenge: | Social IQa contains 38,000 multiple choice questions for probing emotional and social intelligence in a variety of everyday situations. |
| Approach: | They propose a crowdsourcing framework that collects commonsense questions along with correct and incorrect answers about social interactions. |
| Outcome: | The proposed framework mitigates stylistic artifacts in incorrect answers by asking workers to provide the right answer to a different but related question. |
Prompt Optimization via Adversarial In-Context Learning (2024.acl-long)
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Do Long, Yiran Zhao, Hannah Brown, Yuxi Xie, James Zhao, Nancy Chen, Kenji Kawaguchi, Michael Shieh, Junxian He
| Challenge: | Existing methods to optimize prompts for in-context learning are based on adversarial learning and are computationally efficient and extensible to other LLMs and tasks. |
| Approach: | They propose a method to optimize prompts for in-context learning by a generator and a discriminator. |
| Outcome: | The proposed method improves state-of-the-art prompt optimization techniques on 13 generation and classification tasks including summarization, arithmetic reasoning, machine translation, data-to-text generation, and the MMLU and big-bench hard benchmarks. |
MultiMUC: Multilingual Template Filling on MUC-4 (2024.eacl-long)
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William Gantt, Shabnam Behzad, Hannah An, Yunmo Chen, Aaron White, Benjamin Van Durme, Mahsa Yarmohammadi
| Challenge: | We present multilingual parallel template filling datasets for MUCs . systems were required to extract one template per incident, containing details about perpetrators, victims, weapons used . |
| Approach: | They introduce MultiMUC, the first multilingual parallel corpus for template filling . they obtain automatic translations from a strong multilingual machine translation system . |
| Outcome: | The proposed dataset includes translations of the classic MUC-4 template filling benchmark into Arabic, Chinese, Farsi, Korean, and Russian. |
AIPOM: Agent-aware Interactive Planning for Multi-Agent Systems (2025.emnlp-demos)
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| Challenge: | Large language models (LLMs) are being used for planning in orchestrated multi-agent systems . existing LLMs fall short of human expectations and lack effective mechanisms for users to inspect, understand, and control their behaviors. |
| Approach: | They propose a system supporting human-in-the-loop planning through conversational and graph-based interfaces. |
| Outcome: | AIPOM enables users to transparently inspect, refine, and collaboratively guide LLM-generated plans, significantly enhancing user control and trust in multi-agent workflows. |
Addressing Both Statistical and Causal Gender Fairness in NLP Models (2024.findings-naacl)
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| Challenge: | Statistical fairness stipulates equivalent outcomes for all protected groups, whereas causal fairness prescribes that a model makes the same prediction for an individual regardless of their protected characteristics. |
| Approach: | They propose to use statistical and causal debiasing methods to reduce gender bias in NLP models. |
| Outcome: | The proposed methods reduce gender bias measured by the targeted metric, but not on other bias metrics. |
Pointwise Paraphrase Appraisal is Potentially Problematic (2020.acl-srw)
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| Challenge: | prevailing methods for paraphrase identification models are binary classification problems . current methods do not provide consistent and robust performance on unseen samples and real world problems. |
| Approach: | They propose to use binary classification to evaluate paraphrase identification models . they propose to improve methods for fine-tuning BERT models by pairing two sentences as one sequence . |
| Outcome: | The proposed methods may fail on simple tasks like identifying pairs with two identical sentences. |
FactPICO: Factuality Evaluation for Plain Language Summarization of Medical Evidence (2024.acl-long)
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Sebastian Joseph, Lily Chen, Jan Trienes, Hannah Göke, Monika Coers, Wei Xu, Byron Wallace, Junyi Jessy Li
| Challenge: | FactPICO is a factuality benchmark for plain language summarization of medical texts describing randomized controlled trials . existing metrics for factual summarizing medical evidence are poorly correlated with expert judgments on the instance level. |
| Approach: | They propose a factuality benchmark for plain language summarization of medical texts . they assess factuality of critical elements of RCTs in those summaries . |
| Outcome: | The proposed benchmark assesses the factuality of medical summaries using LLMs . the summary summators are based on 345 plain language summaires with fine-grained evaluation . |