Papers by John Alex
Learning to Prioritize: Precision-Driven Sentence Filtering for Long Text Summarization (2022.lrec-1)
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| Challenge: | Neural text summarization models are limited by their maximum input length, posing a challenge to summarizing longer texts comprehensively. |
| Approach: | They propose a pre-processing layer that removes low-quality sentences in articles to improve existing summarization models. |
| Outcome: | The proposed approach improves state-of-the-art summarization models on WikiHow and Reddit TIFU datasets by 3.84 and 8.57 points on the full test set and the long article subset. |
Mitigating Covertly Unsafe Text within Natural Language Systems (2022.findings-emnlp)
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Alex Mei, Anisha Kabir, Sharon Levy, Melanie Subbiah, Emily Allaway, John Judge, Desmond Patton, Bruce Bimber, Kathleen McKeown, William Yang Wang
| Challenge: | Existing studies on text safety have focused on overtly unsafe, covertly, or indirectly unsafe statements. |
| Approach: | They propose a method to identify physical harm-causing statements as overtly, covertly or indirectly unsafe and a solution to mitigate the generation of such statements. |
| Outcome: | The proposed methods identify the type of unsafe language that can cause physical harm and identify mitigation strategies to inspire future researchers to tackle this challenging problem. |
Learning To Split and Rephrase From Wikipedia Edit History (D18-1)
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| Challenge: | Performing split and rephrase tasks is one of the main operations in text simplification, alongside paraphrasing and dropping less salient content. |
| Approach: | They propose to use Wikipedia's edit history to extract a rich new dataset for the task. |
| Outcome: | The proposed model scores 32 BLEU points above the previous best on the WebSplit benchmark. |
Entailed Between the Lines: Incorporating Implication into NLI (2025.acl-long)
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Shreya Havaldar, Hamidreza Alvari, John Palowitch, Mohammad Javad Hosseini, Senaka Buthpitiya, Alex Fabrikant
| Challenge: | True Emotions, social cues, insults, and a myriad of other messages are conveyed implicitly, often even more so than explicitly. |
| Approach: | They propose a dataset to help LLMs understand implied entailment . |
| Outcome: | The proposed dataset enables LLMs to understand implied entailment and can generalize this understanding across datasets and domains. |