Papers by Peter Chen
A Cross-lingual Messenger with Keyword Searchable Phrases for the Travel Domain (C18-2)
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| Challenge: | Query Translator is a cross-lingual messaging app for the travel domain that automatically translates conversations . the application addresses common cross-linguistic communication issues such as translation accuracy, speed, privacy and personalization. |
| Approach: | They present a cross-lingual messaging app that automatically translates conversations while supporting keyword-to-sentence matching. |
| Outcome: | The proposed app translates conversations while supporting keyword-to-sentence matching. |
BIG-Bench Extra Hard (2025.acl-long)
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Mehran Kazemi, Bahare Fatemi, Hritik Bansal, John Palowitch, Chrysovalantis Anastasiou, Sanket Vaibhav Mehta, Lalit K Jain, Virginia Aglietti, Disha Jindal, Peter Chen, Nishanth Dikkala, Gladys Tyen, Xin Liu, Uri Shalit, Silvia Chiappa, Kate Olszewska, Yi Tay, Vinh Q. Tran, Quoc V Le, Orhan Firat
| Challenge: | Current benchmarks for large language model reasoning focus on math and coding abilities, leaving a gap in evaluating broader reasoning proficiencies. |
| Approach: | They propose a benchmark to evaluate general reasoning in large language models . they use BIG-Bench and its harder version BIG-Benefit Hard to assess general reasoning . |
| Outcome: | The new benchmark pushes the boundaries of LLM reasoning evaluation. |
Spoken Document Retrieval for an Unwritten Language: A Case Study on Gormati (2025.findings-emnlp)
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Sanjay Booshanam, Kelly Chen, Ondrej Klejch, Thomas Reitmaier, Dani Kalarikalayil Raju, Electra Wallington, Nina Markl, Jennifer Pearson, Matt Jones, Simon Robinson, Peter Bell
| Challenge: | Speakers of unwritten languages have the potential to benefit from speech-based automatic information retrieval systems. |
| Approach: | They propose a speech embedding technique that facilitates a zero-shot speech-based automatic information retrieval system for unwritten languages. |
| Outcome: | The proposed method achieves a Top 5 retrieval rate of 87.9% on a corpus of Gormati, an unwritten language, that was collected in partnership with an agrarian Banjara community in Maharashtra State, India. |
A Fully Generative Motivational Interviewing Counsellor Chatbot for Moving Smokers Towards the Decision to Quit (2025.findings-acl)
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Zafarullah Mahmood, Soliman Ali, Jiading Zhu, Mohamed Abdelwahab, Michelle Yu Collins, Sihan Chen, Yi Cheng Zhao, Jodi Wolff, Osnat C. Melamed, Nadia Minian, Marta Maslej, Carolynne Cooper, Matt Ratto, Peter Selby, Jonathan Rose
| Challenge: | Large language models (LLMs) are being used to provide automated talk therapy . however, it is crucial to know if they would be effective and adhere to known standards. |
| Approach: | They propose to use large language models to automate talk therapy with a focus on tobacco addiction. |
| Outcome: | The proposed chatbot showed adherence to MI standards in 98% of utterances, higher than human counsellors. |
Latent Positional Information is in the Self-Attention Variance of Transformer Language Models Without Positional Embeddings (2023.acl-short)
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| Challenge: | Recent research has called into question the necessity of positional embeddings in transformer language models. |
| Approach: | They propose to discard positional embeddings in transformer language models to facilitate more efficient pretraining. |
| Outcome: | The proposed model encodes strong positional information through shrinkage of self-attention variance. |
LLMs cannot find reasoning errors, but can correct them given the error location (2024.findings-acl)
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| Challenge: | Recent attempts to self-correct logical or reasoning errors often cause correct answers to become incorrect, resulting in poor performance overall. |
| Approach: | They propose to use a backtracking setup to test the correction abilities of LLMs on their mistake-finding ability to find logical mistakes. |
| Outcome: | The proposed model improves on 5 reasoning tasks, showing that it can correct logical mistakes without ground truth labels or training data. |
A Tale of Two Regulatory Regimes: Creation and Analysis of a Bilingual Privacy Policy Corpus (2022.lrec-1)
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Siddhant Arora, Henry Hosseini, Christine Utz, Vinayshekhar Bannihatti Kumar, Tristan Dhellemmes, Abhilasha Ravichander, Peter Story, Jasmine Mangat, Rex Chen, Martin Degeling, Thomas Norton, Thomas Hupperich, Shomir Wilson, Norman Sadeh
| Challenge: | With the introduction of new privacy regulations, disclosures made by the same organization are not always the same in different languages. |
| Approach: | They propose a language annotation scheme to capture nuances of two new privacy regulations, namely the EU’s GDPR and California’s CCPA/CPRA. |
| Outcome: | The proposed method captures the nuances of two new privacy regulations and compares them to a corpus of 64 privacy policies in English and 91 in German with manual annotations for 8K and 19K fine-grained data practices. |
Generating Logical Forms from Graph Representations of Text and Entities (P19-1)
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| Challenge: | Recent approaches to semantic parsing have cast it as a sequence-to-sequence task, with strong results. |
| Approach: | They propose a Graph Neural Network architecture to incorporate information about relevant entities and their relations during parsing. |
| Outcome: | The proposed approach outperforms the state-of-the-art in several tasks without pre-training and outperformed existing approaches when combined with BERT pre-trainment. |