Papers by Da Ju
The Flores-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation (2022.tacl-1)
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Naman Goyal, Cynthia Gao, Vishrav Chaudhary, Peng-Jen Chen, Guillaume Wenzek, Da Ju, Sanjana Krishnan, Marc’Aurelio Ranzato, Francisco Guzmán, Angela Fan
| Challenge: | a lack of good evaluation benchmarks hinders progress in low-resource and multilingual machine translation . despite advances in translation quality for a handful of languages, many low-source languages are not even supported by most popular translation engines. |
| Approach: | They propose a high-quality evaluation benchmark for machine translation using 3001 sentences from Wikipedia . they aim to improve evaluation of models on long tail of low-resource languages . |
| Outcome: | The proposed evaluation benchmarks are based on 3001 sentences extracted from Wikipedia . the results show that the models can be used to evaluate multilingual systems . |
Recipes for Building an Open-Domain Chatbot (2021.eacl-main)
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Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Eric Michael Smith, Y-Lan Boureau, Jason Weston
| Challenge: | Existing work shows that scaling models in the number of parameters and the size of the data they are trained on gives improved results, but other factors are important. |
| Approach: | They propose to build open-domain chatbots that can be scaled to improve their performance . they use a blend of cognitive and cognitive skills to build a model that combines these skills . |
| Outcome: | The proposed models outperform existing approaches in multi-turn dialogue on engagingness and humanness measurements. |
Multi-Modal Open-Domain Dialogue (2021.emnlp-main)
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| Challenge: | Recent work in open-domain conversational agents has demonstrated that significant improvements in humanness and user preference can be achieved via massive scaling in both pre-training data and model size. |
| Approach: | They combine open-domain dialogue agents with vision models to investigate human preferences and humanness. |
| Outcome: | The proposed model outperforms existing models in multi-modal dialogue while performing as well as its predecessor (text-only) BlenderBot. |
Are Female Carpenters like Blue Bananas? A Corpus Investigation of Occupation Gender Typicality (2024.findings-acl)
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| Challenge: | Typicality effects shape the way we use language, says a researcher . mentioning unusual properties of events is not a typical feature of bananas, he says . |
| Approach: | They explore whether occupations and gender display the same patterns of mentioning as do bananas and color . they find that gender mentioning is correlated with femaleness of occupation in particular . |
| Outcome: | a new study shows that people tend to mention unusual properties of events when they talk about them . the findings contradict the idea that gender mentioning occurs when special events are pointed out . |
The Dialogue Dodecathlon: Open-Domain Knowledge and Image Grounded Conversational Agents (2020.acl-main)
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| Challenge: | a set of 12 tasks that measure if a conversational agent can communicate engagingly with personality and empathy, ask questions, answer questions by utilizing knowledge resources, and perceive and converse about images. |
| Approach: | They propose a set of 12 tasks that measure if a conversational agent can communicate engagingly with personality and empathy . they use large dialogue datasets to multi-task and obtain state-of-the-art results . |
| Outcome: | The proposed model improves over a BERT pre-trained model on large dialogue datasets and provides state-of-the-art results on many of the tasks. |
To the Globe (TTG): Towards Language-Driven Guaranteed Travel Planning (2024.emnlp-demo)
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Da Ju, Song Jiang, Andrew Cohen, Aaron Foss, Sasha Mitts, Arman Zharmagambetov, Brandon Amos, Xian Li, Justine Kao, Maryam Fazel-Zarandi, Yuandong Tian
| Challenge: | a new system that takes natural language requests from users generates and trains optimal travel plans . a user can provide instructions and an agent provides optimal solutions . the system takes 5seconds to reply to the user request with guaranteed itineraries . |
| Approach: | They propose a real-time demo system that takes natural language requests from users . it translates requests to symbolic form and produces optimal travel itineraries with LLM . |
| Outcome: | The proposed system produces optimal travel itineraries with mixed integer linear programming solvers. |
Bot-Adversarial Dialogue for Safe Conversational Agents (2021.naacl-main)
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| Challenge: | a new method for evaluating chatbot safety is proposed to mimic human-generated data . a bot-adversarial dialogue model learns undesirable features from this data, a study finds . |
| Approach: | They propose a human-and-model-in-the-loop framework for evaluating toxicity of chatbots . they propose two methods for safe conversational agents by either training on data or ”baking-in” safety to the generative model itself. |
| Outcome: | The proposed methods are safer than existing models while maintaining usability metrics, the authors say . they show that the proposed methods can be used to make safer models with human-model interactions . |
Domain Regeneration: How well do LLMs match syntactic properties of text domains? (2025.findings-acl)
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| Challenge: | Recent improvements in large language models have improved their ability to approximate distributions . authors find that LLMs can suffer from model collapse due to domain considerations based on pretraining . |
| Approach: | They use open source LLMs to regenerate permissively licensed English text from Wikipedia and news text. |
| Outcome: | The proposed model can faithfully match the human-generated distributions in a semantically-controlled setting. |