Papers by Martin Franz
Learning Cross-Lingual IR from an English Retriever (2022.naacl-main)
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| Challenge: | DR.DECR is a cross-lingual information retrieval system trained using multi-stage knowledge distillation (KD) DRDECR demonstrates superior accuracy over direct fine-tuning with labeled CLIR data. |
| Approach: | They propose a cross-lingual information retrieval system with multi-stage knowledge distillation . they teach powerful multilingual representations and CLIR by optimizing two corresponding KD objectives . |
| Outcome: | The proposed system is the best single-model retriever on the XOR-TyDi benchmark . it is based on a multi-stage knowledge distillation process that can be expensive . |
The TechQA Dataset (2020.acl-main)
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Vittorio Castelli, Rishav Chakravarti, Saswati Dana, Anthony Ferritto, Radu Florian, Martin Franz, Dinesh Garg, Dinesh Khandelwal, Scott McCarley, Michael McCawley, Mohamed Nasr, Lin Pan, Cezar Pendus, John Pitrelli, Saurabh Pujar, Salim Roukos, Andrzej Sakrajda, Avi Sil, Rosario Uceda-Sosa, Todd Ward, Rong Zhang
| Challenge: | TECHQA is a domain-adaptation question answering dataset for the technical support domain. |
| Approach: | They propose a domain-adaptation question-answering dataset for the technical support domain that contains actual questions posed by users on a technical forum . |
| Outcome: | The TECHQA dataset highlights two real-world issues from the automated customer support domain. |
PrimeQA: The Prime Repository for State-of-the-Art Multilingual Question Answering Research and Development (2023.acl-demo)
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Avi Sil, Jaydeep Sen, Bhavani Iyer, Martin Franz, Kshitij Fadnis, Mihaela Bornea, Sara Rosenthal, Scott McCarley, Rong Zhang, Vishwajeet Kumar, Yulong Li, Md Arafat Sultan, Riyaz Bhat, Juergen Bross, Radu Florian, Salim Roukos
| Challenge: | Question Answering (QA) is a major area of research in Natural Language Processing (NLP) |
| Approach: | They propose a one-stop and open-source QA repository for question answering . it supports core QA functionalities like retrieval and reading comprehension . they say it will facilitate easy replication of state-of-the-art (SOTA) QA methods . |
| Outcome: | The proposed framework enables easy replication of state-of-the-art (SOTA) QA methods. |
Moving Beyond Downstream Task Accuracy for Information Retrieval Benchmarking (2023.findings-acl)
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Keshav Santhanam, Jon Saad-Falcon, Martin Franz, Omar Khattab, Avi Sil, Radu Florian, Md Arafat Sultan, Salim Roukos, Matei Zaharia, Christopher Potts
| Challenge: | Neural information retrieval (IR) systems have progressed rapidly in recent years . many IR benchmarks focus on downstream task accuracy, concealing costs incurred . |
| Approach: | They propose to include efficiency considerations on IR benchmarks to help drive progress . eral et al. propose to incorporate query latency and cost budgets into evaluation . |
| Outcome: | a new study shows that the best IR system varies according to how efficiency considerations are chosen and weighed . the proposed benchmarks would allow for more thorough exploration of possible system designs . |
Towards Robust Neural Retrieval with Source Domain Synthetic Pre-Finetuning (2022.coling-1)
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Revanth Gangi Reddy, Vikas Yadav, Md Arafat Sultan, Martin Franz, Vittorio Castelli, Heng Ji, Avirup Sil
| Challenge: | Existing neural IR systems rely on lexical matching for query-passage alignment, while masked language models use a dual encoder architecture to encode passages and questions into continuous vector representations. |
| Approach: | They propose to enhance the out-of-domain generalization of Dense Passage Retrieval (DPR) through synthetic data augmentation only in the source domain. |
| Outcome: | The proposed model outperforms existing models in in-domain and zero-shot evaluations on Wikipedia-based datasets. |
UDAPDR: Unsupervised Domain Adaptation via LLM Prompting and Distillation of Rerankers (2023.emnlp-main)
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Jon Saad-Falcon, Omar Khattab, Keshav Santhanam, Radu Florian, Martin Franz, Salim Roukos, Avirup Sil, Md Sultan, Christopher Potts
| Challenge: | Existing methods for information retrieval tasks require large labeled datasets for fine-tuning, but they can experience significant drops in accuracy due to distribution shifts from the training to the target domain. |
| Approach: | They propose a method for using large language models to generate large numbers of synthetic queries cheaply using an expensive LLM. |
| Outcome: | The proposed method boosts zero-shot accuracy in long-tail domains and achieves substantially lower latency than standard reranking methods. |