Papers by Young-Suk Lee
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 . |
Leveraging Abstract Meaning Representation for Knowledge Base Question Answering (2021.findings-acl)
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Pavan Kapanipathi, Ibrahim Abdelaziz, Srinivas Ravishankar, Salim Roukos, Alexander Gray, Ramón Fernandez Astudillo, Maria Chang, Cristina Cornelio, Saswati Dana, Achille Fokoue, Dinesh Garg, Alfio Gliozzo, Sairam Gurajada, Hima Karanam, Naweed Khan, Dinesh Khandelwal, Young-Suk Lee, Yunyao Li, Francois Luus, Ndivhuwo Makondo, Nandana Mihindukulasooriya, Tahira Naseem, Sumit Neelam, Lucian Popa, Revanth Gangi Reddy, Ryan Riegel, Gaetano Rossiello, Udit Sharma, G P Shrivatsa Bhargav, Mo Yu
| Challenge: | Existing approaches face challenges including complex question understanding and lack of large end-to-end training datasets. |
| Approach: | They propose a modular knowledge base question answering system that leverages AMR parses for task-independent question understanding. |
| Outcome: | The proposed system achieves state-of-the-art performance on two prominent KBQA datasets based on DBpedia. |
Bootstrapping Multilingual AMR with Contextual Word Alignments (2021.eacl-main)
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Janaki Sheth, Young-Suk Lee, Ramón Fernandez Astudillo, Tahira Naseem, Radu Florian, Salim Roukos, Todd Ward
| Challenge: | Abstract Meaning Representation (AMR) is a sentence-level graph that is biased towards English. |
| Approach: | They propose a technique for foreign-text-to-English AMR alignment using contextual word alignment between English and foreign language tokens. |
| Outcome: | The proposed technique outperforms the best results for German, Italian, Spanish and Chinese. |
Pushing the Limits of AMR Parsing with Self-Learning (2020.findings-emnlp)
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Young-Suk Lee, Ramón Fernandez Astudillo, Tahira Naseem, Revanth Gangi Reddy, Radu Florian, Salim Roukos
| Challenge: | Abstract Meaning Representation (AMR) parsing has experienced a notable growth in performance in the last two years due to the impact of transfer learning and the development of novel architectures specific to AMR. |
| Approach: | They propose to use AMR annotations to generate synthetic text and refine actions oracle without additional human annotations for AMR parsing. |
| Outcome: | The proposed models improve on AMR 1.0 and 2.0 without human annotations. |
Ensemble-Instruct: Instruction Tuning Data Generation with a Heterogeneous Mixture of LMs (2023.findings-emnlp)
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Young-Suk Lee, Md Sultan, Yousef El-Kurdi, Tahira Naseem, Asim Munawar, Radu Florian, Salim Roukos, Ramón Astudillo
| Challenge: | Empirical studies with different instruction-tuned LMs show that our proposed method yields higher-quality instruction tuning data than Self-Instruct. |
| Approach: | They propose to use in-context learning techniques to train strong conversational agents . they propose to categorize and simplify ICL templates to make prompt learning easier . |
| Outcome: | Empirical results show that the proposed method yields higher-quality instruction tuning data than Self-Instruct and improves performance of both vanilla and instruction-tuned LMs. |
A Semantics-aware Transformer Model of Relation Linking for Knowledge Base Question Answering (2021.acl-short)
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Tahira Naseem, Srinivas Ravishankar, Nandana Mihindukulasooriya, Ibrahim Abdelaziz, Young-Suk Lee, Pavan Kapanipathi, Salim Roukos, Alfio Gliozzo, Alexander Gray
| Challenge: | Existing knowledge base question answering systems do not leverage the explicit semantic parse of the question text. |
| Approach: | They propose a transformer-based neural model that leverages the AMR semantic parse of a sentence. |
| Outcome: | The proposed model outperforms the state-of-the-art on 4 popular benchmark datasets. |
DocAMR: Multi-Sentence AMR Representation and Evaluation (2022.naacl-main)
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Tahira Naseem, Austin Blodgett, Sadhana Kumaravel, Tim O’Gorman, Young-Suk Lee, Jeffrey Flanigan, Ramón Astudillo, Radu Florian, Salim Roukos, Nathan Schneider
| Challenge: | Abstract Meaning Representation (AMR) graphs are compared to gold graphs by the Smatch metric, but lack a well-defined representation and evaluation. |
| Approach: | They propose an algorithm for deriving a unified graph representation using a super-sentential annotation method. |
| Outcome: | The proposed algorithm avoids the pitfalls of over-merging and lacks coherence from under merging. |
SYGMA: A System for Generalizable and Modular Question Answering Over Knowledge Bases (2022.findings-emnlp)
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Sumit Neelam, Udit Sharma, Hima Karanam, Shajith Ikbal, Pavan Kapanipathi, Ibrahim Abdelaziz, Nandana Mihindukulasooriya, Young-Suk Lee, Santosh Srivastava, Cezar Pendus, Saswati Dana, Dinesh Garg, Achille Fokoue, G P Shrivatsa Bhargav, Dinesh Khandelwal, Srinivas Ravishankar, Sairam Gurajada, Maria Chang, Rosario Uceda-Sosa, Salim Roukos, Alexander Gray, Guilherme Lima, Ryan Riegel, Francois Luus, L V Subramaniam
| Challenge: | Knowledge Base Question Answering (KBQA) systems have limited generalizability across knowledge bases and multiple reasoning types. |
| Approach: | They propose a modular approach for KBQA that is built on a framework adaptable to multiple knowledge bases and reasoning types. |
| Outcome: | The proposed approach is generalized across multiple knowledge bases and reasoning types. |
Structure-aware Fine-tuning of Sequence-to-sequence Transformers for Transition-based AMR Parsing (2021.emnlp-main)
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| Challenge: | Recent work shows that pre-trained sequence-to-sequence Transformer models are effective in predicting linearized Abstract Meaning Representation graphs. |
| Approach: | They propose a structure-aware transition-based approach to AMR parsing that integrates general pre-trained sequence-to-sequence language models with a structured transition set. |
| Outcome: | The proposed approach retains the desirable properties of previous approaches while reaching the new parsing state of the art for AMR 2.0. |
GPT-too: A Language-Model-First Approach for AMR-to-Text Generation (2020.acl-main)
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Manuel Mager, Ramón Fernandez Astudillo, Tahira Naseem, Md Arafat Sultan, Young-Suk Lee, Radu Florian, Salim Roukos
| Challenge: | Existing approaches to generating text from AMRs focus on training sequence-to-sequence or graph-tosequent models on annotated data. |
| Approach: | They propose a strong pre-trained language model with cycle consistency-based re-scoring to generate AMR text. |
| Outcome: | The proposed model outperforms existing methods on the English LDC2017T10 dataset. |
Maximum Bayes Smatch Ensemble Distillation for AMR Parsing (2022.naacl-main)
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| Challenge: | AMR parsing has experienced an unprecendented increase in performance in the last three years due to a mixture of effects including architecture improvements and transfer learning. |
| Approach: | They propose to combine Smatch-based ensembling techniques with ensemble distillation to overcome this diminishing returns of silver data. |
| Outcome: | The proposed technique can produce gains rivaling those of human annotated data for QALD-9 and achieve a new state-of-the-art for BioAMR. |