Papers by Lesly Miculicich
CaLM: Contrasting Large and Small Language Models to Verify Grounded Generation (2024.findings-acl)
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| Challenge: | Existing methods to generate grounded responses are prone to errors due to the irrelevancy of input documents. |
| Approach: | They propose a framework that leverages the insight that a robust grounded response should be consistent with information derived solely from its cited sources. |
| Outcome: | Experiments on three open-domain question-answering datasets show that the proposed framework improves performance by 1.5% to 7% without any model fine-tuning. |
Selecting, Planning, and Rewriting: A Modular Approach for Data-to-Document Generation and Translation (D19-56)
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| Challenge: | Existing systems for document-level generation and translation are too complex to capture the complexity of the problem. |
| Approach: | They propose to adapt a large scale system trained on WMT data to a document in a different language. |
| Outcome: | The proposed system generates a textual document from structured data or a document in a different language. |
Document-Level Neural Machine Translation with Hierarchical Attention Networks (D18-1)
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| Challenge: | Neural machine translation (NMT) can be improved by including document-level contextual information. |
| Approach: | They propose a hierarchical attention model that captures document-level contextual information and conditioning on the NMT model’s own hidden states. |
| Outcome: | The proposed model improves the BLEU score over a strong NMT baseline with the state-of-the-art in context-aware methods and that both the encoder and decoder benefit from context in complementary ways. |
Graph Refinement for Coreference Resolution (2022.findings-acl)
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| Challenge: | Existing models for coreference resolution are based on independent mention pair-wise decisions. |
| Approach: | They propose a model that learns coreference at the document-level and takes global decisions. |
| Outcome: | The proposed model improves over baselines, reinforcing the hypothesis that document-level information improves conference resolution. |