Papers by Daniel Gillick
MOLEMAN: Mention-Only Linking of Entities with a Mention Annotation Network (2021.acl-short)
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| Challenge: | Existing approaches to entity linking represent each entity with a single vector, but instead use a contextualized mention-encoder that learns to place similar mentions of the same entity closer in vector space than mentions from different entities. |
| Approach: | They propose an instance-based nearest neighbor approach to entity linking that allows for a contextualized mention-encoder to learn to place similar mentions of the same entity closer in vector space than mentions from different entities. |
| Outcome: | The proposed approach outperforms all other systems on two multilingual benchmarks and is simpler to train and interpretable. |
Entity Linking in 100 Languages (2020.emnlp-main)
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| Challenge: | Existing approaches to multilingual entity linking are cross-lingual, with a focus on zero-shot evaluation. |
| Approach: | They propose a new formulation for multilingual entity linking where language-specific mentions resolve to a language-agnostic Knowledge Base. |
| Outcome: | The proposed model outperforms state-of-the-art models on a large multilingual dataset and shows that frequency-based analysis provided key insights for the model and training enhancements. |
Time-Aware Language Models as Temporal Knowledge Bases (2022.tacl-1)
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Bhuwan Dhingra, Jeremy R. Cole, Julian Martin Eisenschlos, Daniel Gillick, Jacob Eisenstein, William W. Cohen
| Challenge: | Existing language models are trained on snapshots of data collected at a specific moment in time. |
| Approach: | They propose a diagnostic dataset aimed at probing LMs for factual knowledge that changes over time. |
| Outcome: | The proposed method improves memorization of seen facts and calibration on unseen facts from future time periods. |
NAIL: Lexical Retrieval Indices with Efficient Non-Autoregressive Decoders (2023.emnlp-main)
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| Challenge: | Neural document rerankers require dedicated hardware for serving, which is costly and often not feasible. |
| Approach: | They propose a method that captures 86% of the gains of a Transformer cross-attention model with a lexicalized scoring function that only requires 10-6% of . the model architecture is compatible with recent encoder-decoder and decoder-only large language models, such as T5, GPT-3 and PaLM. |
| Outcome: | The proposed model captures 86% of the gains of a Transformer cross-attention model with a lexicalized scoring function. |
Selectively Answering Ambiguous Questions (2023.emnlp-main)
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| Challenge: | Prior work has focused on the case in which the question is clear and the answer is unambiguous but possibly unknown. |
| Approach: | They propose to use a sampled set of questions to calibrate answers to ambiguous questions with varying model scales. |
| Outcome: | The results show that sampling-based confidence scores help calibrate answers to relatively unambiguous questions, with more dramatic improvements on ambiguous ones. |
A Fast, Compact, Accurate Model for Language Identification of Codemixed Text (D18-1)
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| Challenge: | a feed-forward network can label codemixed and monolingual text in 100 languages and 100 language pairs. |
| Approach: | They propose a feed-forward network that can provide a language code for every token in a sentence . they show that the model can label both codemixed and monolingual text in 100 languages . |
| Outcome: | The proposed model outperforms previous multilingual approaches in terms of accuracy and speed. |