Dan Gillick

Also published as: Daniel Gillick


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Entity Linking in 100 Languages
Jan A. Botha | Zifei Shan | Daniel Gillick
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We propose a new formulation for multilingual entity linking, where language-specific mentions resolve to a language-agnostic Knowledge Base. We train a dual encoder in this new setting, building on prior work with improved feature representation, negative mining, and an auxiliary entity-pairing task, to obtain a single entity retrieval model that covers 100+ languages and 20 million entities. The model outperforms state-of-the-art results from a far more limited cross-lingual linking task. Rare entities and low-resource languages pose challenges at this large-scale, so we advocate for an increased focus on zero- and few-shot evaluation. To this end, we provide Mewsli-9, a large new multilingual dataset matched to our setting, and show how frequency-based analysis provided key insights for our model and training enhancements.


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Learning Dense Representations for Entity Retrieval
Daniel Gillick | Sayali Kulkarni | Larry Lansing | Alessandro Presta | Jason Baldridge | Eugene Ie | Diego Garcia-Olano
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

We show that it is feasible to perform entity linking by training a dual encoder (two-tower) model that encodes mentions and entities in the same dense vector space, where candidate entities are retrieved by approximate nearest neighbor search. Unlike prior work, this setup does not rely on an alias table followed by a re-ranker, and is thus the first fully learned entity retrieval model. We show that our dual encoder, trained using only anchor-text links in Wikipedia, outperforms discrete alias table and BM25 baselines, and is competitive with the best comparable results on the standard TACKBP-2010 dataset. In addition, it can retrieve candidates extremely fast, and generalizes well to a new dataset derived from Wikinews. On the modeling side, we demonstrate the dramatic value of an unsupervised negative mining algorithm for this task.


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A Fast, Compact, Accurate Model for Language Identification of Codemixed Text
Yuan Zhang | Jason Riesa | Daniel Gillick | Anton Bakalov | Jason Baldridge | David Weiss
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We address fine-grained multilingual language identification: providing a language code for every token in a sentence, including codemixed text containing multiple languages. Such text is prevalent online, in documents, social media, and message boards. We show that a feed-forward network with a simple globally constrained decoder can accurately and rapidly label both codemixed and monolingual text in 100 languages and 100 language pairs. This model outperforms previously published multilingual approaches in terms of both accuracy and speed, yielding an 800x speed-up and a 19.5% averaged absolute gain on three codemixed datasets. It furthermore outperforms several benchmark systems on monolingual language identification.


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Multilingual Language Processing From Bytes
Dan Gillick | Cliff Brunk | Oriol Vinyals | Amarnag Subramanya
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Exploring the steps of Verb Phrase Ellipsis
Zhengzhong Liu | Edgar Gonzàlez Pellicer | Daniel Gillick
Proceedings of the Workshop on Coreference Resolution Beyond OntoNotes (CORBON 2016)


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Embedding Methods for Fine Grained Entity Type Classification
Dani Yogatama | Daniel Gillick | Nevena Lazic
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)


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A New Entity Salience Task with Millions of Training Examples
Jesse Dunietz | Daniel Gillick
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, volume 2: Short Papers


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Jointly Learning to Extract and Compress
Taylor Berg-Kirkpatrick | Dan Gillick | Dan Klein
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies


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Non-Expert Evaluation of Summarization Systems is Risky
Dan Gillick | Yang Liu
Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk


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A Scalable Global Model for Summarization
Dan Gillick | Benoit Favre
Proceedings of the Workshop on Integer Linear Programming for Natural Language Processing

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Sentence Boundary Detection and the Problem with the U.S.
Dan Gillick
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers


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Why Generative Phrase Models Underperform Surface Heuristics
John DeNero | Dan Gillick | James Zhang | Dan Klein
Proceedings on the Workshop on Statistical Machine Translation