Kristina Toutanova
9 papers in the PaperMetrix corpus
Papers by this author
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Mitigating Catastrophic Forgetting in Language Transfer via Model Merging
2024 · arXiv (Cornell University)
As open-weight large language models (LLMs) achieve ever more impressive performances across a wide range of tasks in English, practitioners aim to adapt these models to different languages. However, such language adaptation is often accompanied …
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Observed versus latent features for knowledge base and text inference
2015
In this paper we show the surprising effectiveness of a simple observed features model in comparison to latent feature models on two benchmark knowledge base completion datasets, FB15K and WN18. We also compare latent and …
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Representing Text for Joint Embedding of Text and Knowledge Bases
2015
Models that learn to represent textual and knowledge base relations in the same continuous latent space are able to perform joint inferences among the two kinds of relations and obtain high accuracy on knowledge base …
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Compositional Learning of Embeddings for Relation Paths in Knowledge Base and Text
2016
Modeling relation paths has offered significant gains in embedding models for knowledge base (KB) completion. However, enumerating paths between two entities is very expensive, and existing approaches typically resort to approximation with a sampled subset. …
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Cross-Sentence N-ary Relation Extraction with Graph LSTMs
2017 · arXiv (Cornell University)
Past work in relation extraction has focused on binary relations in single sentences. Recent NLP inroads in high-value domains have sparked interest in the more general setting of extracting n-ary relations that span multiple sentences. …
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Natural Questions: A Benchmark for Question Answering Research
2019 · Transactions of the Association for Computational Linguistics
We present the Natural Questions corpus, a question answering data set. Questions consist of real anonymized, aggregated queries issued to the Google search engine. An annotator is presented with a question along with a Wikipedia …
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BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions
2019 · arXiv (Cornell University)
In this paper we study yes/no questions that are naturally occurring --- meaning that they are generated in unprompted and unconstrained settings. We build a reading comprehension dataset, BoolQ, of such questions, and show that …
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A Nested Attention Neural Hybrid Model for Grammatical Error Correction
2017
Jianshu Ji, Qinlong Wang, Kristina Toutanova, Yongen Gong, Steven Truong, Jianfeng Gao. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2017.
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Cross-Sentence <i>N</i>-ary Relation Extraction with Graph LSTMs
2017 · Transactions of the Association for Computational Linguistics
Past work in relation extraction has focused on binary relations in single sentences. Recent NLP inroads in high-value domains have sparked interest in the more general setting of extracting n-ary relations that span multiple sentences. …