Andrew McCallum
13 papers in the PaperMetrix corpus
Papers by this author
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<i>Ask the GRU</i>
2016 · arXiv
In a variety of application domains the content to be recommended to users is associated with text. This includes research papers, movies with associated plot summaries, news articles, blog posts, etc. Recommendation approaches based on …
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Multi-step Retriever-Reader Interaction for Scalable Open-domain Question Answering
2019 · arXiv (Cornell University)
This paper introduces a new framework for open-domain question answering in which the retriever and the reader iteratively interact with each other. The framework is agnostic to the architecture of the machine reading model, only …
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ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning
2020
Given questions regarding some prototypical situation -such as Name something that people usually do before they leave the house for work? -a human can easily answer them via acquired experiences. There can be multiple right …
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CBR-iKB: A Case-Based Reasoning Approach for Question Answering over Incomplete Knowledge Bases
2022 · arXiv (Cornell University)
Knowledge bases (KBs) are often incomplete and constantly changing in practice. Yet, in many question answering applications coupled with knowledge bases, the sparse nature of KBs is often overlooked. To this end, we propose a …
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Low-Resource Compositional Semantic Parsing with Concept Pretraining
2023 · arXiv (Cornell University)
Semantic parsing plays a key role in digital voice assistants such as Alexa, Siri, and Google Assistant by mapping natural language to structured meaning representations. When we want to improve the capabilities of a voice …
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Editable User Profiles for Controllable Text Recommendations
2023
Methods for making high-quality recommendations often rely on learning latent representations from interaction data. These methods, while performant, do not provide ready mechanisms for users to control the recommendation they receive. Our work tackles this …
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Compositional Vector Space Models for Knowledge Base Completion
2015 · arXiv (Cornell University)
Arvind Neelakantan, Benjamin Roth, Andrew McCallum. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015.
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Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning
2017 · arXiv (Cornell University)
Knowledge bases (KB), both automatically and manually constructed, are often incomplete --- many valid facts can be inferred from the KB by synthesizing existing information. A popular approach to KB completion is to infer new …
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Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking
2018
Extraction from raw text to a knowledge base of entities and fine-grained types is often cast as prediction into a flat set of entity and type labels, neglecting the rich hierarchies over types and entities …
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Energy and Policy Considerations for Deep Learning in NLP
2019
Recent progress in hardware and methodology for training neural networks has ushered in a new generation of large networks trained on abundant data. These models have obtained notable gains in accuracy across many NLP tasks. …
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Word Representations via Gaussian Embedding
2015 · arXiv (Cornell University)
Abstract: Current work in lexical distributed representations maps each word to a point vector in low-dimensional space. Mapping instead to a density provides many interesting advantages, including better capturing uncertainty about a representation and its …
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Lexicon Infused Phrase Embeddings for Named Entity Resolution
2015
Most state-of-the-art approaches for named-entity recognition (NER) use semi supervised information in the form of word clusters and lexicons. Recently neural network-based language models have been explored, as they as a byprod-uct generate highly informative …
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Case-based Reasoning for Natural Language Queries over Knowledge Bases
2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Rajarshi Das, Manzil Zaheer, Dung Thai, Ameya Godbole, Ethan Perez, Jay Yoon Lee, Lizhen Tan, Lazaros Polymenakos, Andrew McCallum. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 2021.