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Researcher profile

William W. Cohen

9 papers in the PaperMetrix corpus

Publications

Papers by this author

  1. HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering

    2018 · arXiv (Cornell University)

    Existing question answering (QA) datasets fail to train QA systems to perform complex reasoning and provide explanations for answers. We introduce HotpotQA, a new dataset with 113k Wikipedia-based question-answer pairs with four key features: (1) …

  2. Multi-Task Cross-Lingual Sequence Tagging from Scratch

    2016 · arXiv (Cornell University)

    We present a deep hierarchical recurrent neural network for sequence tagging. Given a sequence of words, our model employs deep gated recurrent units on both character and word levels to encode morphology and context information, …

  3. Personalized Recommendations using Knowledge Graphs

    2016

    Improving the performance of recommender systems using knowledge graphs is an important task. There have been many hybrid systems proposed in the past that use a mix of content-based and collaborative filtering techniques to boost …

  4. Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks

    2017 · arXiv (Cornell University)

    Recent papers have shown that neural networks obtain state-of-the-art performance on several different sequence tagging tasks. One appealing property of such systems is their generality, as excellent performance can be achieved with a unified architecture …

  5. TransNets

    2017

    Recently, deep learning methods have been shown to improve the performance of recommender systems over traditional methods, especially when review text is available. For example, a recent model, DeepCoNN, uses neural nets to learn one …

  6. Quasar: Datasets for Question Answering by Search and Reading

    2017 · arXiv (Cornell University)

    We present two new large-scale datasets aimed at evaluating systems designed to comprehend a natural language query and extract its answer from a large corpus of text. The Quasar-S dataset consists of 37000 cloze-style (fill-in-the-gap) …

  7. Breaking the Softmax Bottleneck: A High-Rank RNN Language Model

    2017 · arXiv (Cornell University)

    We formulate language modeling as a matrix factorization problem, and show that the expressiveness of Softmax-based models (including the majority of neural language models) is limited by a Softmax bottleneck. Given that natural language is …

  8. Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text

    2018

    Open Domain Question Answering (QA) is evolving from complex pipelined systems to end-to-end deep neural networks. Specialized neural models have been developed for extracting answers from either text alone or Knowledge Bases (KBs) alone. In …

  9. PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text

    2019

    Haitian Sun, Tania Bedrax-Weiss, William Cohen. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.