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Linfeng Song

7 papers in the PaperMetrix corpus

Publications

Papers by this author

  1. A Unified Query-based Generative Model for Question Generation and Question Answering

    2017 · arXiv (Cornell University)

    We propose a query-based generative model for solving both tasks of question generation (QG) and question an- swering (QA). The model follows the classic encoder- decoder framework. The encoder takes a passage and a query …

  2. Sentence-State LSTM for Text Representation

    2018

    Bi-directional LSTMs are a powerful tool for text representation. On the other hand, they have been shown to suffer various limitations due to their sequential nature. We investigate an alternative LSTM structure for encoding text, …

  3. Coordinated Reasoning for Cross-Lingual Knowledge Graph Alignment

    2020 · Proceedings of the AAAI Conference on Artificial Intelligence

    Existing entity alignment methods mainly vary on the choices of encoding the knowledge graph, but they typically use the same decoding method, which independently chooses the local optimal match for each source entity. This decoding …

  4. Leveraging Context Information for Natural Question Generation

    2018

    Linfeng Song, Zhiguo Wang, Wael Hamza, Yue Zhang, Daniel Gildea. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers). 2018.

  5. N-ary Relation Extraction using Graph-State LSTM

    2018

    Cross-sentence n-ary relation extraction detects relations among n entities across multiple sentences. Typical methods formulate an input as a document graph, integrating various intra-sentential and inter-sentential dependencies. The current state-of-the-art method splits the input graph …

  6. Semantic Neural Machine Translation Using AMR

    2019 · Transactions of the Association for Computational Linguistics

    Abstract It is intuitive that semantic representations can be useful for machine translation, mainly because they can help in enforcing meaning preservation and handling data sparsity (many sentences correspond to one meaning) of machine translation …

  7. A Graph-to-Sequence Model for AMR-to-Text Generation

    2018

    The problem of AMR-to-text generation is to recover a text representing the same meaning as an input AMR graph. The current state-of-the-art method uses a sequence-to-sequence model, leveraging LSTM for encoding a linearized AMR structure. …