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Viewing 2 papers from 2019 in Semantic Scholar
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    • NAACL 2019
      Arman Cohan, Waleed Ammar, Madeleine van Zuylen, Field Cady

      Identifying the intent of a citation in scientific papers (e.g., background information, use of methods, comparing results) is critical for machine reading of individual publications and automated analysis of the scientific literature. We propose a multitask approach to incorporate information in the structure of scientific papers for effective classification of citation intents. Our model achieves a new state-of-the-art on an existing ACL anthology dataset with a 13.3% absolute increase in F1 score, without relying on external linguistic resources or hand-engineered features as done in existing methods. In addition, we introduce a new dataset of citation intents which is more than five times larger and covers multiple scientific domains compared with existing datasets.

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    • NAACL 2019
      Iz Beltagy, Kyle Lo, Waleed Ammar

      In relation extraction with distant supervision, noisy labels make it difficult to train quality models. Previous neural models addressed this problem using an attention mechanism that attends to sentences that are likely to express the relations. We improve such models by combining the distant supervision data with an additional directly-supervised data, which we use as supervision for the attention weights. We find that joint training on both types of supervision leads to a better model because it improves the model's ability to identify noisy sentences. In addition, we find that sigmoidal attention weights with max pooling achieves better performance over the commonly used weighted average attention in this setup. Our proposed method achieves a new state-of-the-art result on the widely used FB-NYT dataset.

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