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Title: Reducing Distant Supervision Noise with Maxpooled Attention and Sentence-Level Supervision

Paper content:
We propose an effective multitask learning setup for reducing distant supervision noise by leveraging sentence-level supervision.
We show how sentence-level supervision can be used to improve the encoding of individual sentences, and to learn which input sentences are more likely to express the relationship between a pair of entities.
We also introduce a novel neural architecture for collecting signals from multiple input sentences, which combines the benefits of attention and maxpooling.
The proposed method increases AUC by 10% (from 0.261 to 0.284), and outperforms recently published results on the FB-NYT dataset.
Early work in relation extraction from text used fully supervised methods, e.g., BID2 , which motivated the development of relatively small datasets with sentence-level annotations such as ACE 2004 BID2 , BioInfer and SemEval 2010 .
Recognizing the difficulty of annotating text with relations, especially when the number of relation types of interest is large, BID16 pioneered the distant supervision approach to relation extraction, where a knowledge base (KB) and a text corpus are used to automatically generate a large dataset of labeled sentences which is then used to train a relation classifier.
Distant supervision provides a practical alternative to manual annotations, but introduces many noisy examples.
Although many methods have been proposed to reduce the noise in distantly supervised models for relation extraction (e.g., BID8 BID23 BID22 BID5 BID27 BID11 , a rather obvious approach has been understudied: using sentence-level supervision to augment distant supervision.
Intuitively, supervision at the sentence-level can help reduce the noise in distantly supervised models by identifying which of the input sentences for a given pair of entities are likely to express a relation.We experiment with a variety of model architectures to combine sentence-and bag-level supervision and find it most effective to use the sentence-level annotations to directly supervise the sentence encoder component of the model in a multi-task learning framework.
We also introduce a novel maxpooling attention architecture for combining the evidence provided by different sentences where the entity pair is mentioned, and use the sentence-level annotations to supervise attention weights.The contributions of this paper are as follows:• We propose an effective multitask learning setup for reducing distant supervision noise by leveraging existing datasets of relations annotated at the sentence level.•
We propose maxpooled attention, a neural architecture which combines the benefits of maxpooling and soft attention, and show that it helps the model combine information about a pair of entities from multiple sentences.•
We release our library for relation extraction as open source. 1
The following section defines the notation we use, describes the problem and provides an overview of our approach.
We propose two complementary methods to improve performance and reduce noise in distantly supervised relation extraction.
The first is incorporating sentence-level supervision and the second is maxpooled attention, a novel form of attention.
The sentence-level supervision improves sentence encoding and provides supervision for attention weights, while maxpooled attention effectively combines sentence encodings and their weights into a bag encoding.
Our experiments show a 10% improvement in AUC (from 0.261 to 0.284) outperforming recently published results on the FB-NYT dataset .

assistant: text: A new form of attention that works well for the distant supervision setting, and a multitask learning approach to add sentence-level annotations.