  Attribute value extraction refers to the task of identifying values of an
attribute of interest from product information. Product attribute values are
essential in many e-commerce scenarios, such as customer service robots,
product ranking, retrieval and recommendations. While in the real world, the
attribute values of a product are usually incomplete and vary over time, which
greatly hinders the practical applications. In this paper, we introduce MAVE, a
new dataset to better facilitate research on product attribute value
extraction. MAVE is composed of a curated set of 2.2 million products from
Amazon pages, with 3 million attribute-value annotations across 1257 unique
categories. MAVE has four main and unique advantages: First, MAVE is the
largest product attribute value extraction dataset by the number of
attribute-value examples. Second, MAVE includes multi-source representations
from the product, which captures the full product information with high
attribute coverage. Third, MAVE represents a more diverse set of attributes and
values relative to what previous datasets cover. Lastly, MAVE provides a very
challenging zero-shot test set, as we empirically illustrate in the
experiments. We further propose a novel approach that effectively extracts the
attribute value from the multi-source product information. We conduct extensive
experiments with several baselines and show that MAVE is an effective dataset
for attribute value extraction task. It is also a very challenging task on
zero-shot attribute extraction. Data is available at {\it
\url{https://github.com/google-research-datasets/MAVE}}.
