  The task of ultra-fine entity typing (UFET) seeks to predict diverse and
free-form words or phrases that describe the appropriate types of entities
mentioned in sentences. A key challenge for this task lies in the large amount
of types and the scarcity of annotated data per type. Existing systems
formulate the task as a multi-way classification problem and train directly or
distantly supervised classifiers. This causes two issues: (i) the classifiers
do not capture the type semantics since types are often converted into indices;
(ii) systems developed in this way are limited to predicting within a
pre-defined type set, and often fall short of generalizing to types that are
rarely seen or unseen in training. This work presents LITE, a new approach that
formulates entity typing as a natural language inference (NLI) problem, making
use of (i) the indirect supervision from NLI to infer type information
meaningfully represented as textual hypotheses and alleviate the data scarcity
issue, as well as (ii) a learning-to-rank objective to avoid the pre-defining
of a type set. Experiments show that, with limited training data, LITE obtains
state-of-the-art performance on the UFET task. In addition, LITE demonstrates
its strong generalizability, by not only yielding best results on other
fine-grained entity typing benchmarks, more importantly, a pre-trained LITE
system works well on new data containing unseen types.
