We therefore test its transferability by training on UFET and evaluate on traditional fine-grained benchmarks to get promising results. 2 What’s more, because we adopt a learning-to-rank objective to optimize the inference ability of LITE rather than classification on a specified label space, it is feasible to apply the trained model across different typing data sets. In addition, our LITE also yields the best performance on traditional (less) fine-grained entity typing tasks. Experimental results on the UFET benchmark (Choi et al., 2018) show that LITE drastically outperforms the recent state-of-the-art (SOTA) systems (Dai et al., 2021 Onoe et al., 2021 Liu et al., 2021) without any need of distantly supervised data as they do.

Moreover, to handle the dependency of type labels in different granularities, we also utilize the inference ability of an NLI model to learn that the finer label hypothesis of an entity mention entails its general label hypothesis. Second, the hypothesis, as a type description, also provides a semantically rich representation of the type, which further benefits few-shot learning with insufficient labeled data. First, the inference ability of a pre-trained NLI model can provide effective indirect supervision to improve the prediction of type information. Technically, LITE benefits ultra-fine entity typing from three perspectives. Specifically, those pre-trained multi-way classifiers cannot recognize types that are unseen in training, especially when there is no reasonable mapping from existing types to unseen type labels, unless the classifiers are re-trained to include those new types. Second, existing entity typing systems are far behind the desired capability in real-world applications in which any open-form types can appear. Without knowing the semantics of types, it is hard to match an entity mention to a correct type, especially when there is not sufficient annotated data for each type. First, prior studies have not tried to understand the target types since most classification systems converted all types into indices. In this context, two main issues are noticed from prior work. The drastic increase of types forces us to question whether the multi-way classification framework is still suitable for UFET. The task seeks to search for the most appropriate types for an entity among over ten thousand free-form type candidates. One representative work is the Ultra-fine Entity Typing (UFET) benchmark created by Choi et al. Accordingly, the community has recently paid much attention to more fine-grained modeling of types for entities. However, earlier efforts for entity typing are far from enough for representing real-world scenarios, where types of entities can be extremely diverse. 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. Experiments show that, with limited training data, LITE obtains state-of-the-art performance on the UFET task. 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. This causes two issues: (i) the classifiers do not capture the type semantics because 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. Existing systems formulate the task as a multi-way classification problem and train directly or distantly supervised classifiers. A key challenge for this task lies in the large number of types and the scarcity of annotated data per type. 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.

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