Topic models can be useful tools to discover latent topics in collections of documents.
Recent studies have shown the feasibility of approach topic modeling as a clustering task …

Emerging neural radiance fields (NeRF) are a promising scene representation for computer
graphics, enabling high-quality 3D reconstruction and novel view synthesis from image …

GPT-$3 $ has attracted lots of attention due to its superior performance across a wide range
of NLP tasks, especially with its powerful and versatile in-context few-shot learning ability …

We provide the first exploration of sentence embeddings from text-to-text transformers (T5).
Sentence embeddings are broadly useful for language processing tasks. While T5 achieves …

While BERT is an effective method for learning monolingual sentence embeddings for
semantic similarity and embedding based transfer learning (Reimers and Gurevych, 2019) …

How to transfer the semantic information in a sentence to a computable numerical
embedding form is a fundamental problem in natural language processing. An informative …

Structured knowledge grounding (SKG) leverages structured knowledge to complete user
requests, such as semantic parsing over databases and question answering over …

Modeling customer shopping intentions is a crucial task for e-commerce, as it directly
impacts user experience and engagement. Thus, accurately understanding customer …

This paper presents the results of the WMT21 Metrics Shared Task. Participants were asked
to score the outputs of the translation systems competing in the WMT21 News Translation …

We illustrate how standard psychometric inventories originally designed for assessing
noncognitive human traits can be repurposed as diagnostic tools to evaluate analogous …