A bottleneck to developing Semantic Parsing (SP) models is the need for a large volume of
human-labeled training data. Given the complexity and cost of human annotation for SP …

In this work, we take the named entity recognition task in the English language as a case
study and explore style transfer as a data augmentation method to increase the size and …

Zero-shot cross-lingual transfer is when a multilingual model is trained to perform a task in
one language and then is applied to another language. Although the zero-shot cross-lingual …

Cross-lingual semantic parsing transfers parsing capability from a high-resource language
(eg, English) to low-resource languages with scarce training data. Previous work has …

Recognizing entities in texts is a central need in many information-seeking scenarios, and
indeed, Named Entity Recognition (NER) is arguably one of the most successful examples …

Abstract We introduce the Korean-Learner-Morpheme (KLM) corpus, a manually annotated
dataset consisting of 129,784 morphemes from second language (L2) learners of Korean …

Dialogue state tracking (DST) aims to record user queries and goals during a conversational
interaction achieved by maintaining a prede-fined set of slots and their corresponding …

As social media has gradually become an indispensable part of people's life, more and
more users begin to express their opinions on social media. These opinions contain rich …

Sequence Labeling (SL) is long-standing in Natural Language Processing (NLP).
Traditionally, discriminative models have been widely used to capture the conditional …

Large language models have ushered in a golden age of semantic parsing. The seq2seq
paradigm allows for open-schema and abstractive attribute and relation extraction given …