Abstract Machine learning has become the state-of-the-art technique for many tasks
including computer vision, natural language processing, speech processing tasks, etc …

Understanding natural language questions entails the ability to break down a question into
the requisite steps for computing its answer. In this work, we introduce a Question …

How can we design Natural Language Processing (NLP) systems that learn from human
feedback? There is a growing research body of Human-in-the-loop (HITL) NLP frameworks …

We present a new large-scale corpus of Question-Answer driven Semantic Role Labeling
(QA-SRL) annotations, and the first high-quality QA-SRL parser. Our corpus, QA-SRL Bank …

We study the task of semantic parse correction with natural language feedback. Given a
natural language utterance, most semantic parsing systems pose the problem as one-shot …

Entity linking (EL) is concerned with disambiguating entity mentions in a text against
knowledge bases (KB). It is crucial in a considerable number of fields like humanities …

We introduce Question-Answer Meaning Representations (QAMRs), which represent the
predicate-argument structure of a sentence as a set of question-answer pairs. We also …

As a promising paradigm, interactive semantic parsing has shown to improve both semantic
parsing accuracy and user confidence in the results. In this paper, we propose a new …

Despite the widely successful applications, bootstrapping and fine-tuning semantic parsers
are still a tedious process with challenges such as costly data annotation and privacy risks …

Given a text description, most existing semantic parsers synthesize a program in one shot.
However, it is quite challenging to produce a correct program solely based on the …