3 citations · 6 across the 5 of their papers we have counts for
8 papers
PIZZA: A new benchmark for complex end-to-end task-oriented parsing
Konstantine Arkoudas, Nicolas Guenon des Mesnards, Melanie Rubino +4
Much recent work in task-oriented parsing has focused on finding a middle ground between flat slots and intents, which are inexpressive but easy to annotate, and powerful represent…
Compositional Task-Oriented Parsing as Abstractive Question Answering
Wenting Zhao, Konstantine Arkoudas, Weiqi Sun +1
Task-oriented parsing (TOP) aims to convert natural language into machine-readable representations of specific tasks, such as setting an alarm. A popular approach to TOP is to appl…
Training Naturalized Semantic Parsers with Very Little Data
Subendhu Rongali, Konstantine Arkoudas, Melanie Rubino +1
Semantic parsing is an important NLP problem, particularly for voice assistants such as Alexa and Google Assistant. State-of-the-art (SOTA) semantic parsers are seq2seq architectur…
Unfreeze with Care: Space-Efficient Fine-Tuning of Semantic Parsing Models
Weiqi Sun, Haidar Khan, Nicolas Guenon des Mesnards +2
Semantic parsing is a key NLP task that maps natural language to structured meaning representations. As in many other NLP tasks, SOTA performance in semantic parsing is now attaine…
Exploring Transfer Learning For End-to-End Spoken Language Understanding
Subendhu Rongali, Beiye Liu, Liwei Cai +3
Voice Assistants such as Alexa, Siri, and Google Assistant typically use a two-stage Spoken Language Understanding pipeline; first, an Automatic Speech Recognition (ASR) component…
Delexicalized Paraphrase Generation
Boya Yu, Konstantine Arkoudas, Wael Hamza
We present a neural model for paraphrasing and train it to generate delexicalized sentences. We achieve this by creating training data in which each input is paired with a number o…