51 citations · 121 across the 12 of their papers we have counts for
16 papers · 1 filter
Recipes for Sequential Pre-training of Multilingual Encoder and Seq2Seq Models
Saleh Soltan, Andy Rosenbaum, Tobias Falke +3
Pre-trained encoder-only and sequence-to-sequence (seq2seq) models each have advantages, however training both model types from scratch is computationally expensive. We explore rec…
CLASP: Few-Shot Cross-Lingual Data Augmentation for Semantic Parsing
Andy Rosenbaum, Saleh Soltan, Wael Hamza +3
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, lab…
LINGUIST: Language Model Instruction Tuning to Generate Annotated Utterances for Intent Classification and Slot Tagging
Andy Rosenbaum, Saleh Soltan, Wael Hamza +2
We present LINGUIST, a method for generating annotated data for Intent Classification and Slot Tagging (IC+ST), via fine-tuning AlexaTM 5B, a 5-billion-parameter multilingual seque…
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…
Instilling Type Knowledge in Language Models via Multi-Task QA
Shuyang Li, Mukund Sridhar, Chandana Satya Prakash +3
Understanding human language often necessitates understanding entities and their place in a taxonomy of knowledge -- their types. Previous methods to learn entity types rely on tra…
Zero-shot Generalization in Dialog State Tracking through Generative Question Answering
Shuyang Li, Jin Cao, Mukund Sridhar +4
Dialog State Tracking (DST), an integral part of modern dialog systems, aims to track user preferences and constraints (slots) in task-oriented dialogs. In real-world settings with…