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Found 13 papers, 3 papers with code
GPT-4 Can't Reason
GPT-4 was released in March 2023 to wide acclaim, marking a very substantial improvement across the board over GPT-3. 5 (OpenAI's previously best model, which had powered the initial release of ChatGPT).
Low-Resource Compositional Semantic Parsing with Concept Pretraining
In this work, we present an architecture to perform such domain adaptation automatically, with only a small amount of metadata about the new domain and without any new training data (zero-shot) or with very few examples (few-shot).
PIZZA: A new benchmark for complex end-to-end task-oriented parsing
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 representations such as the lambda calculus, which are expressive but costly to annotate.
Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems
We present results from a large-scale experiment on pretraining encoders with non-embedding parameter counts ranging from 700M to 9. 3B, their subsequent distillation into smaller models ranging from 17M-170M parameters, and their application to the Natural Language Understanding (NLU) component of a virtual assistant system.
Cross-Lingual Natural Language Inference
intent-classification
+5
Cross-TOP: Zero-Shot Cross-Schema Task-Oriented Parsing
However, a single model is still typically trained and deployed for each task separately, requiring labeled training data for each, which makes it challenging to support new tasks, even within a single business vertical (e. g., food-ordering or travel booking).
Compositional Task-Oriented Parsing as Abstractive Question Answering
Task-oriented parsing (TOP) aims to convert natural language into machine-readable representations of specific tasks, such as setting an alarm.
Training Naturalized Semantic Parsers with Very Little Data
Semantic parsing is an important NLP problem, particularly for voice assistants such as Alexa and Google Assistant.
Unfreeze with Care: Space-Efficient Fine-Tuning of Semantic Parsing Models
We examine two such promising techniques, prefix tuning and bias-term tuning, specifically on semantic parsing.
Combining Weakly Supervised ML Techniques for Low-Resource NLU
Accordingly, unsupervised learning and SSL (semi-supervised learning) techniques continue to be of vital importance.
Exploring Transfer Learning For End-to-End Spoken Language Understanding
Since our model can process both speech and text input sequences and learn to predict a target sequence, it also allows us to do zero-shot E2E SLU by training on only text-hypothesis data (without any speech) from a new domain.
Ranked #3 on
Spoken Language Understanding
on Snips-SmartLights
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Delexicalized Paraphrase Generation
We present a neural model for paraphrasing and train it to generate delexicalized sentences.
Grammatical Sequence Prediction for Real-Time Neural Semantic Parsing
While sequence-to-sequence (seq2seq) models achieve state-of-the-art performance in many natural language processing tasks, they can be too slow for real-time applications.
Semantically Driven Auto-completion
The Bloomberg Terminal has been a leading source of financial data and analytics for over 30 years.
