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Found 9 papers, 2 papers with code
Unsupervised Parsing with S-DIORA: Single Tree Encoding for Deep Inside-Outside Recursive Autoencoders
The deep inside-outside recursive autoencoder (DIORA; Drozdov et al. 2019) is a self-supervised neural model that learns to induce syntactic tree structures for input sentences *without access to labeled training data*.
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).
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.
Improved Latent Tree Induction with Distant Supervision via Span Constraints
For over thirty years, researchers have developed and analyzed methods for latent tree induction as an approach for unsupervised syntactic parsing.
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
Compressing Transformer-Based Semantic Parsing Models using Compositional Code Embeddings
The current state-of-the-art task-oriented semantic parsing models use BERT or RoBERTa as pretrained encoders; these models have huge memory footprints.
Continual Domain-Tuning for Pretrained Language Models
Pre-trained language models (LM) such as BERT, DistilBERT, and RoBERTa can be tuned for different domains (domain-tuning) by continuing the pre-training phase on a new target domain corpus.
Don't Parse, Generate! A Sequence to Sequence Architecture for Task-Oriented Semantic Parsing
Virtual assistants such as Amazon Alexa, Apple Siri, and Google Assistant often rely on a semantic parsing component to understand which action(s) to execute for an utterance spoken by its users.
Taxonomy grounded aggregation of classifiers with different label sets
We describe the problem of aggregating the label predictions of diverse classifiers using a class taxonomy.
