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Found 20 papers, 8 papers with code
Guided K-best Selection for Semantic Parsing Annotation
Collecting data for conversational semantic parsing is a time-consuming and demanding process.
ToolTalk: Evaluating Tool-Usage in a Conversational Setting
Large language models (LLMs) have displayed massive improvements in reasoning and decision-making skills and can hold natural conversations with users.
Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation
Our results demonstrate that our algorithm can achieve competitive performance with strong privacy levels.
Privacy-Preserving Domain Adaptation of Semantic Parsers
Task-oriented dialogue systems often assist users with personal or confidential matters.
BenchCLAMP: A Benchmark for Evaluating Language Models on Syntactic and Semantic Parsing
We introduce BenchCLAMP, a Benchmark to evaluate Constrained LAnguage Model Parsing, that includes context-free grammars for seven semantic parsing datasets and two syntactic parsing datasets with varied output representations, as well as a constrained decoding interface to generate only valid outputs covered by these grammars.
Addressing Resource and Privacy Constraints in Semantic Parsing Through Data Augmentation
We introduce a novel setup for low-resource task-oriented semantic parsing which incorporates several constraints that may arise in real-world scenarios: (1) lack of similar datasets/models from a related domain, (2) inability to sample useful logical forms directly from a grammar, and (3) privacy requirements for unlabeled natural utterances.
Few-Shot Semantic Parsing with Language Models Trained On Code
Intuitively, such models can more easily output canonical utterances as they are closer to the natural language used for pre-training.
Pruning Pretrained Encoders with a Multitask Objective
The sizes of pretrained language models make them challenging and expensive to use when there are multiple desired downstream tasks.
Constrained Language Models Yield Few-Shot Semantic Parsers
We explore the use of large pretrained language models as few-shot semantic parsers.
Hierarchical Variational Imitation Learning of Control Programs
Autonomous agents can learn by imitating teacher demonstrations of the intended behavior.
Synthetic Datasets for Neural Program Synthesis
The goal of program synthesis is to automatically generate programs in a particular language from corresponding specifications, e. g. input-output behavior.
RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers
The generalization challenge lies in (a) encoding the database relations in an accessible way for the semantic parser, and (b) modeling alignment between database columns and their mentions in a given query.
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Encoding Database Schemas with Relation-Aware Self-Attention for Text-to-SQL Parsers
When translating natural language questions into SQL queries to answer questions from a database, we would like our methods to generalize to domains and database schemas outside of the training set.
Program Synthesis and Semantic Parsing with Learned Code Idioms
Program synthesis of general-purpose source code from natural language specifications is challenging due to the need to reason about high-level patterns in the target program and low-level implementation details at the same time.
Improving Neural Program Synthesis with Inferred Execution Traces
The task of program synthesis, or automatically generating programs that are consistent with a provided specification, remains a challenging task in artificial intelligence.
Parametrized Hierarchical Procedures for Neural Programming
Neural programs are highly accurate and structured policies that perform algorithmic tasks by controlling the behavior of a computation mechanism.
Learning what to learn in a neural program
Recent work has shown that it is possible to address these issues by using recursion in the Neural Programmer-Interpreter, but this technique requires a verification set which is difficult to construct without knowledge of the internals of the oracle used to generate training data.
JPEG-resistant Adversarial Images
Several papers have explored the use of JPEG compression as a defense against adversarial images.
Making Neural Programming Architectures Generalize via Recursion
Empirically, neural networks that attempt to learn programs from data have exhibited poor generalizability.
Latent Attention For If-Then Program Synthesis
Automatic translation from natural language descriptions into programs is a longstanding challenging problem.
