Search Results for author:
Found 38 papers, 18 papers with code
Neural Enquirer: Learning to Query Tables with Natural Language
Neural Enquirer can be trained with gradient descent, with which not only the parameters of the controlling components and semantic parsing component, but also the embeddings of the tables and query words can be learned from scratch.
DyNet: The Dynamic Neural Network Toolkit
In the static declaration strategy that is used in toolkits like Theano, CNTK, and TensorFlow, the user first defines a computation graph (a symbolic representation of the computation), and then examples are fed into an engine that executes this computation and computes its derivatives.
A Syntactic Neural Model for General-Purpose Code Generation
We consider the problem of parsing natural language descriptions into source code written in a general-purpose programming language like Python.
Softmax Q-Distribution Estimation for Structured Prediction: A Theoretical Interpretation for RAML
Reward augmented maximum likelihood (RAML), a simple and effective learning framework to directly optimize towards the reward function in structured prediction tasks, has led to a number of impressive empirical successes.
Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow
For tasks like code synthesis from natural language, code retrieval, and code summarization, data-driven models have shown great promise.
StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing
Semantic parsing is the task of transducing natural language (NL) utterances into formal meaning representations (MRs), commonly represented as tree structures.
A Tree-based Decoder for Neural Machine Translation
Recent advances in Neural Machine Translation (NMT) show that adding syntactic information to NMT systems can improve the quality of their translations.
Retrieval-Based Neural Code Generation
In models to generate program source code from natural language, representing this code in a tree structure has been a common approach.
TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation
We present TRANX, a transition-based neural semantic parser that maps natural language (NL) utterances into formal meaning representations (MRs).
Ranked #2 on
Semantic Parsing
on ATIS
Learning to Represent Edits
We introduce the problem of learning distributed representations of edits.
Improving Open Information Extraction via Iterative Rank-Aware Learning
We found that the extraction likelihood, a confidence measure used by current supervised open IE systems, is not well calibrated when comparing the quality of assertions extracted from different sentences.
Reranking for Neural Semantic Parsing
Semantic parsing considers the task of transducing natural language (NL) utterances into machine executable meaning representations (MRs).
Ranked #4 on
Code Generation
on Django
Merging Weak and Active Supervision for Semantic Parsing
Experiments on WikiTableQuestions with human annotators show that our method can improve the performance with only 100 active queries, especially for weakly-supervised parsers learnt from a cold start.
Incorporating External Knowledge through Pre-training for Natural Language to Code Generation
Open-domain code generation aims to generate code in a general-purpose programming language (such as Python) from natural language (NL) intents.
Ranked #3 on
Code Generation
on CoNaLa-Ext
TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data
Recent years have witnessed the burgeoning of pretrained language models (LMs) for text-based natural language (NL) understanding tasks.
Ranked #10 on
Text-To-SQL
on spider
(Exact Match Accuracy (Dev) metric)
Learning Structural Edits via Incremental Tree Transformations
To show the unique benefits of modeling tree edits directly, we further propose a novel edit encoder for learning to represent edits, as well as an imitation learning method that allows the editor to be more robust.
Compositional Generalization for Neural Semantic Parsing via Span-level Supervised Attention
We describe a span-level supervised attention loss that improves compositional generalization in semantic parsers.
Procedures as Programs: Hierarchical Control of Situated Agents through Natural Language
When humans conceive how to perform a particular task, they do so hierarchically: splitting higher-level tasks into smaller sub-tasks.
Learning to Superoptimize Real-world Programs
We also report that SILO's rate of superoptimization on our test set is over five times that of a standard policy gradient approach and a model pre-trained on compiler optimization demonstration.
On The Ingredients of an Effective Zero-shot Semantic Parser
Semantic parsers map natural language utterances into meaning representations (e. g., programs).
UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models
Structured knowledge grounding (SKG) leverages structured knowledge to complete user requests, such as semantic parsing over databases and question answering over knowledge bases.
Ranked #1 on
Task-Oriented Dialogue Systems
on KVRET
Show Me More Details: Discovering Hierarchies of Procedures from Semi-structured Web Data
To this end, we develop a simple and efficient method that links steps (e. g., "purchase a camera") in an article to other articles with similar goals (e. g., "how to choose a camera"), recursively constructing the KB.
PaLM: Scaling Language Modeling with Pathways
To further our understanding of the impact of scale on few-shot learning, we trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model PaLM.
Ranked #1 on
Coreference Resolution
on Winograd Schema Challenge
Compositional Generalization and Decomposition in Neural Program Synthesis
We first characterize several different axes along which program synthesis methods would be desired to generalize, e. g., length generalization, or the ability to combine known subroutines in new ways that do not occur in the training data.
SCARA: Scalable Graph Neural Networks with Feature-Oriented Optimization
Recent advances in data processing have stimulated the demand for learning graphs of very large scales.
Natural Language to Code Generation in Interactive Data Science Notebooks
To measure the performance of AI pair programmers that automatically synthesize programs for those tasks given natural language (NL) intents from users, we build ARCADE, a benchmark of 1082 code generation problems using the pandas data analysis framework in data science notebooks.
PaLM 2 Technical Report
Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more efficient inference compared to PaLM.
Ranked #1 on
Question Answering
on StrategyQA
SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (extended)
Text-to-SQL, the process of translating natural language into Structured Query Language (SQL), represents a transformative application of large language models (LLMs), potentially revolutionizing how humans interact with data.
ExeDec: Execution Decomposition for Compositional Generalization in Neural Program Synthesis
When writing programs, people have the ability to tackle a new complex task by decomposing it into smaller and more familiar subtasks.
L2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language Models
Recently, large language models (LLMs), especially those that are pretrained on code, have demonstrated strong capabilities in generating programs from natural language inputs in a few-shot or even zero-shot manner.
SQLPrompt: In-Context Text-to-SQL with Minimal Labeled Data
Text-to-SQL aims to automate the process of generating SQL queries on a database from natural language text.
Universal Self-Consistency for Large Language Model Generation
Self-consistency with chain-of-thought prompting (CoT) has demonstrated remarkable performance gains on various challenging tasks, by utilizing multiple reasoning paths sampled from large language models (LLMs).
Gemini: A Family of Highly Capable Multimodal Models
This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding.
Ranked #1 on
Multi-task Language Understanding
on MMLU
(using extra training data)
Grounding Data Science Code Generation with Input-Output Specifications
Specifically, we propose GIFT4Code, a novel approach for the instruction fine-tuning of LLMs with respect to I/O specifications.
Unsupervised Evaluation of Code LLMs with Round-Trip Correctness
To evaluate code large language models (LLMs), research has relied on a few small manually curated benchmarks, such as HumanEval and MBPP, which represent a narrow part of the real-world software domains.
A Survey of Neural Code Intelligence: Paradigms, Advances and Beyond
Building on our examination of the developmental trajectories, we further investigate the emerging synergies between code intelligence and broader machine intelligence, uncovering new cross-domain opportunities and illustrating the substantial influence of code intelligence across various domains.
NExT: Teaching Large Language Models to Reason about Code Execution
A fundamental skill among human developers is the ability to understand and reason about program execution.
