Search Results for author:
Found 15 papers, 4 papers with code
Self-Training using Rules of Grammar for Few-Shot NLU
We propose to use rules of grammar in self-training as a more reliable pseudo-labeling mechanism, especially when there are few labeled data.
MultiFix: Learning to Repair Multiple Errors by Optimal Alignment Learning
We consider the problem of learning to repair erroneous C programs by learning optimal alignments with correct programs.
Boosting Code Summarization by Embedding Code Structures
We find that a program dependency graph (PDG) can represent the structure of a code more effectively.
Generalizable Implicit Hate Speech Detection Using Contrastive Learning
However, it is challenging to identify implicit hate speech in nuance or context when there are insufficient lexical cues.
A Framework for Quantum Finite-State Languages with Density Mapping
We present a framework that provides a simple and intuitive way to build QFAs and maximize the simulation accuracy.
ATHENA: Mathematical Reasoning with Thought Expansion
Solving math word problems depends on how to articulate the problems, the lens through which models view human linguistic expressions.
Ranked #1 on
Math Word Problem Solving
on ASDiv-A
Neuro-Symbolic Regex Synthesis Framework via Neural Example Splitting
We tackle the problem of learning regexes faster from positive and negative strings by relying on a novel approach called `neural example splitting'.
LST: Lexicon-Guided Self-Training for Few-Shot Text Classification
Self-training provides an effective means of using an extremely small amount of labeled data to create pseudo-labels for unlabeled data.
ALP: Data Augmentation using Lexicalized PCFGs for Few-Shot Text Classification
Taken together, our contributions on the data augmentation strategies yield a strong training recipe for few-shot text classification tasks.
SplitRegex: Faster Regex Synthesis via Neural Example Splitting
SplitRegex is a divided-and-conquer framework for learning target regexes; split (=divide) positive strings and infer partial regexes for multiple parts, which is much more accurate than the whole string inferring, and concatenate (=conquer) inferred regexes while satisfying negative strings.
SoftRegex: Generating Regex from Natural Language Descriptions using Softened Regex Equivalence
We continue the study of generating se-mantically correct regular expressions from natural language descriptions (NL).
Detecting context abusiveness using hierarchical deep learning
Our model outperforms all the previous models for detecting abusiveness in a text data without abusive words.
Online Infix Probability Computation for Probabilistic Finite Automata
Probabilistic finite automata (PFAs) are com- mon statistical language model in natural lan- guage and speech processing.
OPERA: Reasoning about continuous common knowledge in asynchronous distributed systems
Each event block is signed by the hashes of the creating node and its $k$ peers.
Distributed, Parallel, and Cluster Computing
Incremental Computation of Infix Probabilities for Probabilistic Finite Automata
The problem of computing infix probabilities of strings when the pattern distribution is given by a probabilistic context-free grammar or by a probabilistic finite automaton is already solved, yet it was open to compute the infix probabilities in an incremental manner.
